[go: up one dir, main page]

WO2025166417A1 - Système de surveillance médicale et vêtement à capteurs physiologiques pouvant être porté sur soi - Google Patents

Système de surveillance médicale et vêtement à capteurs physiologiques pouvant être porté sur soi

Info

Publication number
WO2025166417A1
WO2025166417A1 PCT/AU2025/050089 AU2025050089W WO2025166417A1 WO 2025166417 A1 WO2025166417 A1 WO 2025166417A1 AU 2025050089 W AU2025050089 W AU 2025050089W WO 2025166417 A1 WO2025166417 A1 WO 2025166417A1
Authority
WO
WIPO (PCT)
Prior art keywords
sensor
physiological
garment
data
respiratory
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/AU2025/050089
Other languages
English (en)
Inventor
Nicholas Moore
Nipuna Fernando
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2024900312A external-priority patent/AU2024900312A0/en
Application filed by Individual filed Critical Individual
Publication of WO2025166417A1 publication Critical patent/WO2025166417A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02438—Measuring pulse rate or heart rate with portable devices, e.g. worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/0245—Measuring pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/083—Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A61B5/1117—Fall detection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/251—Means for maintaining electrode contact with the body
    • A61B5/256—Wearable electrodes, e.g. having straps or bands
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
    • A61B5/282—Holders for multiple electrodes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4866—Evaluating metabolism
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6804—Garments; Clothes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0204—Operational features of power management
    • A61B2560/0214—Operational features of power management of power generation or supply
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0008—Temperature signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/083—Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
    • A61B5/0836—Measuring rate of CO2 production
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F7/00—Heating or cooling appliances for medical or therapeutic treatment of the human body
    • A61F7/007—Heating or cooling appliances for medical or therapeutic treatment of the human body characterised by electric heating
    • A61F2007/0077—Details of power supply
    • A61F2007/0078—Details of power supply with a battery
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/0404—Electrodes for external use
    • A61N1/0408—Use-related aspects
    • A61N1/046—Specially adapted for shock therapy, e.g. defibrillation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/0404—Electrodes for external use
    • A61N1/0472—Structure-related aspects
    • A61N1/0484—Garment electrodes worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39—Heart defibrillators
    • A61N1/3904—External heart defibrillators [EHD]

Definitions

  • the system may include a respiratory effort sensor, an electrocardiogram sensor, a transcutaneous carbon dioxide sensor, and a pulse oximeter for generating a work of breathing index.
  • This index may quantify respiratory muscle effort by analysing thoracic expansion, respiratory-modulated ECG signals, and arterial oxygen saturation levels.
  • the work of breathing index may be used to detect early respiratory decompensation and assist in ventilatory therapy adjustments.
  • the system may include an accelerometer-gyroscope module, a blood pressure sensor, a heart rate variability sensor, and a transcutaneous carbon dioxide sensor for generating a falls index. This index may assess movement stability, postural changes, and circulatory dynamics to predict fall risk. Upon detecting a fall event, the system may automatically trigger an alert for remote clinical assessment and, in some cases, initiate emergency intervention if prolonged immobility and absent respiratory effort are detected.
  • the system may continuously refine physiological indices using patientspecific baseline trends, ensuring personalised monitoring and adaptive clinical response.
  • the garment may be further configured with additional features, such as a defibrillator module for automated cardiac intervention, integrated heating elements for thermal regulation, and modular sensor attachment points for expanded physiological monitoring capabilities.
  • the medical monitoring system provides a scalable and adaptable solution for continuous patient monitoring in diverse healthcare environments, including hospitals, rehabilitation facilities, aged care homes, and remote patient monitoring settings.
  • a medical monitoring system comprising a wearable garment incorporating a plurality of physiological sensors configured to obtain real-time patient data, a data processing unit operatively connected to the plurality of sensors to receive and process sensor data, a wireless communication module configured to transmit processed sensor data to a remote server or base station, and a multi-modal physiological signal processing unit configured to integrate and analyse the processed sensor data to generate composite physiological indices that assess patient status and predict clinical deterioration.
  • the sensors may include at least a transcutaneous carbon dioxide sensor, a respiratory effort sensor, a body temperature sensor, and a heart rate variability sensor, wherein the composite physiological indices include a metabolic rate index derived from data obtained from the transcutaneous carbon dioxide sensor, respiratory effort sensor, body temperature sensor, and heart rate variability sensor. More preferably, the metabolic rate index is calculated using a machine learning model trained on historical physiological data to correlate fluctuations in carbon dioxide levels, respiratory rate, and temperature with metabolic demand. In some embodiments, the metabolic rate index is adjusted based on detected activity levels from an accelerometer-gyroscope module to differentiate between metabolic demand due to exertion and physiological stress. The metabolic rate index may further be used to predict metabolic derangements associated with sepsis, endocrine disorders, or critical illness, triggering alerts for early clinical intervention.
  • the sensors may include at least a respiratory effort sensor, a transcutaneous carbon dioxide sensor, an electrocardiogram sensor, and a pulse oximeter, wherein the composite physiological indices include a work of breathing index derived from data obtained from the respiratory effort sensor, transcutaneous carbon dioxide sensor, electrocardiogram-derived respiratory excursion data, and arterial oxygen saturation data measured by the pulse oximeter. More preferably, the work of breathing index is generated by analysing variations in thoracic expansion detected by the electrocardiogram sensor, correlating the amplitude and frequency of respiratory cycles with respiratory muscle effort. The work of breathing index may be adjusted based on transcutaneous carbon dioxide diffusion levels, allowing differentiation between increased ventilatory effort due to respiratory insufficiency and compensatory metabolic mechanisms.
  • the multi-modal physiological signal processing unit applies real-time trend analysis to detect progressive increases in the work of breathing index, providing early warning of respiratory decompensation before hypoxia or acidosis occurs.
  • the work of breathing index may incorporate motion data from an accelerometer-gyroscope module to distinguish between voluntary exertional breathing patterns and pathophysiological respiratory distress.
  • the sensors may include at least an accelerometer-gyroscope module, a blood pressure sensor, a heart rate variability sensor, and a transcutaneous carbon dioxide sensor, wherein the composite physiological indices include a falls index derived from data obtained from the accelerometer-gyroscope module, blood pressure sensor, heart rate variability sensor, and transcutaneous carbon dioxide sensor. More preferably, the falls index is generated by analysing sudden changes in acceleration, angular velocity, and orientation detected by the accelerometer-gyroscope module to identify stumbles, near falls, and actual falls. The falls index may be adjusted based on real-time blood pressure fluctuations, enabling differentiation between falls caused by orthostatic hypotension, cardiac arrhythmias, or neurological impairment.
  • the falls index incorporates heart rate variability and transcutaneous carbon dioxide levels to assess autonomic dysregulation and metabolic distress as contributing factors to falls.
  • a detected fall event may automatically trigger an alert transmitted via the wireless communication module to a remote monitoring station, including real-time physiological data at the time of the fall for clinical assessment.
  • the system may automatically activate defibrillator pads embedded in the garment to deliver an emergency shock if a shockable cardiac rhythm is detected.
  • the medical monitoring system may further include a non-invasive continuous blood pressure monitoring system using an inflatable cuff integrated into the garment, wherein the cuff inflates automatically at scheduled intervals based on detected circulatory trends.
  • the system includes embedded thermal pads within the garment, wherein temperature adjustments are determined based on real-time body temperature readings.
  • the system may further include an integrated emergency alert system, wherein patients can manually or automatically trigger an alert if significant physiological abnormalities are detected.
  • Figure 1 illustrates a conceptual design of a wearable medical monitoring garment with integrated physiological sensors.
  • Figure 2 shows the garment’s medical interface components, including SpO 2 , invasive blood pressure, and transcutaneous CO 2 monitoring ports.
  • Figure 3 depicts an invasive blood pressure monitoring apparatus incorporating a pressure transducer, anti-reflux valve, and blood withdrawal port.
  • Figure 4 illustrates the architecture of the base station, detailing components such as a touchscreen display, processor, storage, and wireless communication interfaces.
  • Figure 5 provides a detailed view of the integrated blood pressure cuff system, including the air compressor, battery unit, and attachment features.
  • Figure 6 shows the back view of the garment, highlighting integrated heating elements, defibrillation pads, and emergency skin access features.
  • Figure 7 presents a simplified line drawing of the garment with a pocket for housing electronics, including the ESP32 microcontroller and battery.
  • Figure 8 details the placement of SpO 2 probes, blood pressure transducers, and integrated wiring stitched into the garment.
  • Figure 9 illustrates the thermal pad assembly, including thermal wires, a dissipation medium, and insulating layers for controlled heat distribution.
  • Figure 10 presents a schematic representation of the base station, which serves as the primary data processing and communication hub.
  • Figure 1 1 illustrates the data processing and communication flow, showing the interaction between the garment, base station, clinician display, and central data store.
  • Figure 12 provides a schematic representation of the garment’s sensor system, showing the ESP32 microcontroller, integrated ECG, thermal monitoring, and power management.
  • Figure 13 illustrates the ambulant garment variant, which incorporates motion tracking, fall detection, and location monitoring sensors.
  • Figure 14 depicts the critical care garment variant, designed for high-acuity monitoring with additional hemodynamic and neurological assessment capabilities.
  • FIG. 1 illustrates a conceptual and functional design of a medical monitoring garment.
  • the garment 100 is equipped with a variety of integrated medical monitoring technologies, enabling continuous physiological assessment while maintaining patient comfort and mobility.
  • the garment 100 is available in various sizes and colours, designed to enhance the patient experience through the use of soft, sleepfriendly fabric that provides warmth and modesty.
  • the garment 100 may include options for long and short sleeves, with some designs incorporating emergency access features such as perforated frontages or side openings 103.
  • a version of the garment 100 may be configured with an open back and tearaway sleeves to facilitate rapid medical access.
  • the left arm of the garment 100 may include a battery unit and compressor module 102, which are detachable and operatively connected to a sleeve-integrated blood pressure cuff 101 positioned on the same arm.
  • the right arm of the garment 100 may be similarly equipped with a sleeve-integrated blood pressure cuff 101 , allowing for bilateral blood pressure monitoring.
  • the detachable compressor 102 can be selectively connected as needed, providing a modular configuration that supports adaptable monitoring.
  • FIG. 5 provides a detailed view of the integrated blood pressure (BP) cuff system 101 in an embodiment, specifically designed to be embedded within the garment 100.
  • the main components depicted include an air compressor module 140, which is combined with a rechargeable battery unit 141 and connected via tubing 142 to the BP cuff balloon. This configuration allows the cuff balloon to be inflated, temporarily restricting blood flow in a standard blood pressure measurement procedure.
  • sensors are integrated for transcutaneous carbon dioxide monitoring 212 and central oxygen saturation monitoring 214. These sensors allow for continuous physiological assessment without requiring invasive procedures.
  • the garment 100 may also supports electrocardiogram (ECG) monitoring in both 5-lead and 12-lead configurations.
  • ECG electrocardiogram
  • the embedded ECG wiring 105 enables comprehensive cardiac monitoring while eliminating the need for adhesive ECG electrodes.
  • the garment 100 may incorporates an anterior defibrillator pad 106 and a posterior defibrillator pad 107, both of which are seamlessly integrated into the fabric. These defibrillator pads may remain concealed under a fabric patch for patient comfort but are accessible in emergencies to facilitate rapid external defibrillation.
  • Emergency access perforations or a zip 103 may be included in the garment 100, allowing for quick access to the patient’s skin. This feature is particularly useful for emergency interventions, such as attaching additional monitoring equipment or administering urgent medical treatments.
  • the garment 100 may comprise an electronics pocket 160, which houses the processing unit of the monitoring system. This pocket 160 may be positioned to provide easy access to the embedded electronics while ensuring they remain secure and unobtrusive to the wearer.
  • the diagram depicts an invasive blood pressure monitoring connector port 1 1 1 , enabling the integration of high-precision blood pressure measurement systems.
  • Invasive blood pressure monitoring in this context is able to provide continuous, high-fidelity arterial pressure measurements that are more accurate and responsive than non-invasive methods.
  • invasive blood pressure monitoring ensures real-time hemodynamic assessment with greater precision.
  • the system enables uninterrupted monitoring in critical conditions, allowing clinicians to detect rapid blood pressure fluctuations, which is essential for managing critically ill patients, particularly those with unstable hemodynamics.
  • Another port 1 12 is provided for additional monitoring capabilities, such as electromyography (EMG) and transcutaneous carbon dioxide (CO 2 ) monitoring.
  • EMG electromyography
  • CO 2 transcutaneous carbon dioxide
  • FIG. 4 presents a flowchart outlining the connectivity and functional components of the base station in an embodiment, which maintains wireless communication with the centralised wired monitoring connector hub 108 of the garment 100.
  • the base station may comprise a touchscreen display 131 , serving as the primary user interface for medical personnel to view real-time patient data.
  • At the core of the base station may be a Raspberry Pi 4b CPU 130, a single-board computer known for its versatility in medical and loT applications.
  • the base station may be powered by a rechargeable battery 141 , which provides up to four hours of continuous operation, ensuring portability and uninterrupted monitoring in situations where external power sources are unavailable.
  • the base station may be equipped with local storage 132 with a capacity of 1 Terabyte (TB). This storage system serves dual purposes: providing a backup repository in case of connection failure and ensuring redundancy in the event of temporary network disruptions.
  • the base station may comprise a secondary Wi-Fi connection 133, which allows seamless integration with the hospital’s internal network for high-speed data transmission. Additionally, for scenarios where hospital Wi-Fi is unavailable or unreliable, the system may include external communication capabilities through an LTE/Satellite module 134. This retrieval model ensures that patient data can be transmitted securely and continuously, even in remote or emergency settings.
  • the base station may communicate with a centralised data store 135, which can be hosted either on-premises or as a cloud-based solution. This flexibility allows institutions to scale their data storage according to operational needs.
  • the centralised data store 135 is designed with restricted access protocols to safeguard patient privacy and enhance data security.
  • the system may operate using an open-standard database and storage format, facilitating interoperability with other hospital information systems and medical software.
  • the fabric of the garment 100 may incorporate embedded wiring 109 for oxygen saturation monitoring, designed to accommodate a standard SpO 2 finger probe 1 10 attached at either sleeve.
  • an alternative chest probe option may be available. This chest probe may be connected to the garment’s internal wiring 109 or wirelessly linked to the central data collection point via a small internal battery 216, which is designed to be rechargeable or replaceable, ensuring long-term usability and reliability.
  • the garment 100 may incorporate warming technology to maintain patient temperature stability.
  • This warming system may be implemented using chemical-based warming materials, similar to existing warming blankets, which are integrated into the fabric, or electrically powered heating elements embedded within the seams of the garment.
  • the warming elements may be positioned in strategic locations, such as the armpits and torso, to optimise heat distribution and ensure effective temperature control.
  • Figure 9 illustrates a detailed view of a thermal pad 180, which may be incorporated into the garment 100 to regulate the patient’s body temperature.
  • the primary heating components consist of thermal wires 180 arranged in parallel across a large surface area to ensure uniform heat distribution and prevent the formation of localised hotspots. These wires are embedded within the fabric structure, allowing for flexible and discreet integration without compromising the comfort of the garment.
  • a cross-sectional diagram provides an in-depth view of the thermal pad's layered composition.
  • the topmost layer consists of a thermal dissipation medium 182, which evenly spreads the heat generated by the wires 180, ensuring consistent warmth delivery across the entire pad. This prevents concentrated heat build-up, which could otherwise cause patient discomfort or thermal injury.
  • the thermal dissipation medium 182 Beneath the thermal dissipation medium 182 is the front or patient-facing surface 183 of the pad, which comes into direct contact with the patient’s body. This surface is designed to transfer heat efficiently while maintaining skin compatibility.
  • the back or bed-facing layer 184 may incorporate an insulating material to prevent heat loss and direct the warmth upwards towards the patient. This insulation is critical for maintaining energy efficiency and optimising the thermal effectiveness of the system.
  • a battery unit 181 serving as the power source for the thermal wires 180.
  • This battery 181 may be designed to provide a portable energy supply, enabling the heating elements to function independently of an external power source.
  • the battery 181 may be rechargeable or replaceable, ensuring continuous operation during extended use.
  • Figure 6 presents a schematic representation of the back view of the garment 100, highlighting its integrated medical technology.
  • Vertical stripes represent the heating element channels 150, which are designed to provide warmth to the wearer. These channels 150 may be positioned along the back and shoulders to maintain core body temperature, particularly in perioperative or critical care scenarios.
  • Figure 6 depicts an apical defibrillation pad 151 , represented by a rectangular shape connected to the main body of the garment 100.
  • This pad 151 may be positioned near the apex of the heart, ensuring rapid accessibility in case of a cardiac emergency.
  • the defibrillation pad 151 may be integrated within the garment fabric and remains concealed under a fabric covering, which can be quickly removed in the event of a cardiac arrest, allowing for immediate defibrillation intervention.
  • the garment 100 may also include features for emergency access to the skin, such as perforations or a zipper 153, shown at the bottom right edge of Figure 6.
  • This emergency access design enables medical personnel to quickly expose the patient’s skin without requiring complete removal of the garment, which is crucial in urgent situations such as defibrillation, catheter insertion, or central line placement.
  • Figure 6 depicts a medically equipped garment 100 that incorporates heating and defibrillation capabilities according to an embodiment, designed to provide both therapeutic warmth and rapid emergency response features for patient care.
  • the integration of these elements ensures that the garment is suitable for use in intensive care units, emergency rooms, and perioperative settings where both continuous monitoring and life-saving interventions are necessary.
  • FIG. 7 illustrates a simplified line drawing of the medical garment 100, highlighting a pocket 160, likely positioned on the front lower section of the garment.
  • This pocket 160 is intended to house the garment's electronics, including essential components such as an ESP32 microcontroller and a rechargeable battery unit 181 . By enclosing these electronic components within a dedicated compartment, the garment ensures that the system remains both secure and unobtrusive for the wearer.
  • the sides of the garment 100 shown in Figure 7 may comprise a Velcro fastening system 161 . These Velcro fasteners 161 may extend from the underarm down to the bottom hem, enabling quick and easy removal of the garment. This fastening system may be particularly beneficial in medical environments where rapid patient access is required, such as during emergency resuscitation or surgical procedures.
  • Figure 8 further illustrates a simplified schematic of the garment 100, detailing the placement of various monitoring components in an embodiment.
  • Figure 8 shows an oxygen saturation (SpO 2 ) probe 170 and pressure transducers 171 as part of an airline monitoring system.
  • SpO 2 oxygen saturation
  • These elements may be designed to be pluggable into a central hub, depicted as a square on the garment sleeve, which serves as the connection point between the embedded sensors and the garment’s internal electronic system.
  • Figure 8 illustrates an integrated blood pressure cuff 172 with associated tubing and wiring. These tubes and wires, stitched into the fabric 173, may ensure that the blood pressure monitoring components remain securely attached to the garment while maintaining flexibility and comfort for the patient. The integration of these monitoring components within the garment eliminates the need for bulky external devices, enhancing the wearability and usability of the system.
  • FIG. 8 The wiring and tubing shown in Figure 8 are shown running across the garment 100 to connect various medical sensors and devices. This design enables continuous patient monitoring without requiring separate, externally mounted components. The seamless integration of these medical technologies into the fabric structure enhances patient comfort and mobility while maintaining the high level of physiological monitoring needed for critical and ambulatory care settings.
  • the medical garment 100 preferably supports invasive blood pressure monitoring through a spring-based pressure chamber 120 attached to a radial artery catheter.
  • This system offers a more stable and accurate alternative to traditional pressure bag-based monitoring, which can be prone to leaks and pressure inconsistencies.
  • the spring-based mechanism 120 applies consistent force to maintain reliable arterial pressure readings, ensuring precise hemodynamic monitoring.
  • the invasive blood pressure monitoring system depicted in Figure 3 may include a reservoir for saline flushing 122 and an anti-reflux ball valve 125 to prevent air injection into the patient’s circulatory system. This design minimises the risk of complications such as embolism or inaccurate readings caused by air bubbles.
  • the blood pressure monitoring system may be configured to be either wired or wirelessly connected to the central monitoring hub 108, allowing for real-time integration with the garment’s physiological data processing system.
  • the invasive blood pressure system may include an adjustable calibration mechanism 121 , enabling medical personnel to fine-tune the monitoring system for each patient. Additionally, the system supports easy relocation for arterial sampling, with features such as a blood withdrawal port 129 and a three- way tap, allowing for convenient access to arterial blood samples without disrupting continuous monitoring.
  • FIG. 3 illustrates an invasive blood pressure apparatus 120 designed for continuous arterial blood pressure monitoring in a healthcare setting in an embodiment.
  • the apparatus includes a pressure chamber 120 containing a spring mechanism that applies a mechanical force to maintain and measure pressure. This spring-based approach ensures a stable and accurate reading, reducing reliance on traditional pressure bags, which may be prone to leaks or fluctuations.
  • the apparatus may also incorporate an adjustable calibration mechanism 121 , allowing for precise recalibration of the spring to maintain measurement accuracy.
  • a fluid holding membrane 122 may be included, which serves as a reservoir for the calibration fluid, ensuring consistent pressure application during monitoring.
  • the system may comprise a visual pressure indicator 123, displaying a green or red status to indicate whether the pressure and electrical transduction are operating within the correct range.
  • a pressure transducer module 124 may be integrated into the apparatus, converting physical pressure readings into an electrical signal for real-time monitoring.
  • an anti-reflux ball valve 125 is incorporated, preventing any backward flow of fluid, which could lead to inaccuracies or contamination.
  • the apparatus may further include a fluid refilling port 126, allowing for routine maintenance and replenishment of calibration fluid.
  • a flow limit valve 127 may be set to restrict fluid movement to a predetermined threshold, in this case, 2 millilitres per hour. Additionally, a manual flush button 128 is provided, enabling the purging of air bubbles or blockages to maintain the accuracy of pressure readings. Small-volume tubing connects the apparatus to the patient’s arterial line, ensuring a stable and precise transduction of pressure data.
  • a further feature of the invasive blood pressure monitoring system may be a blood withdrawal port 129, equipped with a three-way tap. This design allows clinicians to obtain arterial blood samples without disrupting continuous pressure monitoring, thereby improving efficiency and patient safety.
  • the garment may integrate a transcutaneous CO 2 monitoring module 212, which enables continuous assessment of metabolic rate and ventilation effectiveness. This feature is particularly beneficial for non-intubated patients, allowing for real-time respiratory monitoring without invasive procedures.
  • the CO 2 sensor 212 may be positioned under the garment and is connected to the monitoring hub via internal wiring, minimising external cabling and enhancing patient comfort.
  • the garment 100 may include a gyroscope and accelerometer 220, as shown in Figure 13, to facilitate motion tracking and location monitoring. This capability encourages patient mobility while ensuring safety through automatic fall detection. If an unexpected fall is detected, the system may trigger an alert via the wireless communication module 192, enabling rapid response from caregivers or emergency services. Additionally, the garment supports direct communication functions, allowing patients to signal distress or request assistance remotely.
  • Another preferable monitoring feature of the garment 100 is the assessment of work of breathing, which is measured using a remote respiratory effort sensor integrated into a nasal prong oxygen delivery system.
  • This sensor provides detailed insights into the patient’s respiratory status, allowing clinicians to adjust treatment in real-time.
  • the nasal prong system wirelessly transmits data to the garment’s multimodal physiological signal processing unit 202, as depicted in Figure 1 1 , ensuring continuous analysis of respiratory patterns and ventilatory effort.
  • the garment 100 may incorporates several distinctive monitoring capabilities beyond blood pressure and respiratory function. It may utilise a spring-based mechanism 120 for arterial pressure monitoring, reducing reliance on traditional fluidbased systems. Additionally, the garment may comprise integrated transcutaneous CO 2 monitoring 212, offering continuous metabolic rate assessment without requiring arterial blood gas sampling.
  • the garment 100 may include an inbuilt warming system, as depicted in Figure 9.
  • This system comprises thermal wires 180 embedded in the garment fabric, which distribute heat evenly across the patient’s body.
  • a battery-powered heating element 181 ensures continuous operation, while an insulating layer 184 prevents heat dissipation away from the patient.
  • This warming capability may be particularly beneficial for perioperative and critical care settings, where maintaining optimal body temperature is essential for patient outcomes.
  • the garment 100 may be rapidly opened via a tearaway or zippered front 153, as shown in Figure 6, allowing for immediate access to the patient’s chest.
  • This feature is designed to facilitate urgent medical interventions, such as defibrillation, catheter insertion, or chest compressions.
  • the garment 100 may include motion detection capabilities 220, with built-in alarms that alert caregivers in the event of a fall.
  • the falls detection system is integrated with the garment’s accelerometer-gyroscope module 220 and heart rate variability sensor 21 1 , allowing the system to assess fall risk based on movement patterns and physiological data.
  • Respiratory function may be continuously monitored using a nasal prong sensor within the work of breathing module 212.
  • This system enables remote monitoring of breathing effort, respiratory rate, and ventilatory patterns.
  • the garment’s clinician interface 191 as shown in Figure 10, can provide automated recommendations for treatment adjustments.
  • the garment 100 may support remote monitoring and therapeutic interventions, including automated defibrillation.
  • the defibrillator module 215, integrated into the garment as shown in Figure 12, may enable immediate electrical therapy in the event of sudden cardiac arrest. If a life-threatening arrhythmia is detected, the defibrillator pads 151 and 152 ( Figure 6) may automatically deliver a shock, significantly reducing the time to intervention and improving patient survival rates.
  • FIG. 10 illustrates a schematic representation of the base station 130 in accordance with an embodiment, which serves as the primary data processing and communication hub for the medical monitoring garment 100.
  • the base station 130 may comprise a single-board system-on-chip (SOC) 190, such as a Raspberry Pi or Odroid, which functions as the main processing unit for receiving, analysing, and transmitting data from the medical monitoring garment 100.
  • SOC system-on-chip
  • the SOC 190 is operatively connected to a touchscreen display 191 that provides a user interface for medical personnel to monitor patient data in real time.
  • the base station 130 may further comprise an Al inference co-processor 190 that enhances data processing capabilities by applying machine learning algorithms to sensor data received from the garment.
  • This Al inference co-processor 190 facilitates advanced physiological index calculations, such as metabolic demand, work of breathing, central and peripheral perfusion indices, and fall prediction indices, thereby augmenting the clinical utility of the system.
  • a wireless communication module 192 may enable the base station 130 to receive data from the garment’s embedded sensors via a Wi-Fi uplink.
  • the garment’s monitoring unit transmits real-time patient monitoring data wirelessly, ensuring continuous connectivity without the need for physical tethering.
  • the base station may also equipped with local data storage 132, which serves as a buffer for storing patient data in the event of a connection failure or for offline operation, thereby ensuring data integrity and continuity.
  • the base station may include a secondary Wi-Fi interface 133 configured for in-hospital communication. Additionally, for applications outside a hospital environment, the base station may incorporate an LTE, satellite, or LoRa interface 193, enabling data retrieval and remote monitoring in field or ambulatory settings.
  • the base station may communicate with a centralised data store 135, which provides long-term storage and access to patient data for clinical review.
  • Figure 1 1 illustrates the data processing and communication flow within the base station in an embodiment.
  • Data received from the garment sensor system 200 is processed by the base station processor 201 , which is operatively linked to the Al co-processor 190 for additional physiological index calculations.
  • the multi-modal physiological signal processing unit 202 analyses the received data to generate composite physiological indices, such as metabolic rate, work of breathing, and/or fall risk assessments.
  • the data may be stored in the central data store 135 for long-term retrieval, enabling historical trend analysis and facilitating clinical documentation. This capability allows clinicians to review past physiological data to track patient progress, assess long-term trends, and refine treatment strategies based on predictive modelling generated by the system's Al-driven analysis 203.
  • the base station 130 By integrating wireless data transmission, Al-based predictive analytics, and multi-modal physiological monitoring, the base station 130 enhances the efficiency of patient monitoring and provides proactive alerts for early intervention in clinical deterioration scenarios.
  • the system architecture is preferably designed to enhance patient monitoring by integrating real-time data acquisition, wireless transmission, and advanced computational analysis, while ensuring seamless accessibility to medical personnel.
  • the combination of local data processing, remote access capabilities, and Al-driven analysis enables comprehensive and continuous patient monitoring across diverse healthcare environments.
  • FIG. 12 illustrates an integrated garment sensor schematic in accordance with an embodiment, detailing the functional components of the medical monitoring garment 100 and their interaction with the base station 130.
  • the garment includes an embedded ESP32 microcontroller 210, which serves as the central processing unit for managing various sensor inputs, communication protocols, and power management.
  • the garment incorporates an ECG module 21 1 connected via fabric-integrated wiring 109 to capture and transmit real-time electrocardiogram signals. Additionally, the ESP32 microcontroller 210 may interface with thermal monitoring components 213 responsible for both patient and garment temperature regulation. These components include a thermal pad driver, which controls heating elements embedded in the garment to provide warming therapy when necessary.
  • the garment may further include internal Bluetooth connections that link to a transcutaneous CO 2 monitoring module 212, a work of breathing module, and a central Sp0 2 /perfusion module 214. These modules may contribute to comprehensive physiological monitoring, enabling real-time assessment of respiratory effort, metabolic function, and oxygenation levels.
  • the schematic also depicts an arm connector hub, which may facilitate the connection of external monitoring and therapeutic devices.
  • This may include a defibrillator module 215 and a shoulder-mounted blood pressure (BP) controller box, which is installable on either arm or both arms as needed.
  • the BP controller box may integrate an air pump and pressure transducer for non-invasive arterial pressure monitoring, with power supplied via a swappable, click-in battery.
  • the system may supports external wired modules, including an SpO 2 module 214 and an arterial pressure transducer. These modules extend the functionality of the garment, allowing for high-fidelity continuous monitoring of blood oxygen levels and invasive arterial pressure when required.
  • All data collected by the garment’s sensors and modules may be transmitted via a Wi-Fi uplink 192 to the base station 130, ensuring real-time monitoring, remote access, and data logging.
  • the integration of multiple physiological monitoring components within the garment enables comprehensive patient monitoring without restricting movement, reducing reliance on traditional wired medical equipment, and improving patient comfort and mobility.
  • the garment may further comprise an optional blood pressure monitoring system, including a shoulder-mounted BP controller box 222 and a blood pressure cuff 172.
  • the BP controller box which houses an air pump and pressure transducer, may be installable on either arm or both arms as required. It is powered by a swappable, click-in battery to facilitate ease of use in ambulant scenarios.
  • External wired modules such as an optional SpO 2 module 214, may be connected via an arm connector hub, expanding the monitoring capabilities based on clinical needs.
  • the streamlined design of the ambulant garment prioritises patient comfort and mobility, making it particularly suitable for outpatient monitoring, rehabilitation settings, and home healthcare applications.
  • FIG. 14 illustrates a critical care garment schematic in accordance with an embodiment, designed for intensive medical monitoring and intervention in high- acuity environments such as intensive care units (ICUs), emergency departments, and perioperative settings.
  • the critical care garment incorporates the same ESP32 microcontroller 210 architecture as the ambulant garment but supports a broader range of integrated and external monitoring systems.
  • the garment may include advanced physiological monitoring features facilitated through internal Bluetooth connections to various medical modules, including a transcutaneous CO 2 module 212, a nasal prong work of breathing module, and a central Sp0 2 /perfusion module 214. Additional connectivity is provided for a pulmonary artery catheter (PA catheter) module 230 and continuous EEG monitoring electrodes 231 , offering comprehensive data collection for critically ill patients.
  • PA catheter pulmonary artery catheter
  • the critical care garment may include integrated ECG monitoring 21 1 , thermal monitoring 213 for both the patient and garment, and a Wi-Fi uplink 192 for data transmission to the base station 130.
  • the critical care garment expands its functionality with additional connection hubs, including both an arm connector hub and a leg connector hub 234, facilitating a more extensive network of monitoring and therapeutic devices.
  • Medical monitoring is provided by the wearable garment 100 integrating a plurality of physiological sensors for real-time acquisition of patient data.
  • the garment 100 is designed to provide continuous physiological monitoring without impeding patient mobility, addressing limitations associated with traditional wired monitoring systems.
  • the garment 100 structure incorporates embedded wiring 109, sensor attachment points 104, and modular connectivity features to facilitate seamless integration of physiological sensing technologies.
  • FIGS 13 and 14 illustrate variations of the garment designed for ambulatory and critical care applications, respectively.
  • the ambulatory version prioritises mobility while maintaining essential monitoring capabilities, incorporating sensors such as an accelerometer-gyroscope module 220 for motion tracking and fall detection.
  • the critical care version extends the monitoring capabilities by integrating additional invasive and non-invasive physiological sensors, such as a pulmonary artery catheter (PA catheter) module 230 and an arterial pressure transducer 232, allowing for high- fidelity continuous monitoring in intensive care environments.
  • PA catheter pulmonary artery catheter
  • the system may be further designed to ensure data integrity and long-term accessibility through integration with a centralised data store 135, as depicted in Figure 1 1.
  • This data store serves as a repository for historical physiological data, allowing for trend analysis and retrospective assessment of patient health metrics.
  • the system's open-standard database format ensures interoperability with existing hospital information systems, facilitating seamless integration into clinical workflows.
  • the medical monitoring system provides an efficient solution for continuous patient monitoring across diverse healthcare environments.
  • the system's ability to generate composite physiological indices enables clinicians to make informed decisions, improving patient outcomes through proactive management of physiological changes.
  • the medical monitoring system may include a transcutaneous carbon dioxide sensor 212, a respiratory effort sensor, a body temperature sensor, and a heart rate variability sensor for the purpose of generating a metabolic rate index.
  • the transcutaneous carbon dioxide sensor 212 may be integrated into the garment 100 in direct contact with the skin, allowing for continuous monitoring of CO 2 diffusion through the epidermis.
  • the respiratory effort sensor may be positioned at the thoracic or abdominal region to capture variations in breathing patterns, while the body temperature sensor may be located at a stable skin contact area to ensure accurate thermal readings.
  • the heart rate variability sensor which may be implemented using electrocardiogram (ECG) electrodes 21 1 or pulse oximetry-based photoplethysmography (PPG) 214, provides continuous data on autonomic nervous system regulation, which contributes to metabolic assessment.
  • ECG electrocardiogram
  • PPG pulse oximetry-based photoplethysmography
  • the metabolic rate index may be computed using a machine learning model trained on historical physiological datasets.
  • This model may establish dynamic correlations between fluctuations in CO 2 levels, respiratory rate, and temperature, mapping these variations to changes in metabolic demand.
  • the system may employ regression-based models, neural networks, or adaptive learning algorithms to refine metabolic predictions over time.
  • the machine learning model may be implemented within the Al inference co-processor 190 of the base station 130, as shown in Figure 10, where it processes real-time sensor data to generate predictive metabolic assessments.
  • the transcutaneous carbon dioxide sensor 212 may provide continuous CO 2 diffusion rate measurements, which may be weighted against respiratory effort and arterial oxygen saturation levels to compute a metabolic efficiency score. This score may be indicative of oxygen utilisation and energy expenditure, offering insights into metabolic efficiency under various physiological conditions.
  • Figure 12 illustrates the integration of the transcutaneous CO 2 sensor 212 within the garment’s monitoring system, enabling non-invasive continuous assessment without requiring blood gas sampling.
  • the metabolic rate index may be adjusted based on detected activity levels obtained from the accelerometer-gyroscope module 220 embedded within the garment 100.
  • the accelerometer-gyroscope module 220 may differentiate between resting and exertional states by analysing movement intensity, orientation changes, and gait patterns.
  • the metabolic assessment may be dynamically refined to distinguish between metabolic demand due to physical exertion and that associated with physiological stress or pathological conditions.
  • Figure 13 illustrates the ambulant version of the garment, which may include motion tracking to support adaptive metabolic analysis for patients engaged in rehabilitation or fitness training.
  • the multi-modal physiological signal processing unit 202 may continuously update the metabolic rate index based on patient-specific physiological baselines.
  • the system may establish a personalised metabolic profile for each patient by analysing historical trends and adapting to individual physiological variations over time.
  • the processing unit may incorporate adaptive algorithms that recalibrate baseline metabolic parameters in response to environmental factors, disease progression, or recovery phases. This approach allows for precise, patientspecific metabolic monitoring that accounts for longitudinal changes in physiological status.
  • the metabolic rate index may be transmitted to a clinician interface 191 for remote monitoring.
  • the clinician interface as depicted in Figure 1 1 , may display real-time metabolic trend analysis, allowing healthcare providers to track deviations from baseline metabolic function. Alerts may be generated if the metabolic rate index exhibits significant fluctuations indicative of clinical deterioration.
  • the system may integrate predictive modelling 203 to assess the likelihood of impending metabolic crises, enabling proactive therapeutic adjustments before severe complications arise.
  • the work of breathing index may be generated by analysing variations in thoracic expansion as detected by the electrocardiogram sensor 21 1.
  • the system may utilise respiratory-modulated ECG signals, identifying amplitude and frequency changes that correlate with respiratory muscle effort. By extracting these respiratory cycle parameters, the system may quantify the degree of inspiratory and expiratory strain, thereby offering an objective measure of work of breathing. This analysis may be conducted using signal processing techniques that differentiate between normal and laboured breathing patterns, as implemented in the multi-modal physiological signal processing unit 202.
  • the multi-modal physiological signal processing unit 202 may apply real-time trend analysis to detect progressive increases in the work of breathing index, providing early warning of respiratory decompensation before hypoxia or acidosis occurs.
  • the system may compare sequential respiratory effort values, identifying trends that indicate worsening respiratory distress. This capability may be particularly useful in patients at risk of acute respiratory failure, such as those with chronic obstructive pulmonary disease (COPD) or acute pulmonary infections.
  • COPD chronic obstructive pulmonary disease
  • the system may generate proactive alerts that allow clinicians to intervene before critical hypoxic or acidotic events develop.
  • the work of breathing index may incorporate motion data from the accelerometer-gyroscope module 220 to distinguish between voluntary exertional breathing patterns and pathophysiological respiratory distress.
  • the accelerometer-gyroscope module 220 may track patient movement and positional changes, enabling the system to differentiate between increased respiratory effort due to physical exertion (e.g., exercise) and respiratory distress unrelated to movement. This feature is particularly relevant in ambulatory patients, where normal exertion should not be mistaken for clinical deterioration.
  • the work of breathing index may be used to guide remote ventilatory support interventions by correlating respiratory effort with blood gas trends, facilitating real-time titration of oxygen therapy or non-invasive ventilation settings.
  • the system may integrate arterial oxygen saturation data from the pulse oximeter 214 with transcutaneous CO 2 data 212 and respiratory effort levels to optimise ventilatory support parameters. This feature may be particularly useful in home-based ventilation monitoring for patients with neuromuscular disorders or chronic respiratory diseases, allowing remote clinicians to adjust settings based on objective, real-time data.
  • the work of breathing index may be continuously refined using patient-specific physiological baselines, allowing for personalised respiratory monitoring and adaptation to chronic respiratory conditions.
  • the system may establish an individualised baseline for each patient by analysing long-term respiratory trends and adapting threshold values accordingly. This approach ensures that alerts and interventions are tailored to the patient’s typical breathing patterns rather than generic population-based norms.
  • the work of breathing index may be transmitted to a clinician interface 191 with predictive modelling 203 to anticipate impending respiratory failure and recommend therapeutic interventions before clinical deterioration occurs.
  • the clinician interface 191 may provide real-time visualisation of respiratory effort trends, along with predictive analytics that assess the likelihood of impending respiratory failure. If the system detects a worsening trend in work of breathing combined with declining oxygen saturation and rising CO 2 levels, it may recommend specific therapeutic actions, such as increasing ventilatory support or administering bronchodilators.
  • the medical monitoring system may include an accelerometer-gyroscope module 220, a blood pressure sensor 232, a heart rate variability sensor 21 1 , and a transcutaneous carbon dioxide sensor 212 to generate a falls index.
  • the accelerometer-gyroscope module 220 may be embedded within the garment 100 to continuously monitor patient movement, orientation, and stability.
  • the blood pressure sensor 232 may be integrated within an inflatable cuff positioned at the arm or wrist, as shown in Figures 5 and 8, providing real-time blood pressure fluctuations that may contribute to fall risk assessment.
  • the heart rate variability sensor 21 1 which may be implemented as part of the electrocardiogram module 21 1 or pulse oximeter 214, may assess autonomic nervous system responses related to postural adjustments.
  • the transcutaneous carbon dioxide sensor 212 as shown in Figure 12, may provide additional data on respiratory function, offering insights into physiological distress states that may precede falls.
  • the falls index may be generated by analysing sudden changes in acceleration, angular velocity, and orientation detected by the accelerometergyroscope module 220.
  • the system may identify stumbles, near falls, and actual falls by detecting abrupt deviations in movement patterns, such as an unexpected downward acceleration followed by a rapid deceleration or impact.
  • the gyroscopic component may determine whether the patient has undergone a rotational displacement indicative of imbalance or a fall-related postural change. These movement data points may be processed using threshold-based and machine learning algorithms within the multi-modal physiological signal processing unit 202 to distinguish true falls from normal movement fluctuations.
  • the ambulant versio n of the garment 220 as illustrated in Figure 13, may incorporate such motion-tracking features to enhance safety in mobile patients.
  • the falls index may be adjusted based on real-time blood pressure fluctuations, enabling differentiation between falls caused by orthostatic hypotension, cardiac arrhythmias, or neurological impairment.
  • a sudden drop in blood pressure detected by the blood pressure sensor 232 may indicate a fall caused by postural hypotension, whereas an absence of significant blood pressure changes may suggest other underlying causes such as balance disturbances or musculoskeletal issues.
  • the critical care version of the garment 230 as shown in Figure 14, may incorporate continuous blood pressure monitoring for high-risk patients, allowing the system to assess whether a detected fall event is related to circulatory instability.
  • the multi-modal physiological signal processing unit 202 may apply movement pattern recognition algorithms to distinguish between normal gait, instability, and uncontrolled descent, reducing false fall alarms.
  • the system may analyse gait velocity, step cadence, and movement fluidity, using data from the accelerometergyroscope module 220 to differentiate between steady ambulation, minor instability events, and actual falls. Pattern recognition techniques may enable the system to filter out false positives caused by sudden but controlled movements, such as sitting down rapidly or bending forward.
  • the falls index may incorporate heart rate variability and transcutaneous carbon dioxide levels to assess autonomic dysregulation and metabolic distress as contributing factors to falls.
  • a fall event accompanied by a sudden spike or drop in heart rate variability may suggest autonomic dysfunction, which may be associated with cardiovascular or neurological instability.
  • transcutaneous carbon dioxide levels 212 may indicate metabolic distress, hypoxia, or respiratory compromise that could contribute to fall risk.
  • a detected fall event may automatically trigger an alert transmitted via the wireless communication module 192 to a remote monitoring station.
  • This alert may include real-time physiological data from the moment of the fall, such as movement trajectory, blood pressure readings, heart rate variability, and transcutaneous carbon dioxide levels.
  • the wireless transmission of fall-related data may allow clinicians to assess the severity of the event remotely and determine whether immediate intervention is necessary.
  • a detected fall event in the absence of subsequent movement or respiratory effort, may trigger automatic activation of the defibrillator pads 106, 107 embedded in the garment 100. If the system detects a fall followed by prolonged immobility, absent respiratory effort, and cardiac electrical activity indicative of a shockable rhythm, the defibrillator module 215, as illustrated in Figures 6 and 14, may be activated to deliver an emergency shock. This feature may enable rapid defibrillation in cases of sudden cardiac arrest, improving survival rates by reducing time to intervention.
  • the falls index may be continuously refined based on patient-specific movement patterns and historical fall data, allowing for personalised fall risk assessment and proactive intervention.
  • the system may track long-term trends in movement stability, adapting fall detection thresholds to align with each patient’s typical motion characteristics. By analysing past fall events and their associated physiological markers, the system may generate predictive assessments of future fall risk, allowing for early preventive measures such as physical therapy adjustments or medication review.
  • Trend analysis of the falls index may enable early identification of progressive mobility decline, allowing clinicians to adjust treatment plans or implement fall prevention strategies before critical incidents occur.
  • a gradual increase in fall risk parameters, even in the absence of an actual fall, may indicate worsening balance, muscular deterioration, or circulatory dysfunction.
  • the system may integrate these findings into a clinical dashboard, providing healthcare providers with insights to guide interventions such as strength training programs, gait assistance devices, or medication modifications aimed at reducing fall risk.
  • the medical monitoring system is configured to facilitate proactive therapeutic interventions by identifying early deviations in metabolic rate, work of breathing index, and/or falls index, allowing clinicians to implement corrective actions before critical deterioration occurs.
  • the system may provide early-warning alerts and enable remote therapeutic adjustments based on real-time and trend-based assessments.
  • a specific example of proactive intervention enabled by the metabolic rate index involves the early detection of sepsis in a hospital or home healthcare setting.
  • the system may detect a progressive increase in metabolic rate index derived from transcutaneous carbon dioxide diffusion, respiratory effort, heart rate variability, and core body temperature.
  • a sustained elevation in metabolic rate index particularly in the absence of significant exertion detected by the accelerometer-gyroscope module 220, may indicate an early hypermetabolic state consistent with developing sepsis.
  • the system may trigger an early warning alert, notifying clinicians before traditional sepsis markers, such as hypotension or altered mental status, become apparent.
  • Remote access to real-time physiological trends via the clinician interface, as depicted in Figure 1 1 may allow a physician to initiate fluid resuscitation, adjust antibiotic therapy, or escalate monitoring before severe organ dysfunction occurs.
  • the predictive modelling within the multi-modal physiological signal processing unit 202 may recommend a threshold-adjusted response, such as increasing intravenous fluid administration if heart rate variability becomes erratic or if systolic blood pressure begins to decline.
  • the system may facilitate timely intervention, reducing mortality risk.
  • Another example may involve early metabolic decline in patients with endocrine disorders, such as adrenal insufficiency or thyroid dysfunction.
  • the system may track fluctuations in metabolic rate index over weeks, identifying a gradual decline in metabolic activity that correlates with increased fatigue, weight changes, and altered heart rate variability. By comparing the patient’s long-term metabolic profile against baseline trends, the system may notify an endocrinologist of subclinical adrenal suppression or hypothyroid decompensation, allowing for preemptive hormone titration before acute metabolic crisis occurs.
  • the work of breathing index may provide actionable insights into early respiratory failure, allowing for real-time titration of respiratory support before critical hypoxia or hypercapnia occurs.
  • a specific example involves a patient with chronic obstructive pulmonary disease (COPD) using home-based non-invasive ventilation (NIV).
  • COPD chronic obstructive pulmonary disease
  • NMV home-based non-invasive ventilation
  • the system may continuously monitor transcutaneous carbon dioxide levels 212, respiratory effort via thoracic expansion analysis, and arterial oxygen saturation via pulse oximetry 214. If the work of breathing index begins to trend above baseline for several hours — even if oxygen saturation remains normal — the system may detect early respiratory muscle fatigue, which typically precedes overt ventilatory failure.
  • the remote clinician interface may display a progressive increase in thoracic expansion effort while respiratory rate remains unchanged, suggesting inefficient ventilation due to muscle exhaustion.
  • the system may then recommend an increase in positive airway pressure settings via an integrated telemedicine-linked ventilator or alert caregivers to provide rescue bronchodilator therapy. Without such early intervention, the patient may develop worsening carbon dioxide retention (hypercapnia) and hypoventilation-related acidosis, ultimately requiring emergency hospitalisation.
  • Another example involves asthma exacerbation in a paediatric patient, where the system detects a rising work of breathing index despite stable oxygen saturation. This pattern may indicate bronchospasm with increased inspiratory effort, which is an early indicator of respiratory deterioration before arterial hypoxia develops.
  • the system may notify caregivers via the wireless alert system, instructing them to administer a beta-agonist inhaler or seek early medical attention, thereby preventing severe airway obstruction requiring intensive care admission.
  • the falls index may provide predictive fall risk assessment, allowing for proactive interventions to prevent injury and hospitalisation.
  • a specific case may involve an elderly patient in a residential aged care facility with a history of orthostatic hypotension and previous falls.
  • the system may track blood pressure fluctuations 232, accelerometer-gyroscope gait patterns 220, and heart rate variability 21 1 , establishing a personalised fall risk profile. Over several weeks, if the system detects a progressive increase in postural instability combined with intermittent blood pressure drops upon standing, it may generate a pre-fall warning alert.
  • nursing staff may be prompted to review antihypertensive medications that could be contributing to postural hypotension. Additionally, physical therapists may be advised to introduce balance training or gait stabilisation therapy to mitigate fall risk before an actual fall occurs.
  • the clinician interface as depicted in Figure 1 1 , may provide trend-based fall risk analysis, highlighting progressive gait instability correlated with worsening autonomic regulation.
  • Another use case involves sudden cardiac-related falls, particularly in patients with undiagnosed arrhythmias.
  • the system may detect an abrupt drop in blood pressure and heart rate variability just before a fall event, suggesting a transient loss of consciousness due to a cardiac arrhythmia, such as atrial fibrillation with rapid ventricular response or bradyarrhythmia-induced syncope. If such an event is detected, the system may recommend Holter monitoring or cardiology referral before a catastrophic fall-related injury occurs.
  • the system may activate the defibrillator pads 106, 107 embedded in the garment 100, delivering an emergency shock if a shockable rhythm is detected.
  • This automated defibrillation capability may significantly reduce the time to intervention in cases of sudden cardiac arrest, improving survival rates.
  • the medical monitoring system may proactively guide therapy before severe deterioration occurs.
  • the system s ability to generate metabolic rate, work of breathing, and/or falls indices allows for advanced remote monitoring, early detection of clinical decline, and timely therapeutic intervention, reducing hospitalisations, improving patient outcomes, and optimising resource allocation in healthcare settings.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Public Health (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Veterinary Medicine (AREA)
  • Pathology (AREA)
  • Cardiology (AREA)
  • Physiology (AREA)
  • Pulmonology (AREA)
  • Signal Processing (AREA)
  • Obesity (AREA)
  • Optics & Photonics (AREA)
  • Dentistry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Emergency Medicine (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Psychiatry (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne un système de surveillance médicale comprenant un vêtement pouvant être porté sur soi incorporant de multiples capteurs physiologiques pour la surveillance d'un patient en temps réel. Le vêtement inclut une unité de traitement intégrée qui reçoit des données des capteurs et les transmet sans fil à une station de base ou à un serveur distant. Une unité multimodale de traitement de signaux physiologiques analyse les données pour générer des indices physiologiques composés, notamment le taux métabolique, le travail respiratoire et des évaluations de risque de chute. Le vêtement comporte des éléments de surveillance facultatifs tels que la mesure transcutanée du dioxyde de carbone, l'électrocardiographie, l'oxymétrie de pouls, la surveillance de la pression artérielle et le suivi de mouvements basé sur un accéléromètre-gyroscope. Le système fournit des alertes d'avertissement précoce de dégradation clinique, ce qui permet des interventions thérapeutiques proactives. Un module de communication sans fil assure une transmission des données en temps réel, prenant en charge la surveillance à distance et l'intégration avec des réseaux hospitaliers. La conception modulaire du vêtement permet une évaluation physiologique non invasive tout en maintenant la mobilité du patient, ce qui le rend approprié pour des applications de soins critiques, de surveillance ambulatoire et de télémédecine.
PCT/AU2025/050089 2024-02-09 2025-02-07 Système de surveillance médicale et vêtement à capteurs physiologiques pouvant être porté sur soi Pending WO2025166417A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AU2024900312 2024-02-09
AU2024900312A AU2024900312A0 (en) 2024-02-09 Medical monitoring garment

Publications (1)

Publication Number Publication Date
WO2025166417A1 true WO2025166417A1 (fr) 2025-08-14

Family

ID=96698746

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2025/050089 Pending WO2025166417A1 (fr) 2024-02-09 2025-02-07 Système de surveillance médicale et vêtement à capteurs physiologiques pouvant être porté sur soi

Country Status (1)

Country Link
WO (1) WO2025166417A1 (fr)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7122005B2 (en) * 1997-07-31 2006-10-17 Larry Shusterman Remote patient monitoring system with garment and automated medication dispenser
US20090024004A1 (en) * 2004-10-29 2009-01-22 Chang-Ming Yang Method and Apparatus for Monitoring Body Temperature, Respiration, Heart Sound, Swallowing, and Medical Inquiring
US20100056873A1 (en) * 2008-08-27 2010-03-04 Allen Paul G Health-related signaling via wearable items
US20130131524A1 (en) * 2011-11-17 2013-05-23 Jen-Ran Chen Blood pressure measurement system
US20160038083A1 (en) * 2014-08-08 2016-02-11 Orn, Inc. Garment including integrated sensor components and feedback components
US20170216613A1 (en) * 2016-01-28 2017-08-03 Zoll Medical Corporation Using A Wearable Medical Device With Multiple Patients
US20190374160A1 (en) * 2017-01-05 2019-12-12 The Trustees Of Princeton University Hierarchical health decision support system and method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7122005B2 (en) * 1997-07-31 2006-10-17 Larry Shusterman Remote patient monitoring system with garment and automated medication dispenser
US20090024004A1 (en) * 2004-10-29 2009-01-22 Chang-Ming Yang Method and Apparatus for Monitoring Body Temperature, Respiration, Heart Sound, Swallowing, and Medical Inquiring
US20100056873A1 (en) * 2008-08-27 2010-03-04 Allen Paul G Health-related signaling via wearable items
US20130131524A1 (en) * 2011-11-17 2013-05-23 Jen-Ran Chen Blood pressure measurement system
US20160038083A1 (en) * 2014-08-08 2016-02-11 Orn, Inc. Garment including integrated sensor components and feedback components
US20170216613A1 (en) * 2016-01-28 2017-08-03 Zoll Medical Corporation Using A Wearable Medical Device With Multiple Patients
US20190374160A1 (en) * 2017-01-05 2019-12-12 The Trustees Of Princeton University Hierarchical health decision support system and method

Similar Documents

Publication Publication Date Title
US11832918B2 (en) Wearable medical monitoring device
US12138058B2 (en) Wearable congestive heart failure management system
EP3307146B1 (fr) Surveillance de l'état de santé de personnes souffrant de maladies chroniques
JP2018149355A (ja) 監視のための装置および方法
US20150057512A1 (en) Wearable heart failure monitor patch
KR20180044942A (ko) 무선 환자 모니터링 시스템들 및 방법들
EP0969897A1 (fr) Systeme de retour de l'information concernant des signes physiologiques
US20200113472A1 (en) Apparatus and method for calculating a pulse deficit value
WO2020081029A2 (fr) Gilet d'urgence médicalisé
US20220211282A1 (en) Hemodynamic monitoring system and method and harness for same
WO2025166417A1 (fr) Système de surveillance médicale et vêtement à capteurs physiologiques pouvant être porté sur soi
US20160128639A1 (en) Graphical technique for detecting congestive heart failure
KR20050010885A (ko) 생리 기능 상태 모니터링 장치와 그 장치의 모니터링 및처리 방법
Varshney Wireless health monitoring: requirements and examples
EP4149355B1 (fr) Système de détection et d'acquisition de paramètres physiologiques et moteurs par l'intermédiaire de capteurs à porter sur soi
US20240090779A1 (en) Wearable medical monitoring device
Naples Brining A Monitoring System To Market
Kim et al. Health care with wellness wear
Paz Applications of Impedance Cardiography in Dynamic Environments

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 25750961

Country of ref document: EP

Kind code of ref document: A1