WO2021179040A1 - Système de gestion de la santé cardiovasculaire - Google Patents

Système de gestion de la santé cardiovasculaire Download PDF

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Publication number
WO2021179040A1
WO2021179040A1 PCT/AU2021/050208 AU2021050208W WO2021179040A1 WO 2021179040 A1 WO2021179040 A1 WO 2021179040A1 AU 2021050208 W AU2021050208 W AU 2021050208W WO 2021179040 A1 WO2021179040 A1 WO 2021179040A1
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WIPO (PCT)
Prior art keywords
wearable device
patient
sensors
nano
cardiovascular
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PCT/AU2021/050208
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English (en)
Inventor
Peter BARLIS
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Vascutech Pty Ltd
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Publication date
Priority claimed from AU2020900730A external-priority patent/AU2020900730A0/en
Application filed by Vascutech Pty Ltd filed Critical Vascutech Pty Ltd
Publication of WO2021179040A1 publication Critical patent/WO2021179040A1/fr

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    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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Definitions

  • the present invention relates to a system and process for managing the cardiovascular health of a patient by monitoring one or more health indicators of the patient via a wearable device.
  • Cardiovascular disease is a leading cause of illness and mortality in modern society. Accordingly, there is an increased focus on assessing the health of individuals to determine whether an individual has, or is likely to develop, a cardiovascular condition, and on providing appropriate treatment to the individual.
  • cardiovascular health management This process is referred to in the general sense as “cardiovascular health management”, and is often performed by clinicians (such as physicians or cardiologists) on a patient (i.e. for the purpose of diagnosing particular cardiovascular conditions, such as symptomatic heart failure).
  • clinicians such as physicians or cardiologists
  • cardiovascular conditions such as symptomatic heart failure
  • cardiovascular health management strategy is primarily dependent on the ability to accurately infer the existence or absence of particular cardiovascular conditions by the measurement of one or more health indicators from the patient, and the subsequent analysis of these indicators.
  • health indicators include measurements of particular physiological parameters of the patient, such as blood pressure, tissue volume and electrolyte concentrations. For example, an increase in the circumference of a patient's leg, as measured by the tissue volume in the region, may provide insight into the swelling of the leg, which may be interpreted as an indicator of heart failure.
  • the clinician may choose to administer therapeutic treatment (i.e., to prevent the occurrence of the condition, or to treat the effects of the condition in the case that the patient is already afflicted).
  • therapeutic treatment i.e., to prevent the occurrence of the condition, or to treat the effects of the condition in the case that the patient is already afflicted.
  • Systems for automated cardiovascular health management have been proposed involving a data collection device that is configured to obtain health indicator data from the patient, and transmit the collected data to an analysis component.
  • the collection device is typically a sensor that is implanted sub-dermally at a region of interest on the patient's body.
  • a disadvantage of these invasive approaches to cardiovascular health management is a lack of flexibility in the collection device configuration (i.e., since altering the position of the sensor will often require removal and re-implantation), and an inconvenience to the patient (i.e., due to the surgical requirements of implantation).
  • non-invasive cardiovascular health management systems have been proposed in which health indicator data is generated by a wearable collection device that is fitted to the patient. Characteristics of such wearable devices typically depend on the physiological parameters that are intended to be measured, and the body portion from which the measurements are intended to be obtained from. For example, existing systems have utilised wearable devices in the form of bands, patches, and sleeves which act to fix one or more sensing devices, such as ultrasonic, acoustic, optical, and/or electrical sensors, in direct, or conformal, contact with the skin of the patient in the calf region (i.e., in the area between the ankle and knee).
  • sensing devices such as ultrasonic, acoustic, optical, and/or electrical sensors
  • Wearable device based approaches to cardiovascular health management allow physiological parameter measurements to be performed from surface-based sensing. That is, the sensors do not require invasive implantation, which increases the flexibility of their use, the ease and cost efficiency of clinical evaluation, and the satisfaction of the patient.
  • the effectiveness of non-invasive automated cardiovascular health management systems is dependent on the ability of the wearable device, and its integrated sensors, to facilitate effective physiological parameter monitoring when fitted to the patient. For example, existing systems may involve a single measurement index or self diagnosability based on swelling dimension.
  • a cardiovascular health management process executed by at least one processor of a cardiovascular health management device, including:
  • cardiovascular health monitoring data representing measured values of one or more physiological parameters of the patient, where the measured values are obtained from a plurality of nano-sensors of the wearable device, the nano-sensors being arranged with a relative spacing across a surface of the wearable device, such that the nano-sensors are dispersed across a corresponding surface of the body portion when the wearable device is fitted to the body portion;
  • a cardiovascular health management system including:
  • a plurality of nano-sensors arranged with a relative spacing across a surface of the wearable device, such that the nano-sensors to are dispersed across a corresponding surface of a body portion of a patient when the wearable device is fitted to the body portion, where each of the nano-sensors is configured to measure values of one or more physiological parameters of the patient;
  • a microcontroller configured to communicate with one or more of the nano-sensors
  • a cardiovascular health management device connected to the wearable device via the microcontroller, and including:
  • a memory coupled to the at least one computer processor and storing instructions that, when executed by the at least one computer processor, cause the at least one computer processor to execute any of the cardiovascular health management processes described herein.
  • a wearable device for assessing the cardiovascular health of a patient including:
  • a base material adapted to be fitted to a body portion of the patient, the base material having an engagement surface for engaging with the skin of the body portion when the wearable device is fitted;
  • each of the nano-sensors being configured to measure values of one or more physiological parameters indicative of the cardiovascular health of the patient;
  • FIG. 1 is a block diagram of a cardiovascular health management system in accordance with some embodiments of the present invention.
  • Figure 2 is a block diagram of a computing device of the cardiovascular health management system
  • Figure 3a is a schematic diagram of a wearable device of an existing health monitoring system
  • Figure 3b is a schematic diagram of a first exemplary wearable device in accordance with some embodiments of the cardiovascular health management system of Figure 1
  • Figure 3c is a schematic diagram of a second exemplary wearable device in accordance with some embodiments of the cardiovascular health management system of Figure 1
  • Figure 4 is a flow diagram of a process for performing cardiovascular health management of a patient in accordance with some embodiments of the present invention
  • FIG. 5 is a flow diagram of a cardiovascular health monitoring data processing step of the cardiovascular health management process of Figure 4.
  • Figure 6 is a flow diagram of a cardiovascular condition determination process of the cardiovascular health management process of Figure 4.
  • Figure 7 is a flow diagram of a therapeutic treatment process of the cardiovascular health management process of Figure 4.
  • the inventors have identified some drawbacks with existing cardiovascular health management systems that utilise wearable devices to obtain measurements of particular health indicators of a patient.
  • These existing systems typically use sensors to produce data representing a measurement of the indicator (e.g., a physiological characteristic of the patient) from signals directed on to the patient at a body portion.
  • a measurement of the indicator e.g., a physiological characteristic of the patient
  • electrode based systems apply surface electrodes to the body portion to measure skin potentials and detect a change in an underlying physiological parameter (e.g., blood electrolyte concentration).
  • Wearable devices of existing systems often house the sensor within a casing, or other rigid protective member, forming a sensor component that is integrated into the device.
  • the sensor component is often large and protrudes from the surface of the wearable device. Consequently, each individual sensor, or sensor component, occupies a significant area on the surface of the wearable device.
  • it is desirable for the wearable device to have a compact and low profile design i.e., to allow easy fitting to the patient's body and to improve adherence). Consequently, in most conventional health management systems the number of sensors integrated within the wearable device is small.
  • conventional sensors are adaptable to measure only a particular subset of the physiological parameters of interest (i.e., an ultrasonic sensor may detect blood flow, but an electrode is required to measure electrical activity and measure electrolyte concentrations).
  • an ultrasonic sensor may detect blood flow, but an electrode is required to measure electrical activity and measure electrolyte concentrations.
  • a particular physiological parameter of interest is typically measured by a single sensor, or a very small number of isolated sensors, of the wearable device.
  • Sundaram and Flarikrishnan “Devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion” (WO 2019075185 Al) describes a wearable sensor system that is configured to adhere to a body portion of an individual, and where the system has a coupled sensor module including an array of conventional type sensors each configured to monitor particular parameters of interest (e.g., a circumference sensor, orientation sensor, motion sensor, temperature sensor, image sensor, cardiovascular sensor, and magnetometer sensor).
  • the sensor system is in the form of a wearable band that is preferably located between the ankle and knee of the patient.
  • existing systems are configured to perform parameter measurement at discrete and localised points on a surface of the body portion.
  • the sensors are designed to have a substantial spatial separation on the surface of the body portion, and the system processes the resulting measurements independently (i.e., by considering the change in measured values over time for each sensor).
  • Reid et al. "Health Monitoring and Management System” (US 20090234262 Al) describes a health monitoring and management device that uses one or more sensors that are adapted to detect changes in one or more health indicators, and which transmits the collected data to an "interventional element".
  • the sensors are substantially spaced apart in the described embodiments, where the spacing is for the purpose of allowing the sensors to be individually adapted to measure distinct parameters.
  • the physiological parameter value measurements generated by the wearable device are sensitive to variations in the position of the device on the body of the patient. Furthermore, due to the size of conventional sensors, and sensor components, they are often exposed to physical forces (e.g., from a jolt or knock) when fitted to a patient. As a result, the wearable device, and consequently the sensors, often experience deviations in their position on the patient's body from their initial location on fitting (i.e., due to the device being moved, accidentally or otherwise, while being worn by the patient). For example, incidental contact between the wearable device and a foreign object may occur if the patient brushes past the object when wearing the device (e.g., from the component snaring or catching on an external foreign object). Furthermore, there is also a reduced likelihood that the patient will adhere to a corresponding cardiovascular management plan which requires the use of such a device (i.e., since large protruding sensors are likely to cause discomfort or irritation to the patient's body when fitted).
  • existing cardiovascular health management systems determine the presence of cardiovascular conditions by detecting clinically significant changes occurring in the measured parameters over time.
  • the independent measurement of a particular physiological parameter over time at discrete and isolated locations on the body portion may be ineffective to detect particular symptoms of cardiovascular conditions.
  • swelling in the calf region i.e., detected as an increase in tissue while and/or blood flow in the region
  • the detection of this condition by an existing cardiovascular management system requires the swelling to occur at a point in the calf region at which a sensor of the wearable device is capable of obtaining the parameter measurements (which is typically at, or near to, the location of the sensor).
  • the swelling may sometimes present as a progression over time and spatial location in the calf region, in which tissue volume is increased initially in a lower region of the calf with the upper region being substantially unaffected until a significant amount of time has passed.
  • a wearable device such as for example a band, which is fitted to the upper calf region (e.g., below the knee) this could delay, or prevent, the detection of an imminent cardiovascular condition (e.g., symptomatic heart failure) leading to catastrophic consequences for the patient.
  • an imminent cardiovascular condition e.g., symptomatic heart failure
  • the limitation of obtaining physiological parameter values from a relatively few isolated points across locations over the surface of the body portion is a risk of: a failure to diagnose the condition (i.e. if the change in the parameter values are not detectable at the isolated sensor location), or a delayed diagnosis of the condition, leading to adverse clinical consequences; and a false positive diagnosis of a condition (e.g. where a change in a parameter due to a non-cardiac condition occurs locally on the body portion, but where the localised nature of the change is undetectable).
  • tissue volume may be increased at a point in the calf region of the patient's lower leg due to the patient sustaining a blunt force trauma (i.e., resulting in bruising or swelling).
  • a conventional management system which processes sensor measurements independently may be unable to distinguish this from increases in tissue volume that are associated with imminent heart failure (i.e., where the swelling extends circumferentially around the calf).
  • existing cardiovascular health management systems are primarily concerned with the detection of cardiovascular conditions by the monitoring of health data.
  • these systems can be configured to deliver a health intervention to the patient, these systems utilise additional elements in the wearable device.
  • the interventional element is an "adjustable compressive pressure stocking" that can be operated in response to the health indicator data, such as to increase "compressive pressure by a certain amount to enhance blood flow".
  • the system in Reid et al. relies on conventional sensors (e.g., of an electrical, mechanical, ultrasonic, acoustic, optical, or tactile nature) to monitor physiological parameter changes, and has a treatment capability that is limited to the application of compressive pressure to the body portion by a component that is separate to the sensors.
  • a means of therapeutic treatment involving the administration of agents, or the delivery of a particular medicament may be more beneficial.
  • therapeutic treatment procedures are performed by the clinician, or by an external cardiovascular treatment system configured to obtain condition data from the monitoring and detection processes of the management system.
  • the efficiency and effectiveness of cardiovascular health could be improved by a system which integrates the cardiovascular assessment (i.e., to determine the likelihood of a cardiovascular condition through the monitoring of changes in physiological parameters obtained from the body portion) with treatment involving the targeted delivery of medicament(s) to control or eliminate the determined conditions (i.e., via the automated administration and controlled release of therapeutic agents to the patient).
  • the described embodiments of the present invention include a system for cardiovascular health management, including a cardiovascular health management device with at least one processor configured to execute a cardiovascular health management process, which involves receiving, from a wearable device fitted to a body portion of a patient, cardiovascular health monitoring data representing measured values of one or more physiological parameters of the patient.
  • a body portion is an appropriately selected part of the patient's body (e.g., the lower leg) from which measurements of particular physiological parameters can be obtained, where the parameters are indicative of, or at least associated with, the health of the cardiovascular system (e.g., the heart and arteries) of an individual.
  • the body portion may be a lower leg, upper arm, wrist, head, abdomen, or chest region of the patient.
  • other parts of the patient's body may be designated as the body portion depending on the physiological parameters of interest and the corresponding requirements for measuring these parameters.
  • the presence of appropriate tissue, arteries and/or veins within a particular portion of the body may result in a desire to fit a wearable device (such as, for example, an under garment, wrist band, hat/cap, sock, etc.) to that portion for the purpose of cardiovascular health management.
  • a wearable device such as, for example, an under garment, wrist band, hat/cap, sock, etc.
  • the body portion may include a specific region of interest from which it is particularly desired to obtain physiological measurements from.
  • the lower leg body portion has a region of interest along its axial length over the calf muscle (referred to herein as the "calf region").
  • Selection of the region of interest may be subject to several factors. Large regions permit the deployment of a wearable device with a greater number of sensors (as described below). However, this requires an area where artefacts are limited and which facilitates the wear of the device by the patient (e.g., where the device can be designed to be comfortable and relatively fashionable when fitted to cover the region).
  • the application of sensors to multiple regions may allow greater accuracy in the detection of the relevant parameters, particularly electrocardiography.
  • the wearable device is a garment in the form of a sleeve or sock that is designed for fitting to the calf region of the patient.
  • the wearable garment includes a base material that is in the form of a conventional wearable material, such as for example cloth, polyester, linen or a combination thereof.
  • the garment has at least one interior surface configured to be in contact with the skin of the patient when fitted to the patient's body portion (i.e., the lower leg including their calf region).
  • the plurality of sensors are integrated within the garment such that each sensor achieves direct and conformal contact with the skin of the patient in the calf region in the fitted state.
  • wearable device refers to the garment that results from any and all individual pieces of the base material which collectively operate together to achieve the functions described herein.
  • the measured values are obtained from a plurality of nano-sensors of the wearable device.
  • the use of nano-sensors reduces the footprint of each sensing device within the garment, and allows for a significantly larger number of sensors to be integrated into the wearable device compared to the use of conventional large-scale sensors.
  • a large number of sensors are integrated into the garment with a relative spacing across an interior surface of the garment (referred to as an "engagement surface" herein). When the garment is fitted to the patient, the engagement surface is placed in direct conformal contact with a corresponding surface of the body portion (i.e., to cause the nano-sensors to be dispersed across the patient's skin surface).
  • the number and positioning of the nano-sensors on the engagement surface creates a high density sensor "web” or “constellation” over that surface. Consequently, when the garment is fitted to the body portion the nano-sensors are dispersed across a corresponding surface of the body portion.
  • the high density nano-sensor constellation extends substantially over a region of interest of the body portion when fitted.
  • the sensors when fitted to the lower leg the sensors form a high density web on the engagement surface which spans, at least, the patient's calf muscle region. This allows physiological parameter values to be measured from the patient's skin over the entire calf region, rather than from single isolated points on, or near to, the calf muscle.
  • the nano-sensor constellation extends circumferentially around the body portion when the garment is fitted.
  • the fitted sleeve is secured such that the nano-sensors contact the patient's skin in all directions over around the surface of the lower leg (i.e. including the calf and shin regions).
  • the nano-sensor constellation is an ordered arrangement of nano-sensors. For example, one such arrangement involves a helical network of continuously linked sensors, as existing on the engagement surface when the wearable device is fitted to the body portion.
  • the helical network can form a continuous sensor chain that wraps around the patient's calf region, extending from a first point located just below the knee to a second point located at the ankle (or beyond).
  • the use of a high density constellation of nano-sensors allows the measurement of physiological parameter values is advantageous in that it permits the wearable device to have a lower and more streamlined profile.
  • the ordered arrangement of the nano-sensors as a helical network permits the constellation to be utilised in a wearable device of many different forms (e.g., different types of garments, such as socks, shorts, under garments, wrist bands, hats, etc.) increasing the applicability and ease of use of the system.
  • a high density nano-sensor constellation is formed with an average nano-sensor density of at least 10 sensors/cm2 relative to the area of the engagement surface.
  • the use of a high density nano-sensor constellation improves the ability of the system to detect physiological parameter changes, and the ability to delivery therapeutic agents to treat determined cardiovascular conditions (as described herein below).
  • the use of nano-sensors overcomes the drawbacks of conventional electrodes including the discomfort experienced by the patient on displacement of the electrode, and potential irritation to the skin in the region of application.
  • the nanosensors described herein are non-invasive, do not require the use of conducting gels, and are able to produce measured parameter values that are more robust to signal noise while using less power.
  • the nano-sensors use dry electrode sensor techniques for potential quantification.
  • the nano-sensors are nano-tube electrode type sensors, including a nano-tube structure that is fabricated from carbon, or a similar light and rigid material.
  • Each nano-sensor can be configured to measure one or more physiological parameters, including, but not limited to, changes in acceleration, velocity, directions, ionic current, electrical activity, continuous limb dimensions, blood flow evaluation, heart rate, and blood pressure.
  • the nano-sensors are connected to a microcontroller of the wearable device.
  • the microcontroller is an integrated circuit that is embedded into, or fastened onto, the base material of the garment.
  • the microcontroller includes a processor for receiving signals from, and transmitting signals to, one or more of the nano-sensors.
  • the microcontroller is configurable to control the operation of each nano-sensor individually, or as part of one or more predetermined sensor groups. For example, for a given nano-sensor the microcontroller may determine: the physiological parameter measured; and the frequency of the sensed data measurements.
  • the microcontroller may also be configured to store sensor location data indicating the relative spatial location of each nano-sensor according to an intended, or expected, geometry of the garment in the fitted state.
  • the cardiovascular health management system is configured to perform therapeutic treatment of one or more cardiovascular conditions of the patient.
  • the therapeutic treatment involves the dynamic delivery of an agent or medicament to the patient via the wearable device.
  • each nano-sensor is configured to release a therapeutic agent to the body portion of the patient at the location of the sensor.
  • the nano-sensors can be coated by biopolymers that have high permeability and adhesion, such as to achieve the controlled release of a contained therapeutic agent (i.e. a medicament) topically as a response to the physiological changes, and/or determined cardiovascular conditions, detected by the system.
  • the release of the therapeutic agent by a nano-sensor is controlled by the microcontroller via the transmission of therapeutic delivery signals to the nano-sensor.
  • nano-sensors as described herein, to measure physiological parameter values from a body portion, and to perform controlled therapeutic treatment to cardiac conditions via the nano-sensors, has several significant advantages over compared to non-invasive health management systems that utilise conventional large-scale sensors.
  • the small scale and integrated nature of the nano-tube based electrodes allows for the recording of data from a high density nano-sensor constellation. Configuring the constellation to extend across a large area on the surface of the body portion (including a particular region of interest), or to have an ordered arrangement than spans this surface (such as a helical network configuration), provides the ability to better evaluate pathophysiological changes (i.e., by consideration of relative sensor-to-sensor changes) and thereby promotes the earlier detection of cardiovascular disease.
  • Carbon nanotube- based microelectrode sensor systems provide an improved sensing resolution compared to conventional electrode sensing systems, and in particular allow efflux detection of endogenous biomarkers.
  • carbon nanotube sensors can be configured to detect changes in physiological fluids which may result from the presence of elevated serum biomarkers and proteins (e.g. sodium, potassium, magnesium, B-natriuretic peptide) concentrations.
  • the monitoring data generated by the wearable device is processed by the health management device to generate cardiovascular condition data indicating whether the patient has, or is at risk of having, a cardiovascular condition.
  • the system can be configured to determine the cardiovascular condition(s) that are relevant to the patient based on an analysis of the cardiovascular health data (as obtained from the garment) in conjunction with contextual data specific to the patient.
  • the contextual data may include, for example, data representing health information of the patient (such as details of pre-existing health conditions or diseases, medications presently being taken, etc.) In some embodiments, this information may be utilised by the system to determine an appropriate dosing of medication, and/or to assess the effectiveness of the medication as delivered to the patient.
  • the presented cardiovascular health management system can determine the likelihood of a cardiovascular condition affecting the patient based on a consideration of the changes that are observed to occur for physiological parameters both over time, and over spatial locations over the body portion. For example, a patient may experience an increase in tissue volume over time at a particular location (i.e. as measured by particular sensors) in their calf area of the lower leg.
  • the system described herein is advantageous in that the use of a high density nano-sensor constellation allows the wearable device to measure the increase in tissue volume substantially across an entire region of interest, such as over the calf muscle, which may lead to the earlier detection of an underlying cardiac condition.
  • the system can obtain additional insight into potential cardiac conditions affecting the patient.
  • the parameter values measured by nanosensors arranged circumferentially around the calf at a fixed point, or in a helical network of continuously linked sensors, along the length of the garment may reveal a localisation of the swelling to the shin region. This may be used to rule out a cardiac condition determination (i.e., to prevent a false-positive diagnosis).
  • the cardiovascular health monitoring data, and the corresponding cardiovascular condition data, of a patient can be displayed to a user of the health management system (i.e., a clinician) in the form of a cardiovascular evaluation for the patient.
  • the cardiovascular evaluation can include one or more of: a graphical visualisation, and/or textual representation, of the health monitoring data including absolute or relative values of measured physiological parameters; an indication of one or more determined cardiovascular conditions and corresponding likelihoods that the condition affects the patient; and one or more treatment options corresponding to the determined cardiovascular conditions.
  • a graphical visualisation of the health monitoring data may include 3D images of values for one or more parameters at sensor locations on the surface of the body portion at particular time instants.
  • the cardiovascular evaluation is presented to the user via a user interface of the health management device, and in the form of an electronic report document (e.g., in a PDF, Word, or other format).
  • the described system and process can be used in real-time at the point-of-care to provide clinicians with a cardiac disease diagnosis and treatment solution.
  • Cardiovascular Health Management System Figure 1 illustrates an embodiment of a cardiovascular health management system 100, including: a wearable device 102; and a health management device 106.
  • the wearable device 102 is configured to generate cardiovascular health monitoring data representing measured values of one or more physiological parameters of a patient 101a to which the device 102 is fitted.
  • Hea Ith management device 106 is configured to receive the cardiovascular health monitoring data generated by the wearable device 102, and to process the received data to provide an indication of whether the patient 101a has, or is at risk of having, a cardiovascular condition (e.g., heart failure).
  • Health management device 106 is operated by a clinician 101b, such as a physician, cardiologist, or other health care professional.
  • Wearable device 102 is fitted to a body portion of the patient 101a.
  • the body portion is a lower leg part of the patient having a region of interest including the calf muscle (referred to herein as the "calf region").
  • the body portion e.g. a part of the patient's upper body, such as their arm or wrist.
  • the form, size and composition of the wearable device 102 may vary according to the body portion from which physiological parameter measurements are to be obtained from the patient 101a.
  • the wearable device 102 is a garment comprised of at least one layer of a stretchable base material allowing the garment to be fitted around the lower leg of the patient 101a at the calf region.
  • the garment is a sleeve or sock type garment that is rolled onto the calf region via the patient's foot.
  • the garment may be comprised of a cuff or sheet of material that is wrapped around the body portion and secured at opposing ends (e.g., by a Velcro strip or similar).
  • the garment may be a patch that is fixed to the surface of the patient's skin at the body portion (e.g., by taping, an adhesive, and/or similar means).
  • the wearable device 102 includes a plurality of nano-sensor components 1001-100N configured to measured values of one or more physiological parameters of the patient 101a from the body portion.
  • the device 102 includes a microcontroller 101 configured to receive sensing data from, and transmit instruction data to, one or more of the sensors 1001-100N.
  • the microcontroller 101 is configured to exchange data with each sensor 1001-100N individually.
  • some, or all, of the sensors 1001-100N may be arranged into sensor groups with the microcontroller 101 being configured to exchange data with a controller sensor of each group.
  • the health management device 106 is configured to communicate with the wearable device 102 wirelessly via a short-range communication protocol such as Bluetooth, Wi-Fi, or ZigBee.
  • a short-range communication protocol such as Bluetooth, Wi-Fi, or ZigBee.
  • the wireless exchange of data between the health management device 106 and the wearable device 102 allows the patient 101a increased freedom of movement within a vicinity of the health management device 106. This improves the adherence of a patient to a cardiovascular monitoring and/or treatment plan performed by the system 100 compared to other systems which rely on a wired connection between the management and wearable devices.
  • Health management device 106 includes a controller 112 configured to receive cardiovascular health monitoring data from the wearable device 102 and to facilitate the processing of the received cardiovascular health monitoring data by invoking one or more of a monitoring component 110, a condition determination component 120 and a treatment component 130.
  • Monitoring component 110 includes at least one data buffer 114 configured to store measured (i.e. sensed) parameter values of the health monitoring data, as generated in real-time by the device 102.
  • Analyser module 116 of the monitoring component 110 is invoked by the controller 112 to perform temporal and spatial analysis on the measured parameter data, as described herein below.
  • Condition determination component 120 is configured to receive temporal and spatial analysis data from the monitoring component 110 and to generate corresponding generate cardiovascular condition data indicating potential cardiovascular conditions relevant to the patient 101a. The relevance of particular conditions is indicated as a likelihood that the patient has the condition, and/or a risk that the patient will contract the condition within a specified time period.
  • Classification module 122 operates on representative physiological parameter feature values of the analysis data to assign a classification to each corresponding parameter for the patient (as described below).
  • the context data buffer 124 stores patient specific data, including an indication of particular illnesses, ailments or underlying medical conditions that may influence cardiovascular condition determination for patient 101a. The patient specific data is obtained by the controller 112 based on the processing of corresponding relevant patient data stored in a data store 108.
  • the data store 108 is a relational database including a plurality of data tables and a database management system (DBMS) configured to manage data records of each table.
  • Data store 108 is configured to maintain tables including a patient table, a condition table, a parameter table, treatment table, and a device table, which are configured to store records representing patient information (e.g.
  • physiological parameter information e.g., expected values or ranges for low, nominal and elevated states
  • cardiovascular condition and other disease related information e.g., condition data maps and/or thresholds relating physiological parameter states to positive occurrence of particular conditions and/or diseases; clinical training data for the supervised training of disease specific classifiers; etc.
  • treatment plan information e.g., for each cardiovascular condition: agents effective to treat and/or prevent the onset of the condition; dosage map data for determining effective dosage levels of particular agents as a function of particular patient and/or condition characteristics; and delivery data specifying agent delivery information, such as frequency of delivery, for the clinically effective treatment of the condition
  • device information e.g., for each device: a unique identifier of the device, a description of the device characteristics, an indication of the number of device nano-sensors and corresponding locations on the engagement surface of the device, etc
  • the described embodiments of the health management device 106 include a treatment component 130 that is configured process the cardiovascular condition data produced by the determination component 120 to generate therapeutic treatment data for controlling the treatment of patient 101a for one or more cardiovascular conditions.
  • Therapeutic assessment module 136 operates to generate an indication of an appropriate treatment plan given the determined cardiovascular condition(s) and patient specific context data (e.g., the clinical history of the patient).
  • a treatment plan includes the designation of one or more therapeutic agents or medicaments to be administered to the patient via the nano-sensors of the device 102.
  • Aspects of a treatment plan further include: a dosage of each agent to be administered; and a schedule for the delivery of the determined agent(s) to the patient.
  • Dosage regulator 134 and delivery module 132 are respectively configured to generate dosage and delivery scheduling data for the determined therapeutic treatment.
  • the health management device 106 further includes a user interface module 160 configured to accept user input from, and generate output for, a clinician 101b.
  • the user interface 160 generates user interface data representing graphical user interface (GUI) elements that, when rendered on a display device of the system 100, allow the clinician 101b to control the operation of the system 100, and to view clinical outcomes in relation to cardiac disease determination and treatment, as a result of the operation of the system 100 on the patient 101a.
  • GUI graphical user interface
  • the clinician 101b may interact with the GUI elements for the purpose of configuring the system 100 prior to the commencement of cardiovascular monitoring activities (as described below), and/or viewing cardiovascular evaluation information in relation to the diagnosis or treatment of cardiovascular disease, for the patient 101a.
  • the health management device 106 is implemented as one or more standard computer systems 200, such as, for example, an Intel Architecture computer, as shown in Figure 2, and the processes executed by the system 200 are implemented as programming instructions of one or more software modules 202 stored on non-volatile (e.g., hard disk or solid-state drive) storage 204 associated with the computer system.
  • non-volatile (e.g., hard disk or solid-state drive) storage 204 associated with the computer system.
  • non-volatile (e.g., hard disk or solid-state drive) storage 204 associated with the computer system e.g., hard disk or solid-state drive
  • FPGAs field programmable gate arrays
  • ASICs application-specific integrated circuits
  • the system 200 includes random access memory (RAM) 206, at least one processor 208, and external interfaces 210, 212, 214, all interconnected by a bus 216.
  • the external interfaces include universal serial bus (USB) interfaces 210, at least one of which is connected to a keyboard 218 and a pointing device such as a mouse 219, a network interface connector (NIC) 212 which connects the system 200 to a communications network 220, such as the Internet, and a display adapter 214, which is connected to a display device such as an LCD or LED panel display 222.
  • USB universal serial bus
  • NIC network interface connector
  • the system 200 also includes a number of standard software modules 226 to 230, including an operating system 224 such as Linux or Microsoft Windows, web server software 226 such as Apache, available at http://www.apache.org, scripting language support 228 such as PHP, available at http://www.php.net, or Microsoft ASP, and structured query language (SQL) support 230 such as MySQL, available from http://www.mysql.com, which allows data to be stored in and retrieved from an SQL database 232.
  • an operating system 224 such as Linux or Microsoft Windows
  • web server software 226 such as Apache, available at http://www.apache.org
  • scripting language support 228 such as PHP
  • PHP available at http://www.php.net
  • Microsoft ASP ASP
  • SQL structured query language
  • the system 106 operates as a standalone computing device in which the database 232 is a local database managed by the data store component 108.
  • the database 232 is implemented using SQL and is accessed by a database management system (DBMS) of the node.
  • DBMS database management system
  • the database 232 may be implemented on a separate computing device, or across multiple computing devices according to one or more techniques for the distributed processing and storage of data.
  • Figure 3a illustrates a wearable device 301 fitted to the lower leg body portion 300 of the patient 101a according to the existing health monitoring system of Sundaram and Harikrishnan, "Devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion" (WO2019/075185A1).
  • the device 301 includes a sensor module 320 containing multiple application specific sensors (e.g., a circumference sensor, orientation sensor, motion sensor, temperature sensor, image sensor, and cardiovascular sensor).
  • the sensors are conventional large-scale sensors, and are confined to an isolated point on the surface of the patient's lower leg when the stretchable component 310 is fitted securely around the leg.
  • Figures 3b and 3c illustrate exemplary wearable devices 102 of the presented cardiovascular health management system 100 in the form of a sleeve type garment that is adapted to be fitted securely to the lower leg body portion 300 of the patient 101a.
  • the sleeve 102 includes a stretchable layer of a base material 103 configured to support the plurality of nano-sensors 1001-100N.
  • the base material layer is adapted to be fitted to the body portion 300 of the patient by rolling the sleeve 102 onto the patient's body (e.g., upwards from the ankle to the knee for a lower leg portion).
  • the wearable device 102 may be a patch, in which the secure fitting of the device 102 to the body portion involves the application of an adhesive material (e.g., a surgical glue and/or tape) to one or more of the patient's body and the base material 103.
  • an adhesive material e.g., a surgical glue and/or tape
  • each of the nano-sensors is integrated into the sleeve 102 by embedding or weaving the sensor into the base material 103.
  • the sensors 1001-100N may be fixed onto the base material 103 via the use of an adhesive material.
  • the garment 102 has an interior surface, referred to as an engagement surface, that is pressed against the corresponding surface of the body portion (i.e. the patient's lower leg). The integration of the nano-sensors into the sleeve 102 results in the nano-sensors being arranged with a relative spacing across the engagement surface.
  • each nano-sensor 1001-100N includes a nano-tube electrode configured such that at least part the nanotube protrudes through the engagement surface to make direct contact with the skin of the body portion.
  • Figure 3b shows an embodiment in which the wearable sleeve 102, when fitted to the lower leg 300, extends along an axis 300A from a first axial point 302 adjacent to the ankle of the patient 101a, to a second axial point 304 adjacent to the knee of the patient 101a.
  • the large number of nano-sensors 1001-100N of sleeve 102 are arranged to form a high density sensor constellation that extends substantially over a region of interest (i.e., calf region 303) of the lower leg 300.
  • Figure 3c shows an embodiment in which the wearable sleeve 102 includes a nano-sensors constellation in which the ordered arrangement of nano-sensors form a helical network of continuously linked sensors.
  • a first subset of the nano-sensors extend over a front of the lower leg 300 (depicted as solid) while a second subset of the nano-sensors extend around the back of the leg 300 (depicted as hollow with dashed lines).
  • the nano-sensors are arranged with a minimum of 10 sensors per cm2 of engagement surface area.
  • the resulting high density sensor constellation extends circumferentially around the lower leg portion 300 between the first 302 and second 304 axial points (not shown in Figure 3b), such as to wrap around both the calf 303 and shin 305 regions. At a given axial point along the length of the lower leg portion 300, this allows physiological parameter measurements to be obtained from spatial locations around the entirety of the lower leg, as opposed to at a single point.
  • Figure 4 illustrates a process 400 performed by the system 100 to manage the cardiovascular health of a patient.
  • the process 400 is implemented by a cardiovascular health management software application 400 executing on the management device 106, as described herein above.
  • the management device 106 is configured by clinician 101b to perform cardiovascular health management operations.
  • configuration includes a selection of the patient 101a for which cardiovascular assessment and/or treatment is to be performed and a corresponding wearable device 102 that is to be fitted to the patient 101a.
  • the wearable device 102 is assigned to the patient 101a by the recording of the representative patient and wearable device identifier values in the data store 108.
  • the clinician 101b can specify the one or more physiological parameters that are to be monitored by the device 102, and subsequently utilised for cardiovascular disease determination and management. Particular physiological parameters may be selected from a list of available parameters which are displayed to the clinician 101b via the user interface 160. The list of available parameters is determined according to the wearable device 102 assigned to the patient 101a (e.g., based on the sensing capabilities of the nano-sensors embedded in that device). In some embodiments, the clinician 101b can configure the monitoring component 110 to determine the most appropriate and/or relevant physiological parameter values dynamically. Further embodiments of the health management device 106 allow for the extension of this functionality beyond cardiovascular disease assessment, to the detection and treatment of other diseases (e.g., neurological conditions, and endocrine conditions such as diabetes mellitus).
  • diseases e.g., neurological conditions, and endocrine conditions such as diabetes mellitus.
  • the physiological parameters are determined according to a disease management profile that is selected by the clinician 101b specifically for the patient 101a (e.g., after the clinician reviews the medical history of the patient).
  • one or more analysis control values can be specified in relation to the temporal and/or spatial analysis operations of the wearable device 102.
  • the clinician 101b may set the size of the time window used to produce the time representative parameter values (described below) from the received sensed parameter values (i.e. of the cardiovascular monitoring data).
  • the treatment functionality of one or more of the nano-sensors 1001-100N can be configured by the clinician 101b.
  • the clinician 101b may provide an indication of the particular one or more therapeutic agents which the sensors are capable of delivering to the patient at the body portion. This may be performed in conjunction with a physical configuration process to prepare the wearable device 102 for use (e.g., coating/filling the sensors with the particular agents prior to fitting onto the patient), and allows for the dynamic selection of treatment options to manage various types of diseases.
  • the health management device 106 receives cardiovascular monitoring data from the wearable device 102.
  • the cardiovascular monitoring data includes: sensed parameter data representing measured values of particular physiological parameters (as determined during configuration); and device specific data representing the properties of the wearable device 102.
  • the cardiovascular monitoring data is generated by microcontroller 101 and is transmitted wirelessly to the controller module 112 of the device 106.
  • the microcontroller 101 generates the device specific data, including at least an indication of the unique device identifier, and transmits the encapsulated sensed parameter data and device specific data as cardiovascular monitoring data in a predetermined form that is expected by controller 112.
  • the health management device 106 processes the received monitoring data and generates cardiovascular condition data at steps 406 and 408 respectively.
  • processing of the monitoring data by the controller 112 involves extraction of the encapsulated wearable device ID and sensed parameter value data (i.e., at steps 502 and 504) respectively.
  • the controller 112 is configured to match the wearable device ID of device 102 with the corresponding patient identifier of patient 101a via a lookup operation on data store 108.
  • Controller 112 is configured to store the extracted sensed parameter data into data buffer 114 for processing by monitoring component 110.
  • the monitoring component 110 includes an analyser module 116 that is configured to process the sensed parameter values to determine a relative change in one or more of the physiological parameters over a period of time, and/or spatially across the body portion (i.e. the surface of the lower leg).
  • the monitoring component performs one or more pre-processing operations on the sensed parameter data. Pre-processing may include filtering and/or normalisation of the feature values measured for particular parameters are mitigate the effect of erroneous measurements (e.g. to reject measured values of the systolic and diastolic pressure parameters exceeding 300 and 150 mmHg respectively).
  • the analyser 116 is configured to perform temporal and spatial analysis over an analysis time window of a predetermined size. As part of the pre-processing step 506, the analyser 116 collates parameter feature data from sampled time instants belonging to the same analysis window and generates averaged sensed parameter data S p for the window. For example, if the sensor sampling period is 500ms and the analysis window size is 10s, then S p is generated from the averaged values of 20 sampled sensor values. In some embodiments, other pre-processing operations may be performed on the sampled and/or time averaged parameter values (e.g., smoothing or filtering of the data).
  • the pre-processed parameter values are used to generate temporal and spatial representative data at steps 508 and 510 respectively.
  • the analyser 116 generates temporal and spatial representative data by comparing parameter values measured over one or more time intervals (i.e. across multiple analysis windows), and from different nano-sensors (i.e. across multiple locations on the surface of the body portion), respectively.
  • the analyser 116 is configured to generate differential feature vector measurements to represent relative differences in the measured values of a parameter.
  • a temporal analysis of the values obtained by a particular sensor N over particular analysis time instants may involve a consideration of the difference in measured values between each adjacent time window (e.g., N: ⁇ s ti - 3 ⁇ 4, ⁇ 3 ⁇ 4 S t! . ⁇ ) ⁇
  • the relative difference between the parameter p measured by various sensors, at a particular time analysis instant, i.e., (3 ⁇ 4 (l)- s/ (0), 3 ⁇ 4"(2)- 3 ⁇ 4"(l) ⁇
  • the absolute values of the (averaged) sensed parameter data are processed to form the representative feature vectors over time and space (i.e., either by using the parameter data values directly, or by applying normalisation over time and/or sensor location).
  • the controller 112 invokes the condition determination component 120 to generate cardiovascular condition data.
  • the generation of cardiovascular condition data is based on the temporal and/or spatial representative data which is received by the condition determination component at step 602.
  • condition context data is generated representing contextual factors that influence the likelihood of the patent 101a having a particular cardiovascular condition in view of the measured physiological parameter values.
  • Contextual factors may include, for example, one or more of: a physiological characteristic of the patient (e.g., their height or weight); and an aspect of the patient's clinical medical history (e.g., confirmation of the occurrence of scarring or abnormal tissue deposits in the cardiovascular system).
  • controller 112 queries the data store 108 to retrieve patient specific information relevant to the generation of the condition context data.
  • Condition component 120 stores the generated context data in context data buffer 124 for use in a condition assessment or classification process performed by classification module 122. Prior to commencing condition classification, the condition component 120 generates model data representing the relevant condition model information required to perform classification (i.e., at step 606).
  • the condition model data includes: an indication of the type of classification strategy to be used (e.g., thresholding or pattern matching); and model data for one or more candidate conditions according to the classification strategy.
  • the model data specifies threshold values for determining a state of representative parameter values.
  • a systolic pressure parameter may have "low” and “high” threshold values of [90] and [130] respectively for use in determining whether measured values of the parameter correspond to a "low", “nominal” or “elevated” state.
  • the model data specifies the parameters of the classifier.
  • the condition component 120 is configurable to utilise one or more pattern recognition methods to perform the classification, including statistical likelihood based classifiers (e.g., of a GMM/HMM), or discriminative classifiers (SVMs).
  • a corresponding classifier model is trained for each cardiovascular evaluation condition (i.e., each disease for which the system 100 assesses the patient 101a) using a priori data with validation labels obtained from a clinical setting.
  • the module 122 generates parameter specific classification data for parameter measurements obtained from the patient 101a.
  • the representative temporal and/or spatial feature data is used as input to the pattern recognition classifier (or to a comparator when thresholding is used), and the output is scored against a set of reference likelihoods (or threshold values) to determine the parameter state. For example, using thresholding representative values of systolic blood pressure may be classified as 'low' if they are below the low threshold of 90, 'elevated' if they are above the high threshold of 130, and 'nominal' if they are in between those values.
  • the classification module 122 processes the individual parameter classifications to generate condition determination data representing an indication of whether the patient 101a has, or is likely to contract, each cardiovascular evaluation condition.
  • the condition component 120 generates the condition determinations based on the condition context data. For example, if patient 101a has a clinical history of ischemic heart disease then 'elevated' classifications of their (systolic and/or diastolic) blood pressure parameters will have a greater positive influence on a determination that the patient 101a has the 'imminent heart failure' evaluation condition.
  • the management device 106 can be optionally configured to report the cardiovascular condition determination(s) of patient 101a to the clinician 101b.
  • the reporting of the condition determination(s) may occur via an audio and/or visual prompt delivered to the clinician 101b via the user interface 160.
  • the management device 106 is configurable to produce a condition evaluation report, in the form of an electronic text-based document (such as a PDF), which contains cardiovascular health summary information in respect of the parameter monitoring and the subsequent condition determination(s).
  • the report may indicate: average measured values for the parameters of tissue volume over time and systolic and/or diastolic blood pressure; a map of the measured tissue volume values over a spatial region of the lower leg (e.g., showing values measured extending around the circumference of the leg); parameter specific classifications indicating an increase from 'nominal' to 'elevated' of both the tissue volume and blood pressure values, where the increase occurs over a common analysis time period (e.g., over several hours); an indication of a clinical history of type I diabetes; and a subsequent determination that patient 101a is at a 'high' risk for the 'imminent heart failure' cardiovascular condition.
  • the health management device 106 generates therapeutic treatment data for controlling the automated therapeutic treatment of the cardiac conditions of patient 101a.
  • the therapeutic treatment data includes: treatment plan data; therapeutic delivery data; and transmission data.
  • therapeutic treatment component 130 receives cardiovascular condition data representing the determined conditions from the condition component 120 (via the controller 112).
  • the therapeutic assessment module 136 processes the cardiovascular condition data to generate treatment plan data representing a treatment plan for one or more of the cardiovascular conditions determined for patient 101a. Treatment of the cardiovascular condition(s) occurs via the nano-sensors 1001-100N of the wearable device 102.
  • the plan data specifies aspects of a cardiac disease treatment plan, including: an indication of at least one selected treatment agent; a delivery mode of each selected agent (e.g., an identifier of the sensor(s) through which the agent is to be delivered and an indication of the delivery mechanism of the sensor(s)); a dosage of each agent; and a schedule of delivery of each agent.
  • a delivery mode of each selected agent e.g., an identifier of the sensor(s) through which the agent is to be delivered and an indication of the delivery mechanism of the sensor(s)
  • a dosage of each agent e.g., a dosage of each agent.
  • the therapeutic assessment module 136 matches each determined cardiovascular condition to corresponding treatment data represented by records from the treatment table of data store 108 (as described above).
  • the assessment module 136 collates the corresponding treatment data and processes this data to determine a dosage of each agent, and a corresponding schedule on which the agent is to be provided to the patient 101a, as part of the plan.
  • the dosage of an agent is determine based on the properties of the cardiovascular condition and on the context data. For example, an agent that may otherwise be suitable to treat hypertension (e.g., beta blockers) may not be suitable if patient 101a has another, and possibly non-cardiac related, health condition (e.g., chronic asthma).
  • the treatment schedule includes delivery mode data specifying whether the dosage of each agent is to be delivered as a single treatment at a specified treatment time, or as a plurality of treatments over a treatment time interval.
  • the clinician 101b can specify one or more properties of the treatment schedule including the delivery mode, and the corresponding treatment times (e.g., a treatment start time, and a treatment end time in the case of periodic delivery over a pre-determined time interval), by submitting input to the system 100 via the user interface 160.
  • the therapeutic assessment module 136 invokes the treatment delivery module 132 to generate treatment delivery data representing instructions for the nanosensors 1001-100N to deliver the agent(s) to the patient 101a according to the determined treatment plan.
  • the delivery module 132 receives device information from the controller 112 including an indication of the number of nano-sensors of device 102, a type of each sensor and microcontroller 101, and corresponding locations of each sensor on the engagement surface of the device 102.
  • the delivery module 132 determines a series of control instructions that, when executed by the microcontroller 101, cause the sensors to release the therapeutic agent to the patient at the determined dosage, and according to the determined schedule (i.e., as specified by the treatment plan data).
  • the treatment delivery data generated by therapeutic assessment module 136 is transmitted to the microcontroller 101 via the device 112 (i.e., at step 708).
  • the microcontroller 101 processes the instructions to control the operation of one or more of the nano-sensors 1001-100N such that the patient 101a receives the agent through their skin surface at the body portion (i.e., via the sensors), and consequently experiences therapeutic treatment.
  • the treatment component 130 is executed continuously and in real-time by the controller 112 such that the treatment plan data generated by the therapeutic assessment module 136 is updated according to further cardiovascular monitoring data received from the wearable device 102 (i.e. at step 710).
  • the therapeutic assessment module 136 can be configured to process cardiovascular health monitoring data, and/or corresponding cardiovascular condition data, that is received as a consequence of patient 101a undergoing an existing therapeutic treatment administered by the system 100.
  • the treatment component 130 is configured to generate treatment adjustment data representing modifications or changes to an existing therapeutic treatment of the patient 101a based on the received monitoring data.
  • the treatment adjustment data includes changes to the treatment plan, such as to the dosage level of one or more therapeutic agents and/or to the delivery schedule for the agent(s) to the patient 101a.
  • the treatment adjustment data is processed by the treatment component to update corresponding therapeutic treatment data of the existing treatment. This allows the system 100 to administer a medicament (e.g. blood thinning agents) to a patient for the purpose of treating a determined cardiac disease of the patient, where the treatment is adjusted dynamically according to the effect of the delivered medicament on the patient.
  • a medicament e.g. blood thinning agents

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Abstract

Procédé de gestion de la santé cardiovasculaire exécuté par au moins un processeur d'un dispositif de gestion de la santé cardiovasculaire, consistant : à recevoir, depuis un dispositif à porter sur soi ajusté à une partie corporelle d'un patient, des données de surveillance de la santé cardiovasculaire représentant des valeurs mesurées d'un ou plusieurs paramètres physiologiques du patient, où les valeurs mesurées sont obtenues à partir d'une pluralité de nanocapteurs du dispositif à porter sur soi, les nanocapteurs étant disposés selon un espacement relatif à travers une surface du dispositif à porter sur soi, de sorte que les nanocapteurs sont dispersés à travers une surface correspondante de la partie corporelle lorsque le dispositif à porter sur soi est ajusté à la partie corporelle; et à traiter les données de surveillance de la santé cardiovasculaire pour générer des données d'état cardiovasculaire indiquant si le patient présente, ou présente un risque de développer, un état cardiovasculaire.
PCT/AU2021/050208 2020-03-10 2021-03-10 Système de gestion de la santé cardiovasculaire WO2021179040A1 (fr)

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WO2024019993A1 (fr) * 2022-07-20 2024-01-25 The Johns Hopkins University Systèmes et procédés de surveillance et de traitement de lésions dans une population de patients à l'aide d'un ou de plusieurs dispositifs portables

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US20180325407A1 (en) * 2017-05-02 2018-11-15 Nanowear Inc. Wearable congestive heart failure management system
WO2018209100A1 (fr) * 2017-05-10 2018-11-15 Northwestern University Dispositifs de type tissus fonctionnels comportant des capteurs intégrés
US20190132948A1 (en) * 2012-09-11 2019-05-02 L.I.F.E. Corporation S.A. Physiological monitoring garments

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US20130281795A1 (en) * 2012-04-18 2013-10-24 The Board Of Trustees Of The University Of Arkansas Wearable remote electrophysiological monitoring system
US20190132948A1 (en) * 2012-09-11 2019-05-02 L.I.F.E. Corporation S.A. Physiological monitoring garments
WO2017121434A1 (fr) * 2016-01-13 2017-07-20 Specialbandager.Dk A/S Dispositif et procédé pour fournir une mesure de la circonférence d'une partie du corps
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WO2024019993A1 (fr) * 2022-07-20 2024-01-25 The Johns Hopkins University Systèmes et procédés de surveillance et de traitement de lésions dans une population de patients à l'aide d'un ou de plusieurs dispositifs portables

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