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WO2025096635A1 - Systèmes et méthodes d'évaluation de la douleur - Google Patents

Systèmes et méthodes d'évaluation de la douleur Download PDF

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Publication number
WO2025096635A1
WO2025096635A1 PCT/US2024/053694 US2024053694W WO2025096635A1 WO 2025096635 A1 WO2025096635 A1 WO 2025096635A1 US 2024053694 W US2024053694 W US 2024053694W WO 2025096635 A1 WO2025096635 A1 WO 2025096635A1
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WO
WIPO (PCT)
Prior art keywords
user
pain
intensity
stimulation
stimulus
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Pending
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PCT/US2024/053694
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English (en)
Inventor
Ian KOPP
Khang TO
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Xpressivetech Inc
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Xpressivetech Inc
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Publication of WO2025096635A1 publication Critical patent/WO2025096635A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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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
    • A61B5/48—Other medical applications
    • A61B5/4824—Touch or pain perception evaluation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • 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
    • A61B5/681—Wristwatch-type devices

Definitions

  • TECHNICAL FIELD f 0002 J The present technology relates to assessment of pain and other conditions within the medical field to improve accuracy and accessibility.
  • a system for pain assessment is capable of administering an electrical stimulus to the user which can be controlled in amplitude, frequency, wave shape and duration by the user. This can then be associated with a numerical value which may vary over time.
  • a pain assessment system can include a small wearable stimulation device, a control device (e.g., a smartphone or tablet) running a software application capable of controlling the operation of the stimulation device, and a machine learning or statistical algorithm to evaluate and analyze data collected via the wearable stimulation device and/or the control device.
  • FIG. 1A is a schematic block diagram of a pain assessment system in accordance with the present technology.
  • FIG. IB is a schematic illustration of the pain assessment system operated by a user in accordance with the present technology.
  • FIG. 2 is a perspective view of components of a pain assessment system in accordance with the present technology.
  • FIGS. 3A and 3B are side perspective and plan views, respectively, of a wearable stimulator device for a pain assessment system in accordance with the present technology.
  • FIGS. 4 and 5 are example graphs of stimulus values over time in accordance with the present technology.
  • FIGS. 6A-6E illustrate example user interfaces for a control device of a pain assessment system in accordance with the present technology.
  • FIGS. 7 A and 7B illustrate an example user interface and corresponding stimulation waveform for a pain assessment system in accordance with the present technology.
  • FIGS. 8 A and 8B illustrate an example user interface and corresponding stimulation waveform for a pain assessment system in accordance with the present technology.
  • FIGS. 9A-10B illustrate example user interfaces for calibrating a pain assessment system in accordance with the present technology.
  • FIG. 11A illustrates an example user interface for capturing a user-provided pain recording in accordance with the present technology.
  • FIG. 11B illustrates an example stimulation waveform based on the user- provided pain recording of FIG. 11 A.
  • FIG. 11C illustrates an example user interface following recording of the applied stimulation waveform of FIG. 11B.
  • FIG. 12A illustrates another example user interface for capturing a user- provided pain recording in accordance with the present technology.
  • FIG. 12B illustrates an example stimulation waveform based on the user- provided pain recording of FIG. 12 A.
  • FIG. 12C illustrates an example user interface following recording of the applied stimulation waveform of FIG. 12B.
  • FIG. 13 A illustrates another example user interface for capturing a user- provided pain recording in accordance with the present technology.
  • FIG. 13B illustrates an example stimulation waveform based on the user- provided pain recording of FIG. 13 A.
  • FIG. 13C illustrates an example user interface following recording of the applied stimulation waveform of FIG. 13B.
  • Figure 14A illustrates another example stimulation waveform based on a user- provided pain recording.
  • Figure 14B illustrates an example user interface following recording of the applied stimulation waveform of FIG. 14B.
  • Figure 15 A illustrates another example stimulation waveform based on a user- provided pain recording.
  • Figure 15B illustrates an example user interface following recording of the applied stimulation waveform of FIG. 15B.
  • Figure 16A illustrates another example stimulation waveform based on a user- provided pain recording.
  • Figure 16B illustrates an example user interface following recording of the applied stimulation waveform of FIG. 16B.
  • the stimulation device can assume various form factors, such as that resembling a watch, bracelet, ring, or any other suitable form factor.
  • the wearable stimulation device can operate in conjunction with a separate control device (e.g., a portable computing device such as a smartphone, tablet, etc.).
  • the control device can provide a control signal to the wearable stimulation device that causes the stimulation device to output a stimulus (e.g., apply an electric current to the user’s skin or provide other suitable stimulation).
  • the control device can provide a signal (transmitted via wired or wireless connection) transmitting stimulus characteristic information such as amplitude, frequency, duty cycle, wave shape, etc.
  • the user can control the applied stimulation levels via the control device (e.g., interacting with a user interface of the control device). This can involve an initial configuration and calibration as well as ongoing control of the stimulation delivered over time. The user can be prompted to adjust the applied stimulation until the pain caused by the stimulation is similar in magnitude, severity, and/or with respect to the nature or qualities of the native pain the user feels elsewhere in her body. The control device can then indicate the associated output signal delivered at that time.
  • the various control signals and user input data can be collected and transmitted to one or more external computing devices (e.g., remote servers or other suitable devices) for further processing, including aggregation with data from a wide population of users.
  • aspects of the technology can also be practiced in distributed computing environments where tasks or modules are performed by remote processing devices, which are linked through a communication network (e.g., a wireless communication network, a wired communication network, a cellular communication network, the Internet, a short-range radio network (e.g., via Bluetooth)).
  • a communication network e.g., a wireless communication network, a wired communication network, a cellular communication network, the Internet, a short-range radio network (e.g., via Bluetooth)
  • program modules may be located in both local and remote memory storage devices.
  • Computer-implemented instructions, data structures, screen displays, and other data under aspects of the technology may be stored or distributed on computer-readable storage media, including magnetically or optically readable computer disks, as microcode on semiconductor memory, nanotechnology memory, organic or optical memory, or other portable and/or non-transitory data storage media.
  • aspects of the technology may be distributed over the Internet or over other networks (e.g. a Bluetooth network) on a propagated signal on a propagation medium (e.g., an electromagnetic wave(s), a sound wave) over a period of time, or may be provided on any analog or digital network (packet switched, circuit switched, or other scheme).
  • FIG. 1A is a schematic diagram of a pain assessment system 100 configured in accordance with an embodiment of the disclosed technology.
  • FIG. IB is a schematic diagram of the pain assessment system 100 in operation by a user.
  • the system 100 is shown with certain devices for purposes of explanation, in various examples any one or more of the devices shown in FIGS. 1 A and IB can be omitted.
  • the devices shown in FIGS. 1 A and IB are illustrated as including certain components, in various examples any one or more of the particular components within these devices can be omitted (e.g., the stimulation device 110 may omit the sensor(s) 113).
  • any of the devices can include additional components not specifically shown here.
  • the pain assessment system 100 can include one or more stimulation device(s) 110 which can be worn by or otherwise coupled to a user 190, a control device 150, and one or more external computing device(s) 180.
  • the stimulation device 110 includes one or more actuator(s) 111, one or more sensor(s) 113, input 115, output 117, a power source 119, a communications link 121, signal processing component(s) 123, and a memory 125.
  • the stimulation device 110 is configured to be coupled to a user for delivery if stimulus to induce pain at a delivery site.
  • the stimulation device 110 can be removably worn by the user, for example positioned directly over the user’s wrist and held in place via a band or other fastener.
  • the actuator(s) 111 can be any suitable component or combination of components configured to supply stimulus to the user to induce pain at the application site.
  • the actuator(s) 111 can be configured to induce pain without causing lasting damage to the user’s body, such as leaving scars or enduring pain beyond the application of the stimulation.
  • suitable actuators include one or more electrodes configured to deliver electrical energy to the user’s skin.
  • several levels of intensity in stimulation can be obtained by the amount of voltage supplied to the actuator(s) 111.
  • different patterns can be provided achieving variance in rhythm, pulses, intensity, and/or frequency of the stimulation.
  • the stimulation device can employ different types of actuators 111 and/or stimulus delivery mechanisms, either individually or in combination. While some examples described herein relate on electrical stimulation via electrodes, the technology can utilize various other approaches for delivering controlled stimuli to the user.
  • the electrical stimulation can be delivered using various waveform patterns.
  • the actuator(s) can deliver square waves, sine waves, triangle waves, sawtooth patterns, or arbitrary custom waveforms.
  • the control signal can specify multiple waveform parameters. These parameters can include the basic wave shape, such as square, sine, or triangle waves.
  • the signal can additionally specify one or more frequencies, allowing for either single-frequency operation or frequency modulation over time.
  • the control signal can also define amplitude characteristics, including fixed amplitude levels or amplitude modulation patterns. Phase relationships between multiple signals can be specified when using multiple stimulation channels.
  • the system can additionally control duty cycle variations and pulse width modulation parameters. Rise and fall characteristics of the waveform can be independently specified, as can DC offset or bias levels applied to the base waveform.
  • multiple electrodes can be arranged in arrays or patterns to deliver spatially varying stimulation.
  • the control signal can specify different parameters for different regions of the electrode array, creating patterns of stimulation that move or change over time.
  • the system can employ multiple stimulation channels operating independently or in coordination to create complex stimulus patterns.
  • the actuator(s) 111 can include various mechanical stimulation mechanisms.
  • the stimulation device can incorporate vibrational elements that operate at different frequencies and amplitudes.
  • the device can include arrays of moveable pins or protrusions that can apply localized pressure to specific areas of the user's skin.
  • Pneumatic or hydraulic systems can be employed for delivering controlled pressure patterns across the stimulation area.
  • Mechanical resonators can create standing wave patterns that provide distinctive sensations.
  • the system can incorporate ultrasonic transducers for deep tissue stimulation.
  • Microelectromechanical systems (MEMS) can be used for precise mechanical control of stimulus delivery.
  • the mechanical stimulation can be applied in patterns that vary spatially and temporally.
  • the system can coordinate multiple mechanical actuators to create traveling waves, pressure gradients, or other complex patterns of mechanical stimulation.
  • the actuator(s) 111 can include thermal elements for delivering controlled temperature variations.
  • the system can employ thermoelectric (Peltier) devices that enable both heating and cooling functions through the same component. Resistive heating elements can provide controlled warming effects. Infrared emitters can deliver radiative heating to specific areas.
  • the system can incorporate heat sink systems for controlled cooling effects. Thermal mass elements can be used for creating sustained temperature effects.
  • Microfluidic systems can enable liquid-based thermal control with precise temperature regulation.
  • the thermal stimulation can be precisely controlled in both temperature and timing, allowing for rapid or gradual temperature changes.
  • the system can maintain temperature profiles within safe operating bounds while delivering thermal patterns that correspond to user-indicated pain characteristics.
  • the stimulation device can include mechanisms for controlled release or application of chemical compounds that create sensations corresponding to different pain characteristics.
  • the device can employ microfluidic delivery systems for precise control of chemical release. lontophoretic transport mechanisms can be used to control the delivery of ionic compounds.
  • the system can utilize controlled-release matrices that modulate compound delivery over time. Microporous membranes can regulate the release of active compounds. Electrochemical activation systems can provide controlled delivery of stimulus-producing compounds. Microneedle arrays can enable shallow chemical delivery with spatial control.
  • the stimulation device can employ multiple stimulation modalities simultaneously or in coordinated patterns.
  • the system can include appropriate safety mechanisms.
  • the implementation can incorporate maximum intensity limiters specific to each stimulus type. Temperature monitoring and thermal cutoff systems can prevent excessive heating or cooling.
  • the device can include pressure and force monitoring systems when delivering mechanical stimulation. Chemical concentration and delivery rate limiters can ensure safe compound administration.
  • the system can employ cross-modal safety interlocks to prevent unsafe combinations of stimuli. Physiological response monitoring can enable dynamic safety adjustments.
  • the device can perform real-time stimulus adjustment based on sensor feedback to maintain safe and effective operation.
  • the sensor(s) 113 can include a number of different sensors and/or types of sensors.
  • the sensor(s) 113 can include a plurality of electrodes, an accelerometer, a blood pressure sensor, a pulse oximeter, an ECG sensor or other heart-recording device, an EMG sensor or other muscle-activity recording device, a temperature sensor, a skin galvanometer, hygrometer, altimeter, gyroscope, magnetometer, proximity sensor, hall effect sensors, or any other suitable sensor for monitoring physiological characteristics of the user.
  • These particular sensors are exemplary, and in various embodiments the sensors employed can vary.
  • the stimulation device 110 omits the sensors 113 altogether.
  • the power source 119 can be rechargeable, for example using inductive charging or other wireless charging techniques. Such rechargeability can facilitate long-term placement of the stimulation device 110 on or within a user.
  • the input 115 and output 117 components can include, for example one or more buttons, keys, lights, microphones, speakers, ports (e.g., USB-C connector ports), touch-sensitive screen or other surface, etc.
  • the stimulation device 110 can omit the input 115 and/or output 117 components altogether.
  • the communications link 121 enables the stimulation device 110 to transmit to and/or receive data from external devices (e.g., control device 150 or external computing devices 180).
  • the communications link 121 can include a wired communication link and/or a wireless communication link (e.g., Bluetooth, Near-Field Communications, LTE, 5G, Wi-Fi, infrared and/or another wireless radio transmission network).
  • the simulation device 110 can additionally include signal processing component(s) 123 that are configured to control operation of the actuator(s) 111.
  • the signal processing component(s) 123 can include drive circuitry configured to deliver waveforms having predetermined and controllable parameters to one or more actuator(s) 111.
  • the waveforms or other drive signal delivered to the actuator(s) 111 can be based at least in part on a control signal received from the control device 150 (e.g., via communications link 121).
  • a low-amplitude analog control signal similar to an audio signal can be sent from the control device 150 to the stimulation device 110.
  • the signal processing component(s) 123 can generate one or more drive signals that cause the actuator(s) 111 to apply stimulus to the user (e.g., applying electric current to the user’s skin).
  • the stimulation device 110 can include memory 125, which can take the form of one or more computer readable storage modules configured to store information (e.g., signal data, subject information or profiles, environmental data, stimulus regimes, data collected from one or more sensing components, media files) and/or executable instructions that can be executed by a controller.
  • the stimulation device 110 can omit the memory 125, and operate solely based on real-time response to a control signal received from the control device 150.
  • the stimulation device 110 can include a controller such as a processor or central processing unit (“CPU”) that controls operation of the stimulation device 110 in accordance with computer-readable instructions stored on the memory 125.
  • a controller may be any logic processing unit, such as one or more CPUs, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc.
  • DSPs digital signal processors
  • ASICs application-specific integrated circuits
  • such a controller can also be configured to initiate data collection via one or more sensor(s) 113.
  • the sensor(s) 113 of the stimulation device 110 can detect physiological data of a user (e.g., motion data, temperature data, heart rhythm data, etc.). In some embodiments, this physiological data can be used in a feedback loop to affect operation of the actuator(s) 111.
  • the stimulation device 110 can be communicatively coupled to the control device 150, for example via a wireless connection.
  • the control device 150 can be a mobile device (e.g., a smartphone, tablet, smartwatch, etc.) or other computing device with which the user can interact.
  • the stimulation device 110 and the control device 150 can be combined into a single device (e.g., a smartwatch, head-mounted display device, etc.).
  • the stimulation device 110 may receive input from and/or can be controlled by instructions from the control device 150.
  • the control device 150 can cause the stimulation device 110 to initiate or cease stimulus delivery, can schedule stimulus delivery sessions, can vary parameters of the stimulus delivery (e.g., intensity, frequency, etc.), and/or provide other control instructions to the stimulation device 110.
  • the control device 150 may output user prompts which can be used to allow the user to control stimulus delivery.
  • the stimulation device 110 may also communicate information to the control device 150, whether by wired or wireless communication link. For instance, the stimulation device 110 can transmit data indicative of stimulation that has been applied during a session. Additionally or alternatively, the stimulation device 110 can transmit data related to sensor readings, device operation or performance, or any other parameters that may be useful in evaluating and effecting future operation of the stimulation device 110. I0056J
  • the stimulation device 110 and/or the control device 150 can also be communicatively coupled with one or more external computing devices 180 (e.g., over network 170).
  • the external computing devices 180 can take the form of servers, personal computers, tablet computers or other computing devices associated with one or more healthcare providers (e.g., hospitals, medical data analytics companies, device manufacturers, etc.).
  • These external computing devices 180 can collect data recorded by the stimulation device 110 and/or the control device 150. In some embodiments, such data can be anonymized and aggregated to perform large-scale analysis (e.g., using machine-learning techniques or other suitable data analysis techniques) to develop and improve pain-assessment or treatment algorithms using data collected by a large number of stimulation devices 110. Additionally, the external computing devices 180 may transmit data to the control device 150 and/or the stimulation device 110.
  • large-scale analysis e.g., using machine-learning techniques or other suitable data analysis techniques
  • the external computing devices 180 may transmit data to the control device 150 and/or the stimulation device 110.
  • an updated algorithm for pain assessment, stimulus delivery, or other such algorithm may be developed by the external computing devices 180 (e.g., using machine learning or other techniques) and then provided to the stimulation device 110 and/or the control device 150 via the network (e.g., as an over-the-air update), and installed on the stimulation device 110 and/or control device 150.
  • the external computing devices 180 e.g., using machine learning or other techniques
  • the network e.g., as an over-the-air update
  • the stimulation device 110 may also communicate with a control device 150.
  • the control device 150 can be, for example, a smartwatch, smartphone, laptop, tablet, desktop PC, or any other suitable computing device and can include one or more features, applications and/or other elements commonly found in such devices.
  • the control device 150 can include display 151, a communications link 153 (e.g., a wireless transceiver (e.g., a BLUETOOTH transceiver) that may include one or more antennas for wirelessly communicating with, for example, other devices, websites, and the stimulation device 110).
  • a wireless transceiver e.g., a BLUETOOTH transceiver
  • control device 150 Communication between the control device 150 and other devices can be performed via, e.g., a network 170 (which can include the Internet, public and private intranet, a local or extended Wi-Fi network, cell towers, the plain old telephone system (POTS), etc.), direct wireless communication, etc.
  • the control device 150 can additionally include well- known input components 155 and output components 157, including, for example, a touch screen, a keypad, speakers, a camera, etc.
  • the control device 150 can include a power supply 159 (e.g., rechargeable battery), one or more processors 161, memory 163, and/or any other components 165.
  • a power supply 159 e.g., rechargeable battery
  • a user interface of the control device can be used to capture pain assessments provided by a user. For instance, a user input in the form of a path traced over a region of the touchscreen display may indicate a timevarying pain intensity. This recorded path may then be used to generate a control signal to drive a stimulation applied via the stimulation device, after which the user may confirm that the applied stimulation corresponds to the user’s subject pain, or the user may alter the input to more closely align with the user’s pain experience.
  • control device can implement various user interface configurations beyond the touch-based path tracing described above.
  • the interface can accept three-dimensional gesture input captured via the device's camera or motion sensors, allowing users to indicate pain intensity through spatial hand movements.
  • the control device can map the spatial position and movement characteristics to corresponding stimulus parameters.
  • the system can provide alternative input mechanisms such as voice commands indicating intensity levels or patterns.
  • the interface can also accept input through eye tracking, where gaze position on the display corresponds to pain intensity and gaze duration represents temporal aspects of the pain experience.
  • brain-computer interfaces can enable direct neural input for controlling stimulus parameters.
  • the interface can provide haptic feedback during input, allowing users to feel tactile confirmation of their intensity selections. This haptic response can be coordinated with the stimulation delivery to provide an integrated sensory experience. Additionally or alternatively, the interface can employ pressure-sensitive displays, where the force of touch input corresponds to pain intensity.
  • the interface can support simultaneous input from multiple users, enabling real-time adjustment and calibration during consultation sessions.
  • the interface can also provide split-screen views showing both the patient's input and the resulting stimulus parameters.
  • the external computing device(s) 180 can take the form of servers or other computing devices associated with healthcare providers or other entities.
  • the external devices can include a communications link 181 (e.g., components to facilitate wired or wireless communication with other devices either directly or via the network 170), a memory 183, and one or more processors 185.
  • These external computing devices 180 can collect data recorded by the stimulation device 110 and/or the control device 150.
  • data can be anonymized and aggregated to perform large-scale analysis (e.g., using machine-learning techniques or other suitable data analysis techniques) to develop and improve pain assessment, treatment, or other such algorithms using data collected by a large number of stimulation devices 110 associated with a large population of users.
  • the external computing devices 180 may transmit data to the control device 150 and/or the stimulation device 110.
  • an updated algorithm for assessing or treating conditions may be developed by the external computing devices 180 (e.g., using machine learning or other techniques) and then provided to the stimulation device 110 and/or the control device 150 via the network 170, and installed on the recipient stimulation device 110 and/or control device 150.
  • the system 100 can incorporate various networking capabilities to enhance functionality and enable broader healthcare integration.
  • the control device 150 can establish secure, real-time connections with healthcare providers' systems, enabling remote pain assessment sessions. During these sessions, the healthcare provider can observe the user's input and stimulus patterns in real time, adjust parameters, and provide immediate feedback through integrated audio/video communication channels.
  • the system can implement distributed processing approaches where complex analysis tasks are shared between the control device 150, stimulation device 110, and cloudbased resources such as external computing device(s) 180.
  • This distributed architecture can enable advanced pattern recognition and analysis while maintaining responsive local control of stimulus delivery.
  • the system can maintain a local cache of essential functions while leveraging cloud resources for intensive computational tasks.
  • Cross-platform synchronization capabilities can enable seamless transition between different control devices while maintaining consistent user profiles and calibration settings.
  • the system can employ secure protocols for transferring session data and device settings between platforms, ensuring continuity of care across different locations or devices. Additionally or alternatively, the system can synchronize with other healthcare devices and systems, integrating pain assessment data with broader health monitoring platforms.
  • the network features can include emergency notification systems that automatically alert healthcare providers or designated contacts when certain pain patterns or threshold conditions are detected. These notifications can be customized based on user-specific parameters and healthcare protocols, with different notification levels for different conditions or circumstances.
  • FIG. 2 illustrates one example configuration of the stimulation device 110 and the control device 150.
  • the stimulation device 110 which can be sized and configured to be worn over a user’s wrist, hand, or other suitable location, optionally with the use of a strap, garment, fastener, or other structure to hold the stimulation device 110 in position over the application site.
  • FIGS. 3A and 3B illustrate side perspective and plan views, respectively, of an example stimulation device 110 in which a housing securing the internal components is coupled to a strap that can secure the stimulation device 110 against a user’s wrist or at another suitable location. External electrodes or other such actuators 111 (not shown in FIGS.
  • actuators 111 can be configured to deliver the stimulus without direct contact (e.g. thermal actuators delivering radiant heating).
  • actuators 111 can be separate components that are coupled to the stimulation device 110, such as TENS electrodes that can be connected to a stimulation device 110 via wires.
  • control device 150 can take the form of a smartphone. Additionally or alternatively, the control device 150 can be any suitable computing device, whether portable or stationary, such as a tablet, laptop, smartwatch, head-mounted computing device, desktop computer, etc.
  • the stimulation device 110 can include circuitry (e.g., communication link 121) that allows the stimulation device 110 to pair with the control device 150 via a wireless connection (e.g., BLUETOOTH) to receive a control signal from the control device 150.
  • the control signal can transmit stimulus characteristic information such as amplitude, frequency, duty cycle, wave shape, etc.
  • the control device 150 can then construct and apply an appropriate stimulus (e.g., via actuator(s) 111), which may vary over time along one or more characteristics (e.g., amplitude, frequency, duty cycle, wave shape, etc.). .
  • the signal processing components of the stimulation device 110 can include protection circuitry to prevent the output to the user from exceeding a safe voltage level, as well as protecting against the output being delivered for a longer duration than is deemed safe. Additionally or alternatively, stimulation can be terminated either via the stimulation device 110 (e.g., via an off-switch or other user interface) or via the control device 150 (e.g., user selection of an “off’ button via a touchscreen or otherwise).
  • the stimulation device 110 is configured to deliver the intended stimulus to the user (e.g., delivering the intended voltage, waveform, and current levels to the skin in the case of electrical stimulus).
  • the stimulation device 110 can sense the voltage delivered to the skin and utilize this in a feedback loop to help compensate for variability in the contact resistance to the skin. For instance, this can be accomplished using a conductive surface connected to the interior of the stimulation device 110 that contacts the user’s skin.
  • the stimulation output e.g., voltage/current supplied to electrodes
  • FIGS. 4 and 5 are graphs illustrating example variations in applied voltage over time.
  • the applied voltage can always remain below a predetermined maximum voltage level to ensure user safety.
  • the voltage is monotonically increased over time until reaching the maximum level.
  • the voltage varies more dynamically, from a highest level VI, then dropped to a lower level V2 before increasing again to an intermediate level V3.
  • the user may be prompted to indicate the stimulus level at which the perceived pain from the stimulus has the same magnitude or severity as the pain experienced at other parts of the body. For instance, a user with a chronic back pain condition may perform a pain assessment at different times throughout the day, and at each time the corresponding stimulus level identified by the user may be different depending on the severity of the user’s back pain at those times. By correlating the user’s other pain to the pain induced by the controlled stimulus, a more objective assessment of the user’s pain can be established.
  • control device 150 can run a software application (or app) that facilitates control of the stimulation device 110.
  • the control device 150 can output a control signal (e.g., via wired or wireless connection) to the stimulation device 110, which is then used by the stimulation device 110 to deliver a stimulus to the user (e.g., supply electric current to the user’s skin via one or more electrodes).
  • the software application running on the control device 150 can also provide a user interface (e.g., a graphical user interface displayed via a display screen of the control device 150) that both outputs information to the user and can receive user input (e.g., via touch screen input, voice input, physical buttons, etc.).
  • the user input can include direct modifications of the stimulus output (e.g., lowering or raising the stimulus output), an indication by the user that the current stimulus output level corresponds to a pain level similar in magnitude (or other characteristic) to other pain experienced by the patient, or other suitable user input.
  • users can provide additional information, such as medical history, medication information, other physiological data (e.g., height, weight, blood pressure, etc.), or any other suitable information.
  • FIGS. 6A-6E illustrate example user interfaces that can be presented to a user via the control device 150.
  • FIG. 6A illustrates a main menu in which a user can select among options, including taking a pain assessment (referred to in the user interface as an “Xpression”).
  • FIG. 6B illustrates an example user interface through which a user can add medication information.
  • FIG. 6C illustrates an example user interface in which a user can review her history of individual pain assessments (“Review Xpressions”). Selecting an individual pain assessment generates the user interface as shown in FIG. 6D (“Individual Xpression”), which depicts a user’s Personal Pain Scale over time as a graph.
  • FIG. 6D Intelligent Xpression
  • This graph can reflect the user’s selected stimulus levels over time that correspond to the pain experienced by the user from other pain (i.e., pain due to conditions unrelated to the applied stimulus).
  • This user interface can also provide other physiological measurements where available, such as pulse, blood pressure, body temperature, barometric pressure, and outside temperature readings. Selecting the “Pain Intensity Delta Trend” tab generates the user interface in FIG. 6E, which shows the patient’s Personal Pain Scale over a longer period of time (e.g., on the order of days or weeks), which can indicate how a patient’s pain has changed over time.
  • a splash screen can first load, followed by a menu system that guides the user through the process of adding their information, instructions for applying the device, calibrating the device, and guided step-by- step instructions for using the device to take measurements.
  • Calibration can include applying stimulus levels that the user can associate with a sensation threshold corresponding to where a sensation is first felt and a tolerance threshold which is the maximum level the user is comfortable with administering.
  • the measurement process can allow the user to control frequency, amplitude, wave shape and duration over time to replicate the pain felt elsewhere in their body. Each of these values can be recorded via the software application as a measurement event. Measurement readings can be stored in a data structure and transmitted via the application to a remote, back-end server (e.g., external computing devices 180 shown in FIGS. 1A-1B). The application can also generate graphs of the measurements for viewing by the user or others, as well as transmitting files for viewing over client computing devices. In various examples, the application (or software running on external computing devices 180) can utilize machine learning and artificial intelligence algorithms to provide predictive methods for pain management. The machine learning algorithm can take the form of neural networks trained with basic PMI variables found in available data sets. Such a machine learning algorithm can find patterns associated with the new personal pain score, and existing PMI to provide past activities, therapies, or treatments that were the most successful to the patient and their provider to ensure the most effective care plan is developed.
  • FIGS. 7A-10B illustrate example user interfaces and corresponding waveform outputs associated with calibrating a pain assessment system in accordance with implementations of the present technology.
  • the calibration process can be performed during initial setup of the system, at periodic intervals, or at any time as selected by the user.
  • the user interface displayed via the control device can present adjustable parameters for establishing baseline stimulation levels.
  • these parameters include a “sensation” level corresponding to a minimum stimulation threshold at which the user first detects the stimulus, and an “intensity” level corresponding to a maximum comfortable stimulation threshold.
  • the user interface can include visual indicators showing current values of these parameters, along with one or more user input mechanisms for adjusting the values.
  • FIG. 7B illustrates a corresponding waveform output when the sensation level is set to a relatively low value (e.g., level 1 as shown), which results in a square wave output having a correspondingly low amplitude.
  • the user interface allows adjustment of the sensation level while maintaining the previously established intensity level.
  • the resulting waveform output depicted in FIG. 8B, exhibits an increased amplitude corresponding to the higher sensation level selected by the user.
  • the waveform maintains its general characteristics (e.g., square wave shape, frequency, duty cycle) while the amplitude is modified based on the user input.
  • other waveform parameters can be adjusted based on the sensation level settings, such as frequency, duty cycle, wave shape, or combinations thereof.
  • FIGS. 9A-10B illustrate example user interactions for establishing and saving calibration parameters. Specifically, FIGS. 9A and 9B demonstrate a process for adjusting and saving the sensation level.
  • the user can provide input via a sliding gesture applied to a touch-sensitive display of the control device, with the position of a slider element along a horizontal axis corresponding to different sensation levels.
  • the user interface can provide visual feedback indicating the currently selected level, and can include a “save” option that, when selected, stores the current sensation level for use in subsequent operations of the pain assessment system.
  • FIGS. 10A and 10B demonstrate a process for adjusting and saving the intensity level.
  • the user interface can mirror that used for sensation level adjustment, with a slider element moveable along a horizontal axis to select different intensity levels.
  • the control device prevents setting the intensity level below the previously established sensation level, ensuring proper scaling between minimum and maximum thresholds.
  • FIGS. 9A-10B depict specific examples of user interface elements (e.g., horizontal slider bars) for receiving user input
  • the calibration interface can alternatively employ various other input mechanisms.
  • Such mechanisms can include, without limitation, vertical slider bars, rotatable dials or wheels, direct numerical input, gesture-based input, voice commands, or any other suitable input modality.
  • the calibration process can involve automated adjustment of stimulus parameters based on physiological feedback received from one or more sensors associated with the stimulation device.
  • the calibration parameters established through these interfaces serve multiple purposes in operation of the pain assessment system.
  • the sensation and intensity levels define minimum and maximum bounds for subsequent stimulus delivery, ensuring both detectability and user comfort.
  • these calibration values can be used to normalize or scale user inputs during pain assessment sessions, mapping user-indicated pain levels to appropriate stimulus levels between the established thresholds.
  • the calibration data can also be stored and analyzed over time to track changes in user sensitivity or adapt system operation based on identified patterns or trends.
  • FIGS. 11A-11C illustrate an example sequence of user interfaces and corresponding stimulus output for capturing pain assessment data (“Xpression”) in accordance with implementations of the present technology. These figures specifically demonstrate recording a pain pattern characterized by an initial increase followed by maintenance at a static level.
  • the user interface displayed via the control device can include a recording area in which the user provides input indicating variations in pain intensity over time.
  • the recording area comprises a two-dimensional space where vertical position corresponds to pain intensity.
  • the horizontal position can correspond to elapsed time.
  • the temporal aspect can be captured based on movement of the user’s input over time without necessarily corresponding to horizontal movement.
  • the user can trace a path within this space using various input mechanisms, such as touching and dragging a finger across a touch-sensitive display of the control device, manipulating a cursor or other interface element via a pointing device, or providing input via any other suitable user interface component.
  • FIG. 11B depicts a stimulus waveform generated based on the user input captured in FIG. 11 A.
  • the waveform begins at a baseline level corresponding to the previously established sensation threshold, then increases in amplitude to a sustained higher level based on the user's traced path.
  • the control device can generate a control signal that, when transmitted to the stimulation device, results in delivery of a stimulus having characteristics (e.g., amplitude, frequency, wave shape, duty cycle, etc.) that vary in accordance with the user-provided input.
  • FIG. 11C illustrates a resulting record of the captured pain assessment, which can be stored in memory associated with the control device and/or transmitted to one or more external computing devices for further processing or analysis.
  • the pain assessment interface can take forms different from the specific example shown in FIG. 11 A.
  • the interface can alternatively present a circular or radial input region, where angular position corresponds to elapsed time and radial position corresponds to pain intensity.
  • the interface can employ separate input mechanisms for time and intensity, such as a slider, dial, or other control for intensity coupled with a separate timing mechanism (e.g., a button held for the duration of a particular intensity level).
  • a separate timing mechanism e.g., a button held for the duration of a particular intensity level.
  • the user interface can also provide various mechanisms for initiating and terminating a recording session. In some implementations, recording begins automatically when the user first contacts the input region and continues until the user breaks contact or selects a “save” option.
  • the interface can include explicit start/stop controls, allowing the user to pause recording or make adjustments during the session.
  • the recording duration can be open-ended, limited to a predetermined time window, or adjustable based on user preference or the particular type of pain being assessed.
  • the control device can accept voice commands indicating pain intensity levels, with the duration of each verbal command corresponding to the time course of that intensity.
  • the interface can accept input via physical buttons or controls (e.g., pressing harder on a pressure-sensitive input device to indicate higher intensity), gesture recognition (e.g., hand motions captured via a camera), or motion sensing (e.g., tilting or moving the control device).
  • the system can accept input via multiple different input modalities, either simultaneously or as selected by the user based on preference or circumstance.
  • the control device can process the received input in various ways before generating the corresponding control signal. This processing can include smoothing or filtering the input to remove unintended variations, scaling the input based on the previously established calibration parameters, applying transformations to map the input to different stimulus characteristics, or performing other signal processing operations.
  • the resulting control signal can modify one or more stimulus parameters based on the processed input, providing a pain stimulus that corresponds to the user's indicated pain pattern while maintaining safe and comfortable operating bounds.
  • FIGS. 12A-12C illustrate another example sequence of user interfaces and corresponding stimulus output for capturing pain assessment data, specifically demonstrating recording of a transient sharp pain pattern in accordance with implementations of the present technology.
  • This sequence illustrates how the system can capture and reproduce brief but intense pain experiences.
  • the user interface allows the user to trace a path indicating a sharp increase in pain intensity followed by a rapid decrease back to baseline.
  • the path begins at a low intensity level, rises steeply to a pronounced peak, and then descends back toward the initial level.
  • the vertical position of the traced path corresponds to pain intensity, while the horizontal distance covered can reflect the time course of the pain experience.
  • the rate at which the user traces the path can correspond to the temporal characteristics of the pain, allowing capture of both the intensity and timing of rapid pain transitions.
  • FIG. 12B illustrates a stimulus waveform generated based on the user input from FIG. 12 A.
  • the waveform exhibits a sharp increase from a baseline level (e.g., corresponding to the previously established sensation threshold) to a peak amplitude, followed by a decrease back to the baseline.
  • the control device generates a control signal that causes the stimulation device to modify one or more stimulus parameters in a manner corresponding to the user input.
  • These parameters can include, without limitation, amplitude, frequency, pulse width, duty cycle, or wave shape.
  • the stimulus can be delivered as a continuous variation in one or more parameters, or alternatively, as a series of discrete steps approximating the indicated intensity pattern.
  • FIG. 12C shows a recorded log of the pain assessment session, which can include various data related to the captured pain pattern.
  • the log can include metrics such as peak intensity, duration of the pain episode, rate of intensity increase (e.g., rise time from baseline to peak), rate of intensity decrease (e.g., fall time from peak to baseline), or other characteristics derived from the user input and/or the delivered stimulus.
  • the log can include contextual information such as time and date of recording, environmental conditions, physiological measurements, or other relevant data.
  • the system can process the user input in different ways when generating the corresponding stimulus pattern.
  • the control device can apply smoothing algorithms to the input path to reduce unintended variations while maintaining the essential characteristics of the pain pattern.
  • the system can analyze the input to identify specific features (e.g., peak intensity, rise time, fall time) and generate a standardized or idealized waveform matching these characteristics.
  • the system can also accommodate instances where the user-indicated pain intensity exceeds the previously established maximum threshold (intensity level).
  • the interface can allow the user to indicate pain intensities beyond the calibrated range while the delivered stimulus remains bounded by the maximum threshold.
  • the system can record this discrepancy and optionally prompt the user to perform recalibration if such instances become frequent.
  • the system can apply different scaling or mapping functions to compress the indicated pain range into the available stimulus range while maintaining relative relationships between different intensity levels.
  • the system can provide various mechanisms for ensuring accurate capture of rapid intensity changes. These can include increased sampling rates during periods of rapid change, predictive algorithms that anticipate trajectory based on initial movement, or alternative input methods particularly suited to capturing sudden variations.
  • the user can modify or refine the recorded pattern after initial capture, for instance by adjusting specific points along the intensity curve or applying timing corrections to better match their pain experience.
  • FIGS. 13A-13C illustrate a sequence for capturing pain assessment data characterized by rhythmic or throbbing sensations in accordance with implementations of the present technology.
  • the user interface captures user input in the form of a series of regular oscillations between lower and higher intensity levels.
  • the user traces multiple arc-shaped paths that represent periodic increases and decreases in pain intensity. The timing between successive peaks can correspond to the frequency of the throbbing sensation experienced by the user.
  • the vertical range of the traced path indicates the intensity variation between peak and trough states, while the horizontal spacing between peaks reflects the periodicity of the throbbing pattern.
  • FIG. 13B depicts a stimulus waveform generated based on the rhythmic input pattern.
  • the waveform comprises a series of peaks rising from and returning to a baseline level, with the amplitude and timing of these peaks corresponding to the user-provided input.
  • the control device can analyze the input pattern to identify characteristics such as throbbing frequency, peak-to-trough amplitude range, and regularity or variation in the rhythm. The control signal sent to the stimulation device can then be adjusted to reproduce these characteristics in the delivered stimulus.
  • FIG. 13C shows the recorded log of the rhythmic pain assessment.
  • the log can include specific parameters related to the rhythmic nature of the pain, such as frequency of oscillations, consistency of timing between peaks, average and maximum intensity levels, or other relevant characteristics of the throbbing pattern.
  • the system can provide specialized input mechanisms particularly suited to capturing rhythmic pain patterns.
  • the interface can include a tempo-matching feature where the user taps or makes another repeated input at the frequency of their throbbing pain.
  • the system can provide tools for specifying regular patterns and then modifying specific characteristics such as frequency, intensity range, or regularity.
  • the system can also employ pattern recognition algorithms to identify and enhance the regularity of roughly periodic input, helping users more accurately capture rhythmic pain experiences even when their manual input is imperfect.
  • the system can optionally extend or extrapolate the pattern beyond the specific duration of user input, generating a consistent rhythmic stimulus that matches the characteristics of the user-indicated pattern. This can be particularly useful for ongoing throbbing pain where manually tracing each oscillation might become tedious or impractical.
  • FIGS. 14A and 14B illustrate capture and recording of pain patterns characterized by varying temporal profiles in accordance with implementations of the present technology. These figures demonstrate how the system can distinguish between pain experiences that may reach similar intensity levels but differ in their rates of onset and decline.
  • the stimulus waveform comprises three distinct peaks, each exhibiting different temporal characteristics while reaching comparable maximum amplitudes.
  • the first peak demonstrates a rapid rise time and similarly quick decline, which can correspond to sudden, sharp pain that quickly subsides.
  • the second peak exhibits moderate rise and fall times, representing a more gradual onset and decline of pain.
  • the third peak shows the slowest temporal progression, with extended rise and fall times that can represent slowly building and gradually subsiding pain.
  • these temporal variations can be generated based on different characteristics in the user's input, such as the speed at which different portions of a path are traced or through separate controls for specifying timing characteristics.
  • FIG. 14B illustrates the corresponding recorded log, which can capture and distinguish these temporal characteristics.
  • the system can analyze and quantify various timing-related parameters for each pain episode, such as: rise time from baseline to peak intensity; fall time from peak intensity back to baseline; duration of peak intensity; total duration of the pain episode; rate of intensity change during onset and decline; and/or symmetry or asymmetry between rise and fall characteristics.
  • the system can process input in various ways to capture these temporal characteristics.
  • the control device can analyze the speed or acceleration of user input movements when tracing a path, interpreting faster movements as indicating more rapid changes in pain intensity.
  • the interface can provide separate mechanisms for specifying the timing characteristics of pain episodes, such as selectable presets for “rapid,” “moderate,” or “gradual” transitions, or controls for directly adjusting rise and fall times.
  • the temporal analysis of pain patterns can provide additional diagnostic or assessment value beyond simple intensity measurements. For example, different medical conditions or types of pain may be characterized by distinct temporal signatures.
  • the system can analyze these temporal patterns in conjunction with other pain characteristics to help identify or track specific pain conditions. Additionally or alternatively, the system can use temporal characteristics as additional parameters when aggregating and analyzing pain data across multiple assessment sessions or across different users.
  • the control device can generate control signals that preserve these temporal characteristics while ensuring safe and comfortable stimulus delivery. This can involve modulating multiple stimulus parameters simultaneously to achieve the desired rates of intensity change while remaining within established safety bounds.
  • the system can employ different mapping functions or scaling factors for temporal characteristics versus absolute intensity levels, allowing faithful reproduction of timing patterns even when intensity ranges require compression or scaling.
  • FIGS. 15A and 15B illustrate capture and recording of rhythmic pain patterns with varying peak intensities in accordance with implementations of the present technology. These figures demonstrate how the system can represent pain experiences that maintain consistent temporal characteristics while exhibiting changes in intensity from one cycle to the next.
  • the stimulus waveform shows a series of peaks occurring at regular intervals, with each successive peak reaching a higher maximum amplitude than the previous one. While the temporal characteristics (e.g., rise time, fall time, and period between peaks) remain relatively consistent across cycles, the peak amplitudes follow an increasing progression.
  • This pattern can represent various pain experiences, such as throbbing pain that intensifies over time while maintaining its rhythmic character.
  • the baseline intensity between peaks can also vary independently of the peak intensities, allowing representation of changes in both the baseline and peak pain levels.
  • FIG. 15B shows the corresponding recorded log of this variable-intensity rhythmic pattern.
  • the system can analyze and record various characteristics of the intensity progression, such as: rate of intensity increase across successive peaks; relationship between peak intensities and baseline levels; consistency of temporal characteristics despite intensity changes; overall trend in pain intensity (e.g., linear increase, exponential growth); and/or pattern regularity or irregularity in both time and intensity domains.
  • characteristics of the intensity progression such as: rate of intensity increase across successive peaks; relationship between peak intensities and baseline levels; consistency of temporal characteristics despite intensity changes; overall trend in pain intensity (e.g., linear increase, exponential growth); and/or pattern regularity or irregularity in both time and intensity domains.
  • the system can provide various mechanisms for capturing these combined temporal and intensity patterns.
  • the user interface can allow simultaneous control of rhythm and intensity, such as maintaining contact with the interface to establish timing while varying pressure or position to indicate intensity changes. Additionally or alternatively, the interface can provide tools for creating a basic rhythmic pattern and then modifying the intensity envelope that modulates that pattern.
  • the control device can process these inputs to generate appropriate control signals that preserve both the temporal consistency and the intensity variations of the indicated pattern. This can involve separate scaling or adjustment of timing and intensity parameters to ensure that both aspects of the pattern are accurately reproduced within the calibrated operating range of the system. In some implementations, the system can also analyze the relationship between timing and intensity characteristics to identify potentially significant patterns or correlations that might have diagnostic or therapeutic relevance.
  • the ability to capture intensity variations within regular temporal patterns can provide additional insight into the progression and characteristics of different pain conditions. For example, some medical conditions might be characterized by pain that intensifies in a predictable pattern while maintaining consistent timing between episodes.
  • the system can use these combined temporal -intensity patterns as part of a larger analysis framework for pain assessment and monitoring.
  • FIGS. 16A and 16B illustrate capture and recording of pain patterns characterized by irregular variations in both intensity and temporal characteristics in accordance with implementations of the present technology. These figures demonstrate how the system can represent more complex pain experiences that do not follow regular or predictable patterns.
  • I0114J As shown in FIG. 16 A, the stimulus waveform exhibits multiple peaks with varying characteristics across several dimensions. The peaks differ not only in their maximum amplitudes but also in their temporal profiles, spacing between peaks, and baseline levels. Some peaks demonstrate rapid onset followed by gradual decline, while others show different combinations of rise and fall characteristics. The intervals between successive peaks vary rather than following a regular rhythm, and the baseline intensity level can shift throughout the pattern.
  • This type of complex waveform can represent pain experiences that change unpredictably in both intensity and temporal characteristics, such as may be experienced with certain neurological conditions or complex regional pain patterns.
  • FIG. 16B depicts the corresponding recorded log of this irregular pain pattern.
  • the system can analyze multiple aspects of the pattern's irregularity, including: variation in peak intensities and their distribution; range of temporal characteristics (rise times, fall times, peak durations); statistical measures of pattern irregularity or randomness; identification of any recurring sub-patterns or motifs; relationships between temporal and intensity variations; and/or overall trend analysis despite local irregularities.
  • the system can employ various approaches to capture these complex patterns accurately.
  • the user interface can allow completely free-form input, enabling users to trace arbitrary paths that reflect the unpredictable nature of their pain experience. Additionally or alternatively, the interface can provide tools for combining and modifying multiple simpler patterns to build up more complex representations.
  • the control device can process these irregular patterns while maintaining safe and consistent stimulus delivery. This can involve real-time analysis and adjustment of the control signals to ensure that rapid or unexpected changes in the input pattern are translated into appropriate stimulus variations while remaining within established safety parameters.
  • the system can employ pattern recognition algorithms to identify underlying structure or regularities within apparently random variations, potentially revealing subtle patterns that might not be immediately apparent.
  • the ability to capture and analyze irregular pain patterns can provide valuable diagnostic and monitoring capabilities. Different medical conditions might be characterized by distinct types of irregularity in their pain patterns.
  • the system can analyze the specific nature of pattern irregularities to help identify or track particular conditions, or to monitor changes in pain characteristics over time. Additionally or alternatively, the system can use machine learning or other analytical techniques to identify correlations between irregular pain patterns and other physiological or environmental factors.
  • the pain assessment system 100 described above can be used in a variety of different contexts.
  • the system can be used at a doctor’s office by attaching the stimulation device 110 to the user and instructing the user on how to operate the system in order to capture a measurement data set.
  • the pain assessment system 100 may also be used at home or other location away from the doctor’s office by the user to take measurements in real time when the pain is being felt to more accurately capture the characteristics of that pain.
  • the pain assessment system 100 enables users to send their results to their provider in real time. Their provider can review their personal pain score and provide advice and treatment in real time from a telehealth setting, increasing access to care.
  • the pain assessment system 100 can be used in clinical studies of new medications or devices in order to assess efficacy of those medications or devices. Another context in which the pain assessment system 100 can be used is establishing psychological states and treatment efficacy (e.g., for evaluating and treating depression, anxiety, or other psychological disorders).
  • the data obtained can be used by machine learning algorithms to provide insights into care by looking at patterns associated with pain intensity, duration, treatment efficacy, environmental factors, biometrics (pulse, blood pressure, 02, etc.), and diagnoses. With additional data over time, such machine learning algorithms may also facilitate diagnosis or characterization of illnesses such as fibromyalgia or others that involve significant pain. As another example, machine learning algorithms can also generate recommendations of alternative treatment methods that may be more effective for different users, such as recommending physical therapy over opiates (or vice versa) depending on the user’s pain assessment data and/or other user data.
  • the system can be adapted for various other applications in healthcare and research settings.
  • the system can be used to assess and quantify other subjective experiences such as anxiety or depression.
  • the stimulus pattems can be calibrated to correspond with different psychological states, allowing users to match their internal experience with an external reference.
  • the technology can serve therapeutic purposes beyond assessment.
  • the stimulation patterns identified during pain assessment can be modified and reapplied as part of pain management protocols.
  • the system can deliver controlled counter-stimulation to help manage chronic pain conditions or provide biofeedback training.
  • the system can provide standardized assessment protocols for evaluating treatment efficacy.
  • the ability to capture detailed temporal and intensity characteristics of subjective experiences can enable more precise measurement of intervention outcomes.
  • the system can also facilitate double-blind studies by allowing automated stimulus delivery with predetermined patterns unknown to the researcher or participant.
  • the technology can be adapted for medical training and education, allowing students to experience and understand patient-reported symptoms.
  • the system can reproduce pain patterns described by previous patients, helping healthcare providers develop better understanding and empathy.
  • the system can be modified to help assess pain in animals through behavioral response matching.
  • Veterinarians can use the system to calibrate their understanding of animal pain responses and develop more effective treatment protocols.
  • the stimulation parameters can be adjusted to account for species-specific sensitivities and responses.
  • the present technology is illustrated, for example, according to various aspects described below. Various examples of aspects of the present technology are described as numbered Examples (1, 2, 3, etc.) for convenience. These are provided as examples and do not limit the present technology. It is noted that any of the dependent Examples may be combined in any combination, and placed into a respective independent Example. The other Examples can be presented in a similar manner.
  • a method for pain assessment comprising: coupling a stimulation device to a user’s body; receiving, via a user interface of a control device in communication with the stimulation device, a first user input indicating a time-varying stimulation intensity; generating, via the control device, a control signal based on the user input; transmitting the control signal from the control device to the stimulation device; and applying a time-varying pain stimulus to the user’s body via the stimulation device based at least in part on the control signal.
  • receiving the first user input comprises: displaying a two-dimensional input region on the user interface; detecting user contact with the input region; and tracking movement of the user contact within the input region, wherein a first dimension of the input region corresponds to pain intensity.
  • the method of any one of the Examples herein, wherein the first user input comprises a user tracing a path via the user contact within the input region, the path indicating the time-varying stimulation intensity.
  • receiving the first user input comprises tracking movement of the user contact over a predefined time period.
  • the time period is less than 60 seconds.
  • generating the control signal comprises scaling the time-varying stimulation intensity between the sensation threshold and the intensity threshold.
  • applying the timevarying pain stimulus to the user’s body comprises applying electric current to the user’s body via the stimulation device.
  • applying the timevarying pain stimulus comprises applying a stimulus that varies in amplitude over time.
  • applying the timevarying pain stimulus comprises applying a stimulus that varies in one or more of: frequency, waveform shape, or duty cycle over time.
  • receiving the first user input comprises: detecting a rhythmic input pattern having multiple peaks; determining temporal characteristics of the rhythmic input pattern; and generating the control signal to produce a stimulus pattern having corresponding temporal characteristics.
  • any one of the Examples herein further comprising: analyzing the time-varying stimulation intensity to identify one or more characteristics selected from: peak intensity values; baseline intensity values; rise times between intensity levels; fall times between intensity levels; rhythm frequency; pattern regularity; and storing the identified characteristics in association with the pain assessment.
  • a pain assessment system comprising: a stimulation device configured to be coupled to a user’s body; a control device configured to be in wireless communication with the stimulation device; and data storage having instructions stored thereon that, when executed by one or more processors, cause the pain assessment system to perform operations comprising: the method of any one of the Examples herein.
  • One or more tangible, non-transitory computer-readable media storing instructions that, when executed by one or more processors of a pain assessment system, cause the pain assessment system to perform operations comprising: a method according to any one of the Examples herein.

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  • Medicinal Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

Un système d'évaluation de douleur comprend un dispositif à porter sur soi qui délivre un stimulus à un utilisateur et un dispositif de commande tel qu'un téléphone intelligent qui fournit un signal de commande au dispositif à porter sur soi pour appliquer différents stimuli tels que commandés par l'utilisateur. L'utilisateur peut régler le stimulus délivré jusqu'à ce que celui-ci soit proportionnel à la douleur native que l'utilisateur subit ailleurs dans son corps. Ce stimulus délivré peut ensuite être journalisé sous la forme d'une valeur actuelle proportionnelle à la douleur et utilisé pour des évaluations comparatives et analyses futures.
PCT/US2024/053694 2023-10-30 2024-10-30 Systèmes et méthodes d'évaluation de la douleur Pending WO2025096635A1 (fr)

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