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WO2018053593A1 - Outil de détection de thrombose veineuse profonde (dvt) - Google Patents

Outil de détection de thrombose veineuse profonde (dvt) Download PDF

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Publication number
WO2018053593A1
WO2018053593A1 PCT/AU2017/051032 AU2017051032W WO2018053593A1 WO 2018053593 A1 WO2018053593 A1 WO 2018053593A1 AU 2017051032 W AU2017051032 W AU 2017051032W WO 2018053593 A1 WO2018053593 A1 WO 2018053593A1
Authority
WO
WIPO (PCT)
Prior art keywords
limb
detection device
data
property
change
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.)
Ceased
Application number
PCT/AU2017/051032
Other languages
English (en)
Inventor
Ranjan Thilagarajah
Alwyn Joseph DAVIES
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.)
Context Health Pty Ltd
Context Systems LLP
Original Assignee
Context Health Pty Ltd
Context Systems LLP
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 AU2016903846A external-priority patent/AU2016903846A0/en
Application filed by Context Health Pty Ltd, Context Systems LLP filed Critical Context Health Pty Ltd
Publication of WO2018053593A1 publication Critical patent/WO2018053593A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • 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
    • A61B5/1032—Determining colour of tissue for diagnostic purposes
    • 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
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531—Measuring skin impedance
    • 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
    • A61B5/6807—Footwear
    • 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
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6828—Leg
    • 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
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6829—Foot or ankle
    • 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
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6831—Straps, bands or harnesses
    • 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
    • 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
    • A61B2562/0261—Strain gauges
    • 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
    • A61B2562/04—Arrangements of multiple sensors of the same type
    • A61B2562/046—Arrangements of multiple sensors of the same type in a matrix array
    • 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
    • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance

Definitions

  • Described embodiments relate generally to systems and methods for detecting medical conditions such as deep vein thrombosis (DVT).
  • DVT deep vein thrombosis
  • described embodiments relate to systems and methods for detecting medical conditions such as DVT via a wearable detection device.
  • Deep vein thrombosis is a blood clot that forms within a deep vein within the body, usually in a deep leg vein within the calf or thigh.
  • VTE venous thromboembolism
  • PE pulmonary emboli
  • VTE risk factor including but not limited to: active cancer or cancer treatment, a known thrombophilia, obesity, the use of hormone replacement therapy, pregnancy, admission for hip or knee replacement and admission for surgery resulting in reduced mobility. Without preventative treatment, VTE occurs most commonly in patients with cancer and in those undergoing orthopaedic surgery.
  • Some embodiments relate to a detection device for the detection of a condition in a limb, the detection device comprising: a sensor material configured to be worn on a limb of a user, wherein at least one property of the sensor material changes in response to a change in at least one property of the limb; and
  • a processor electrically coupled to the sensor material and configured to detect the change of the at least one property of the sensor material.
  • the processor is further configured to determine whether the change of the at least one property of the sensor material corresponds to a symptom of the condition being present in the limb.
  • the processor is configured to communicate data corresponding to the change of the at least one property of the sensor material to a remote device, to allow the remote device to determine whether the change of the at least one property of the sensor material corresponds to a symptom of the condition being present in the limb.
  • the processor is configured to communicate data to the remote device via a direct physical wired connection. In some embodiments, the processor is configured to communicate data to the remote device via a wireless connection.
  • the detection device further comprises an antenna to allow the detection device to communicate with the remote device.
  • the remote device is configured to store the data in a database.
  • the at least one property of the sensor material that changes in response to a change in at least one property of the limb includes at least one of the colour, resistance or impedance of the sensor material.
  • the condition causes at least one of swelling, pain, a change in temperature or a change in colour of the limb.
  • the condition is deep vein thrombosis (DVT).
  • the at least one property of the limb includes at least one of the colour, temperature, size or conductivity of the limb.
  • the at least one property of the limb includes the colour of the limb
  • the sensor material comprises optoelectronics that cause the resistance or impedance of the sensor material to change when the colour of the limb changes.
  • the at least one property of the limb includes the temperature of the limb, and wherein the sensor material comprises thermo-chromic pigments in microcrystals that cause the colour of the sensor material to change when the temperature of the limb changes.
  • Some embodiments further comprise at least one zoning band electrically coupled to the sensor mesh separating the sensor mesh into at least two zones, wherein changes in the at least one property of the limb can be monitored separately in each zone.
  • Some embodiments further comprise an interconnecting band that allows for communication between at least two zoning bands.
  • Some embodiments further comprising a primary band, wherein data from each of the zoning bands is communicated to the primary band, and wherein the processor is situated on the primary band.
  • the sensor material comprises a mesh network of sensors.
  • Some embodiments further comprise a power supply for providing power to at least one of the processor and the sensor material.
  • the sensor material is sized to be worn on a leg of a user.
  • the senor material is sized to be worn between the knee and the ankle of the user. In some embodiments, the sensor material is a stretch material.
  • Some embodiments relate to a system for the detection of a condition in a limb, the system comprising:
  • a computing device configured to receive data from the detection device and to determine whether the change of the at least one property of the sensor material corresponds to a symptom of the condition being present in the limb.
  • the computing device is configured to compare the data received from the first detection device with a baseline data set to determine whether the change of the at least one property of the sensor material corresponds to a symptom of the condition being present in the limb.
  • Some embodiments further comprise a second detection device of some other embodiments.
  • the computing device is configured to compare the data received from the first detection device with data received from the second detection device to determine whether the change of the at least one property of the sensor material corresponds to a symptom of the condition being present in the limb.
  • the computing device is configured to determine the difference between the data received from the first detection device and the data received from the second device, and to determine whether the condition is present in the limb based on whether the determined difference exceeds a predetermined threshold.
  • the condition causes at least one of swelling, pain, a change in temperature or change in colour in the limb.
  • the condition is deep vein thrombosis (DVT).
  • Some embodiments relate to a method of detecting a condition in a limb, the method comprising: receiving data from a first wearable detection device, the data corresponding to at least one property of a limb on which the first detection device is worn;
  • the baseline data is retrieved from a database.
  • the data stored in the database is earlier data received from the first detection device.
  • the baseline data is received from a second wearable detection device, the baseline data corresponding to at least one property of a limb on which the second detection device is worn.
  • Some embodiments further comprise pairing the first detection device with the second detection device. Some embodiments further comprise determining the difference between the data received from the first detection device and the data received from the second device, and determining whether the condition is present in the limb based on whether the determined difference exceeds a predetermined threshold.
  • Some embodiments further comprise generating an alert if it is determined that the condition is present in the limb.
  • Some embodiments further comprise storing the received data in a database.
  • the received data from the first wearable device corresponds to a change in at least one property of the limb.
  • the at least one property of the limb includes at least one of the colour, temperature, size or conductivity of the limb.
  • the condition causes at least one of swelling, pain, a change in temperature or change in colour in the limb.
  • the condition is deep vein thrombosis (DVT).
  • the limb is a leg.
  • Figure 1 shows a perspective side view of a DVT detection device according to some embodiments
  • Figure 2 shows a rear view of the DVT detection device of Figure 1;
  • Figure 2a shows an example data path within the DVT detection device of Figure 1;
  • Figure 2b shows a rear view of an alternative embodiment of a DVT detection device
  • FIG 3 shows a detailed view of a primary band of the DVT detection device of Figure 1;
  • Figure 4 shows a microcontroller unit (MCU) and battery of the device of Figure 1 in more detail;
  • MCU microcontroller unit
  • Figure 5 shows a block diagram of an analogue to digital converter (ADC) and MCU connections of a first device according to some embodiments;
  • ADC analogue to digital converter
  • Figure 6 shows a block diagram of an ADC and MCU connections of a second device according to some embodiments
  • Figure 7 shows a series of signals generated during use of a system of DVT detection incorporating the device of Figure 1 ;
  • Figure 8 shows a block diagram of a system of DVT detection incorporating the device of Figure 1 ; and Figure 9 shows a flowchart process map of a method of DVT detection according to some embodiments.
  • Described embodiments relate generally to systems and methods for detecting medical conditions such as deep vein thrombosis (DVT).
  • DVT deep vein thrombosis
  • described embodiments relate to systems and methods for detecting medical conditions such as DVT via a wearable detection device.
  • the clinical symptoms and signs of DVT include the following:
  • Some embodiments relate to the generation and relay of sensor information from a deep vein thrombosis (DVT) detection device positioned on a user's leg or limb to a receiving device, for the purposes of clinical applications such as early detection of DVT and early intervention if DVT is suspected.
  • the detection device may be configured to detect one or more of the above-listed symptoms and signs of DVT.
  • the device may be configured to detect conditions outside of DVT that share one or more symptoms of DVT.
  • Some embodiments relate to wearable technology utilising intelligent materials to perform these functions and to transmit physiological data to remote or external devices that enable clinical decisions to be made based on the received data. Described embodiments utilise a mesh sensor built into the fabric of a stocking that is configured to detect specific changes within the person's leg consistent with signs suggestive of a disease process when compared with the normal health state for that person.
  • the stocking may be configured to be worn on the user's calf.
  • Some embodiments relate to having two associated stockings worn by a user, so that the difference in sensor readings from each of the stockings can be used to determine whether a person may be suffering from DVT or a similar condition.
  • Figure 1 shows a detection device 100 for the detection of a condition in a limb according to some embodiments.
  • the condition causes at least one of swelling, pain, a change in temperature or change in colour in the limb.
  • the condition is deep vein thrombosis (DVT).
  • Device 100 comprises a tubular stocking that is designed to be worn below the knee of the leg 110 of a user, and is sized to extend from the knee down to the ankle of leg 110.
  • device 100 may comprise an anti-embolic stocking
  • Device 100 can be worn as a calf stocking as shown in Figure 1, being positioned below the knee and extending to the ankle.
  • device 100 may be configured and sized to be worn on another limb or elsewhere on a user's body.
  • device 100 may be configured and sized to be worn as a full leg stocking, extending from the upper thigh to the ankle, or as a thigh stocking, extending from the upper thigh to the knee.
  • Device 100 may be sized differently depending on where it is to be worn.
  • Device 100 comprises a mesh fabric 101 made of a sensor material that is configured to act as a sensor for a number of parameters.
  • Mesh fabric 101 may be configured such that at least one property of mesh fabric 101 changes in response to a change in at least one property of the limb on which device 100 is worn.
  • Mesh fabric 101 may be configured to detect changes in at least one of the swelling, temperature, pain and colour of the leg on which it is worn, which can be indicative of an inflammatory process in that leg.
  • mesh fabric 101 may be configured to detect pain as a neurophysiological response, such as a change in skin conductivity, resistance or impedance of the limb.
  • Mesh fabric 101 may be configured to detect swelling or expansion of the limb as a tension or stretch within mesh fabric 101.
  • Mesh fabric 101 comprises constituents that are conductive and which are configured to act as direct sensors.
  • Mesh fabric 101 may comprise a mesh network of sensors.
  • Mesh fabric 101 may be made of the constituent sensor mesh material alone, or a combination of the sensor mesh material with a non-sensor fabric, such as Lycra® or another stretch fabric.
  • the sensor mesh material may be applied and bonded to the non- sensor material using techniques such as sputtering or inkjet printing application, so that the sensor material and non- sensor material become integrated to form one fabric, being mesh fabric 101.
  • Mesh fabric 101 may be configured to detect swelling of leg 110 on which it is worn. Swelling may be detected based on resistance and impedance changes within mesh fabric 101, where mesh fabric 101 may comprise a woven textile containing nano wires of copper or silver: as stretching occurs with swelling, the contact areas between the fibres is reduced leading to in increased electrical resistance between points of the textile.
  • Another embodiment of mesh fabric 101 may comprise carbon or graphene nanomaterials, which increase in electrical resistance on stretching, as a result of a reduction in conduction pathways across the material.
  • mesh fabric 101 may alternatively or additionally detect alterations in temperature of the underlying leg 110 as a property of the mesh material used.
  • the sensors for temperature changes may be similar to the tensile stretch sensors, namely silver or copper nano wires or flexible graphene thermistor material.
  • mesh material 101 may alternatively comprise polymer composites filled with nickel microparticles. As the temperature of leg 110 increases within the human physiological range, the polymer expands thereby increasing the distance between nickel microparticles resulting in an increase in electrical resistance. There are known issues in differentiating between impedance and resistance changes in the above tensile stretch and temperature change sensors;
  • mesh fabric 101 may alternatively comprise thermochromic pigments in microcrystals that change colour when the absolute temperature changes. This may allow a user to receive a visual alert that a symptom of DVT may be present in the limb, by observing the colour change in device 100.
  • mesh fabric 101 may alternatively or additionally detect pain felt in leg 110. Pain within leg 101 may be detected by mesh fabric 101 due to changes in electrical resistance and impedance of the skin of leg 110 as a result of neurophysiological changes at the skin surface.
  • Mesh fabric 101 may utilise silver or copper nano wires or flexible graphene thermistors within the fabric material to allow for the detection of pain in leg 110.
  • mesh fabric 101 may alternatively or additionally detect changes in colour of leg 110. Changes in colour of leg 110 can be detected using a mesh fabric 101 when mesh fabric 101 includes textile optoelectronics that comprise plastic optical fibres interwoven with polyester fibres having stretchable qualities. As described above, the sensor materials used in mesh fabric 101 can be chosen to detect changes in tensile stretch, temperature, colour and pain sensation in leg 110 based on electrical resistance in the fabric 101. For the purposes of clinical assessment, any or all of swelling, increased temperature, change in colour or pain in leg 110 in any combination can cause an alteration of electrical resistance and impedance in mesh fabric 101.
  • the absolute data relating to the change in resistance and/or impedance in mesh fabric 101 may be used to assess whether DVT is present in a limb. If the change exceeds a predetermined threshold value, it may be determined that DVT has occurred.
  • two DVT detection devices 100 may be worn by a user, one on each leg.
  • sensor data from one DVT detection device 100 can be compared with sensor data from one DVT detection device 100 to measure a difference in electrical signal delta, and determine whether DVT is likely. Comparing sensor readings between two legs reduces the risk of false alarms that may be caused by physical exercise or ambient temperature, which would cause an increase in temperature and swelling in both legs.
  • mesh fabric 101 may exhibit one or more of the tensile, photoelectric, electric and impedance qualities described above.
  • Mesh fabric 101 forms part of the tensile structure that enables device 101 to be worn without any additional physical support.
  • Mesh fabric 101 may comprise a material that is able to detect changes within the leg 110 with respect to heat, swelling, colour changes and pain sensation as a result of neurophysiological alterations at the skin surface.
  • mesh fabric 101 is stretchable, and may comprise a Lycra® type material to allow it to be easily fitted to a user's leg without requiring closures such as zippers or buttons, and to allow one size of device 100 to fit a number of differently sized users.
  • the sensor material of mesh fabric 101 may be intimately associated with any non-sensor material using one of the bonding techniques described above.
  • Mesh fabric 101 may be configured to act as a sensor for any one or more of the modalities stated above, whilst being flexible and stretchable to be used within the device 100.
  • Mesh fabric 101 may comprise one or more of plastic, spray-coated textile substrates, elastomeric substrates, printable ink-based solutes, poly(3,4- ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), tellurium compound nanorods within a glass fabric, carbon nanomaterial films associated with
  • PDMS polydimethylsiloxane
  • Mesh fabric 101 is connected to a number of zoning bands or secondary bands 102 on leg 110 that collect all sensor data from regions or zones in leg 110 between the knee and ankle.
  • the secondary bands may divide device 100 into a number of zones, which may be at least two zones in some embodiments. Changes in properties of the limb can be monitored separately in each zone.
  • the secondary bands 102 are connected to a primary band 103 situated at the top or bottom of the stocking device 100 via an interconnecting band 104 (shown in Figure 2). Interconnecting band 104 may be connected to primary band 103, which may be situated at the top or bottom of device 100.
  • the mesh design of mesh fabric 101 allows the passage of the multiple channels of sensory data to flow through mesh fabric 101 in a loop.
  • Each data signal originates at a point on a secondary band 102, travels through mesh fabric 101 and returns to a 'collection point' on the same secondary band 102, thereby, completing the loop around the circumference of leg 110 at the level of leg 110 at which the secondary band 102 is positioned.
  • Each secondary bandl02 may be configured to collect analogue data signals at the level of leg 110 at which the band is positioned. The analogue data signals from each secondary band 102 form multiple information data paths and are then transmitted via the interconnecting band 104 to the primary band 103.
  • Figure 2(a) shows an example of a number of analogue data paths from three zones of mesh fabric 101 to MCU 116, represented as dashed lines. Data signals flow from mesh fabric 101 to secondary bands 102, and then via interconnecting band 104 to the primary band 103, which hosts MCU 116.
  • Figure 2(b) shows an embodiment of device 100 that dispenses with secondary bands 102. Instead, the analogue data signals from mesh fabric 101 pass directly to interconnecting band 104 prior to being transmitted to primary band 103. This necessarily removes the collection of data at specific levels of leg 110, and instead a single stream of electrical resistance and impedance data is received at primary band 103.
  • FIG. 3 shows primary band 103 in further detail.
  • Primary band 103 may host a battery 105 or other power source or power supply for providing power to the device, and a microprocessor unit (MCU) chip 106 for processing the data signals received from mesh fabric 101.
  • MCU 116 may be a processor or other processing or computing device.
  • MCU 116 may be electrically coupled to the mesh fabric 101 and may be configured to detect the change of the at least one property of mesh fabric 101.
  • MCU 116 may further be configured to determine whether the change of the at least one property of mesh fabric 101 corresponds to a symptom of DVT or another conditions being present in the limb on which device 100 is worn.
  • Either primary band 103 or mesh fabric 101 may host an analogue to digital converter (ADC) 107 and an antenna or aerial 112 for transmission of data from MCU 116 to a remote device.
  • ADC 107 may be part of MCU 116.
  • Aerial 112 may be integral to mesh fabric 101 to facilitate a larger surface area for transmission.
  • transmission of data to a remote device may additionally or alternatively be performed by direct physical wired connection, via Bluetooth connectivity, via a wireless connection, using radio frequency transmission or via the Internet .
  • Device 100 may include one or more communication components to allow this communication to take place.
  • Primary band 103 may comprise a firm material with a degree of stretch, to allow primary band 103 to be positioned on leg 110 and remain comfortably in position.
  • Secondary bands 102 and interconnecting band 104 may also be made of a firm material with a degree of stretch.
  • Mesh fabric 101 may also have some elasticity to allow it to fit snugly to the body of the user. This is important as much for the comfort of the wearer as it is for the quality of collection of the physiological data, as device 100 should be worn close to the skin to allow for accurate sensor readings.
  • Device 100 may comprise a stretchable and flexible material that can be worn comfortably by the user.
  • the parts of the device 100 containing primary band 103, secondary bands 102 and antenna aerial 112 may be less stretchable and less flexible than other parts, in order to provide stability to these components.
  • the material may also be breathable and durable for use over a designated period of time.
  • the material may be non-allergenic in nature.
  • Battery 105 may be chosen to reduce the risk of harm to the user, either on initial application or over a length of time for which device 100 may be worn.
  • Devices 100 are fitted to the limbs of the user and each device 100 is assigned to the user by the application software 114. This process is carried out by the clinician or nursing staff who are looking after the user. Each user is assigned a unique identifier associated with the user. This is associated with an account set up on application software 114 (shown in Figure 8), which confirms that device 100 is associated with that particular individual user. If the system is unable to correctly assign and associate devices 100 with the individual user, an alert will be raised. This alert may appear on the user interface of application software 114.
  • device 100 may comprise one or more LED or OLED lights (not shown) or other visual or audible alert means, and an alert may be displayed via device 100.
  • the analogue sensor data generated by mesh fabric 101 may be relayed from mesh fabric 101 in via a secondary band 102 and then interconnecting band 104 to a microcontroller unit (MCU) 106 receiver, which is built directly into device 100 within primary band 103.
  • MCU 106 may be configured to query the device 100 at hourly intervals for data regarding resistance and impedance of mesh fabric 101.
  • MCU 106 may query mesh fabric 101 more frequently, such as every half hour, quarter hour, or every minute.
  • MCU 106 may query mesh fabric 101 less frequently, such as once every 2, 3, 4, 5, 6, or more hours.
  • mesh fabric 101 may continuously send data signals to MCU 106.
  • the analogue data generated by the mesh material 101 is delivered to the MCU 106 via microelectrodes 119 embedded within the primary band 103, as shown in Figure 4.
  • MCU 106 may be directly connected to mesh fabric 101 using electrode connectors 119 embedded into the sensor fabric material of primary band 103.
  • Figure 5 shows an analogue signal input 108 generated by a first device 100 entering ADC 107, being passed to MCU and being converted to a digital signal output 109.
  • Figure 6 shows an analogue signal input 117 generated by a second device 100 entering ADC 107, being passed to MCU and being converted to a digital signal output 109.
  • the first device 100 and the second device 100 may be each be worn on a respective leg or limb of a user.
  • Digital signal output 109 and/or 118, data corresponding to the change of at least one property of mesh fabric 101, or other data may be transmitted from device 100 to an external or remote computing device such as data-receiving device 111 sited at a location remote from the user of device 100, as shown in Figure 8.
  • the data-receiving device 111 may be a server, which may be either a cloud-based or on premise server, that houses or has access to a database 113 and application 114.
  • the data stored in database 113 may include unique identifiers for each device 100 sending data and also unique user identifiers that associate two devices 100 to each other and/or to an individual user.
  • Application 114 in conjunction with database 113, is connected to data-receiving device 111 and manages the digital signals 109 and/or 118 received.
  • Application 114 may be configured to determine whether the change of the at least one property of mesh fabric 101 corresponds to a symptom of DVT or another condition being present in the limb.
  • application 114 may be configured to deliver absolute data received from a single device 100 or relative comparison data from two related device 100 for visualisation by an observer on a remote device.
  • application 114 may additionally or alternatively be configured to deliver temporal comparison data showing from one device 100 received over a period of time for visualisation by an observer on a remote device.
  • the observer may be a clinician or nurse or the user. The user may wish to receive this data if device 100 is being used in a non-clinical scenario, such as long journey, where DVT is a risk.
  • the remote device may be a device directly associated with the user or observer, such as a handheld device 115.
  • handheld device 115 may be a monitoring device in the near vicinity to the user, which may collect data from a number of individuals. This may occur, for example, in a hospital ward, with the device data being visualised at the nurses' station.
  • the remote device may be a distant monitoring device 116, which again may collect data from a number of individuals in the community.
  • the data may be available from application 114 after being received by database 113 and visualised by the observer, being one or more of the user and medical teams including nurses, general practitioners and hospital staff. Exception monitoring may enable alerts to be set within the system to flag up changes in the data received from one or more device 100, which in turn will alert the observer to act upon the information.
  • Application 114 may be configured to calculate the difference between data output values of one or more devices 100 and to express the information as an alert signal or using a graphical user interface (GUI) via remote device 115 or 116.
  • GUI graphical user interface
  • the data received from two devices differs greatly, it may be a sign that only one of a user's legs is experiencing pain, swelling, heat or colour change, which my indicate that DVT or a similar condition is present in that limb. In this case, an alert may be generated by application 114.
  • Figure 7 shows how data received from a first and second associated device 100 may be processed.
  • First digital sensor data 109 is generated based on first analogue sensor data 108 by a first ADC 107 in a first device 100
  • second digital sensor data 118 is calculated based on second analogue sensor data 117 by a second ADC 107 in a second device 100.
  • First digital sensor data 109 received from one device 100 is compared against second digital sensor data 118 received from a corresponding device 100 worn on another limb the user in order to detect whether there is a difference between the two readings. This may be calculated by subtracting data 118 from data 109 to arrive at the difference data 115.
  • the comparison may be carried out automatically by application 114 after querying database 113. If the difference data 115 exceeds a predetermined threshold, this may indicate that DVT or a similar condition is present in one of the limbs on which devices 100 are worn.
  • the calculated difference data 115 may enable an observer to detect a new event within an individual user rather than relying on non-comparison absolute or non-relative data, which may be of less use in a particular individual when assessing the risk of an event occurring in a limb.
  • An example of absolute data is the data received from a single device 100 in terms of the digital data output from that device 100, being the data signall09 or 118.
  • Some embodiments may also allow for the ability to also collect absolute non-relative data as part of the detection and monitoring process, such as data collected by device 100 when it is known that it is worn by a healthy user. This will allow base measurements to be established which will allow for the identification of variation from the baseline measurement by comparing the baseline measurement to measurements taken at regular time intervals.
  • measurements may be taken at hourly intervals, in keeping with clinical observations of a patient who may be having a clinical event relating to a limb, such as the onset of a DVT.
  • measurements may be taken more or less frequently, such as every minute, quarter hour, half hour, or every 2, 3, 4, 5, 6 or more hours, for example.
  • Devices 100 may be used in pairs, one on each leg 110 of a user, where the baseline may be established at the initial point in time at which devices 100 are positioned on the calves of the user.
  • Devices 100 may be powered by a number of methods including one or more of a small battery 105, which may be sited within or in close proximity to device 100 and which may be electrically connected to device 100, a wired connection directly from a power socket supply, an inductive charging method from a nearby source, photovoltaic light sources, radiofrequency (RF) energy sources, electromagnetic (EM) waves via antennae, magnetic resonance (MR) inductive power transfer and energy sources derived from the body itself including thermoelectrical, biomechanical and
  • MCU 106 may be configured to collect and transmit data from the sensor transducers within mesh fabric 101, as well as to allow association of the device 100 on each leg to the person user, by storing a unique device identification number, for example.
  • FIG 9 shows a flowchart of a method 900 of operation of two devices 100.
  • the method starts at step 901, at which a first device 100 is digitally or electronically paired with a second device 100 at an application level via an association created by software application 114.
  • each device 100 further communicates with application software 114, and may be assigned a unique identification number by software 114.
  • devices 100 are placed on the calf area or whole leg of the recipient, as shown above with reference to Figures 1 to 3.
  • sensor data from each device 100 is submitted to the application 114, as shown with reference to Figure 8.
  • data received from each device 100 may be stored by application 114 in database 113, associated with the unique identification number of device 100 and/or a user record associated with device 100.
  • application 114 analyses the digital data received from device 100, as described above with reference to Figure 7 and 8, and determines the difference between the data signals received from each of the first device 100 and the second device 100. According to some embodiments, application 114 may alternatively or in addition compare data received from device 100 with baseline data retrieved from database 113. Depending on the alert parameters set, at step 907 an alert may be generated if the determined difference in data signals or an absolute level for a data signal breaches predefined limits or thresholds. The alert allows the observer to clinically review the device user to determine whether the user is, in fact, suffering from DVT or another condition.

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Abstract

Selon des modes de réalisation, l'invention concerne de manière générale un dispositif de détection pour la détection d'un état dans un membre, le dispositif de détection comportant un matériau de capteur configuré pour être porté sur un membre d'un utilisateur, au moins une propriété du matériau de capteur se modifiant en réaction à un changement d'au moins une propriété du membre; et un processeur couplé électriquement au matériau de capteur et configuré pour détecter le changement de ladite une propriété du matériau de capteur. Selon des modes de réalisation, l'invention concerne également d'une manière générale un procédé de détection d'un état dans un membre, le procédé comprenant la réception de données en provenance d'un premier dispositif de détection portable, les données correspondant à au moins une propriété d'un membre sur lequel le premier dispositif de détection est porté; la comparaison des données reçues avec des données de base; et sur la base de la comparaison, la détermination de la correspondance ou non des données reçues à un symptôme de l'état présent dans le membre.
PCT/AU2017/051032 2016-09-23 2017-09-22 Outil de détection de thrombose veineuse profonde (dvt) Ceased WO2018053593A1 (fr)

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AU2016903846A AU2016903846A0 (en) 2016-09-23 DTV detection tool
AU2016903846 2016-09-23

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Citations (8)

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WO2009007780A2 (fr) * 2006-10-26 2009-01-15 Medical Compression Systems (D.B.N.) Ltd. Prévention et de diagnostic d'une thrombose veineuse profonde: système et méthode
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EP3081162A2 (fr) * 2015-03-24 2016-10-19 Covidien LP Dispositif de détection d'une maladie vasculaire
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US20030199783A1 (en) * 2002-04-17 2003-10-23 Matthew Bloom User-retainable temperature and impedance monitoring methods and devices
WO2009007780A2 (fr) * 2006-10-26 2009-01-15 Medical Compression Systems (D.B.N.) Ltd. Prévention et de diagnostic d'une thrombose veineuse profonde: système et méthode
WO2013177112A1 (fr) * 2012-05-22 2013-11-28 Hot Dot, Inc. Patch thermochromatique pour surveiller/détecter la température corporelle
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US20160015297A1 (en) * 2014-07-17 2016-01-21 Cardimetrix Llc Device for detecting presence and severity of edema
EP3081162A2 (fr) * 2015-03-24 2016-10-19 Covidien LP Dispositif de détection d'une maladie vasculaire
WO2017127157A1 (fr) * 2016-01-21 2017-07-27 Plethy, Inc. Dispositifs, systèmes et procédés pour surveillance de santé utilisant des changements circonférentiels d'une partie du corps

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