Conceptio › Archive › Google Patents

Google Patentsopen access

Non-invasive wearable biomechanical and physiology monitor for injury … — George Mason Research Foundation, Inc. (US12011291B2)

George Mason Research Foundation, Inc. · Google Patents

Google Patents · Patents · License: Open Access

patent, google patents, intellectual property, US12011291B2, George Mason Research Foundation, Inc., Marissa A. Howard, en, 2024

ABSTRACT

Abstract

A wearable device measures, tracks, and monitors a wearer's physical physiological conditions during a rehabilitation period. Metrics such as temperature, patellar shifting, limb circumference, and acceleration may be collected. Changes in monitored conditions may be assessed for detecting medical abnormalities which require specialized attention, including for example embolisms or infections. A networked communication system and various user interfaces may be used for medical support personnel and patients alike to stay updated with the patient's rehabilitation progress and to make adjustments in a patient's individual rehabilitation program.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 16/169,295, filed Oct. 24, 2018. This application claims the benefit of U.S. provisional patent application No. 62/576,361, filed Oct. 24, 2017, the complete contents of which are herein incorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under grant number AR068436 awarded by the National Institutes of Health. The government has certain rights in the invention.

FIELD OF THE INVENTION

The invention generally relates to wearable technology usable to measure and monitor physiological and activity information about a user for applications such as post-injury or post-operative rehabilitation.

BACKGROUND

When surgical procedures are necessary to treat problems with the musculoskeletal system (e.g. ligament tears), a high percentage of patients do not have an optimal outcome. Instead they are functionally impaired by poor joint mobility and limited range of motion. For civilian or military personnel this causes a delay in a return to active duty and reduced duty performance. Such poor outcomes are often caused by failure to detect and treat injury complications, and by poor compliance during rehabilitation and post injury follow-up. Many complications can arise during rehabilitation such as but not limited to swelling, blood clots, and infection.

When active duty service members suffer a knee injury such as meniscal tears, ACL, PCL, MCL, LCL repairs, or patellar dislocations, the estimated time to return to full active duty following rehabilitation ranges from 9 to 24 weeks. Moreover, due to shortages of physical therapists or rehabilitation specialists in the military, those with the most severe knee injuries are preferentially treated. Therefore, soldiers with minor knee injuries are more likely to undergo an imperfect rehabilitation plan.

The physical demands and collision forces experienced by active duty military personnel in training and operational environments generates an increased risk of knee injuries. Currently, the incidence and prevalence of knee injuries in training and service are unnecessarily high, thus impairing readiness and increasing the rate of removal from active duty.

Joint replacement surgeries are expected to rise ten times their current rate. By 2030, it is estimated that 11 million individuals will require a joint replacement surgery. Current post-operative rehabilitation methods provide short intervals of guided care, after which the patient is expected to perform self-care exercises in order to heal. Current rehabilitation devices focus on enhancing support via immobilization or compression.

Even though patients are instructed to start physical therapy the day after their surgery, or following an injury, a physical therapist can only spend time with the patient for a limited number of hours per week. Low patient compliance to perform at-home rehabilitation exercises, as prescribed, has been a major hurdle facing orthopedic surgeons and physical therapists. A patient's unwillingness to perform prescribed exercises or, for Active Duty personnel, a desire to push too hard too fast increases the likelihood of risk of re-injury, muscle atrophy, joint stiffening, and life-threatening complications. Additionally, inadequate rehabilitation is shown to lead to increased risk of osteoarthritis (OA) development and reduced final range of motion (ROM) of the joint. Potentially fatal complications can arise in the off-time when the patient is not being seen by a treating physician or rehabilitation specialist. All of these issues are contributing factors to extended delay prior to return to the workforce for Active Duty personnel and Veterans after knee injury/surgery.

Current rehabilitative tools for surgeries such as total knee replacement are rigid braces which provide no diagnostic information and often force the patient to âcompensateâ to accommodate the constraints of the brace. Alternative rehab assistive devices are needed for improving the quality of rehab care and shortening the time for full rehabilitation of personnel with acute knee injuries, including strains, sprains, dislocations, and ligament tears, as well as for treating many other musculoskeletal injuries.

SUMMARY

An objective of some embodiments is to provide end users with a device or system of sensors and software that can collect meaningful physiological measurements for rehabilitative purposes.

Another objective of some embodiments is to reduce rehabilitation time and improve long term rehabilitation goals. These goals may be achieved with humans and with various other animals (e.g., horses, pigs, cows, cattle, dogs, cats, other ranch animals, other domestic animals, wild animals, etc.).

Another objective of some embodiments is to provide an alert system for users by which users are alerted to a risk or presence of a medical condition requiring attention, such as but not limited to an infection, embolism, and effusion, based on monitoring of a part of the body (e.g., a joint) during a rehabilitation or recovery period, for example.

Another objective is to monitor and instruct adjustment of wearable position with respect to a patient's patella and/or to monitor and detect patellar deviations. Some embodiments comprise one or more pressure sensors configured to detect pressure indicating the location, movement, and circumference of a patient's patella.

According to an aspect of the invention, some embodiments comprise a wearable device (i.e., âwearableâ) configured to monitor patient parameters such as but not limited to knee movement, heart rate, temperature, and muscle flexing. The device may be configured to perform or assist in performing a noninvasive method of monitoring joint rehabilitation by utilizing unique sensors and a learning algorithm.

Some exemplary embodiments may include one or more of (e.g., all of) the following sensors to monitor a patient: stretch sensors, accelerometer, gyroscope, temperature, and heart rate monitor. All of these sensors can provide information regarding patient movement, swelling, heart rate, infection, and force applied with each step.

According to another aspect, some embodiments provide the patient and/or doctor feedback on the patient's vitals and movements in order to prevent deep vein thrombosis (DVT) or infection and/or to ensure that the patient follows the recommended therapeutic exercises. In addition, embodiments may provide custom feedback for the patient to instruct him or her on exercises and knee usage. In some embodiments, a healthcare provider (e.g., doctor, physical therapist, surgeon, nurse, etc.) may aid the patient with device calibration after surgery (e.g., a total knee replacement (TKR)). An exemplary electronic application in communication with the wearable worn by the patient may provide the doctor with early warning notifications regarding potential complications as well as to provide recurring reports (e.g., daily, weekly, monthly, etc.) to track the patient's ability to achieve exercise goals set by or with the input of the healthcare provider.

According to an aspect of some embodiments, a patient joint monitoring system is provided which can detect swelling, infection, and/or blood clots. The system may track body movements to ensure proper joint use. The system may include additional sensors such as ultrasound or infrared to detect DVT.

Some embodiments may comprise non-invasive wearable devices which provide real-time physiological and clinical measurements with wireless embedded sensing technology with advantages of improving operational readiness and soldier deployability, or improving civilian rehabilitation following injury or surgery measurable in: a) reduced duration of rehabilitation and rapid return to duty or employment, b) absence or substantial reduction in the incidence of impaired physiologic function, and c) reduction in the risk of unattended medical complications occurring during rehabilitation regimens and outside clinic visit time. Rehabilitation goals may include but are not limited to full range of motion, lack of pain, strength and stability substantially equivalent to pre injury state of the joint.

Some embodiments involve non-invasively and wirelessly measuring and monitoring a set of physiologic and health parameters associated with both joint function and joint health, while continually learning and updating the system to the individual.

Exemplary systems will improve patient outcome in terms of joint mobility and range of motion while reducing recovery time following a knee injury or knee surgery.

A wearable's microelectronics may include sensors, data storage, battery, and wireless communication antennas (e.g., WiFi and/or Bluetooth).

An exemplary device is configured to communicate in real-time with the patient and rehabilitation specialist. A custom mobile app for Android and iOS operating systems graphically displays the parameters requested by their rehab specialist.

Exemplary embodiments may provide simultaneous measuring and tracking, monitoring of joint heath, and learning of a patient's individual knee kinematics. Information may be transmitted in real-time, wirelessly, and longitudinally, to the treating physician and to the phone application of the patient, and displayed in a graphic format that is easy to understand by the patient and medical personnel.

Some devices may include a patella position tracking element. In addition to the superior and inferior motion of the patella, the patella also tracks lateral-medial during tibiofemoral extension to flexion. The type of measurements for the patella proposed for the wearable device, in all four directions, is an innovative approach to identify situations in which excessive medial or lateral motion occurs during flexion, since the patella remains relatively centered on the trochlea in normal or athletic use.

An exemplary method for a knee wearable may comprise securing a flexible wrap to a patient, the flexible wrap comprising a plurality of pressure sensors; positioning the flexible wrap such that at least one respective pressure sensor is arranged at each of a superior, inferior, lateral, and medial position relative a patella of the patient; determining a position of the wearable with respect to the patient's knee by monitoring outputs of the pressure sensors; and alerting the patient to adjust a position of the flexible wrap based on the determined position. The method may further comprise monitoring patellar deviations with the pressure sensors.

Exemplary wearables may be customized in shape, fit, size, etc. for different joints or parts of the body including but not limited to shoulder, ankle, hip, wrist, elbow, neck/spin (cervical).

Exemplary wearables may configured for use with humans, equine (e.g. race and working horses), and other species (e.g., canine, feline, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 A and 1 B are an exemplary wearable.

FIGS. 2 A and 2 B are examples of exemplary patella sensors.

FIG. 3 illustrates information flow and production from data collection to analysis to output of joint information for users.

FIG. 4 A is a block diagram generally illustrating data collection, signal processing, and transmission.

FIG. 4 B is a diagram illustrating the supply of information to a variety of different users, including the wearer of the wearable via a mobile app or computer as well as including a medical cohort.

FIG. 5 is an example algorithm flow chart for sleeve calibration.

FIG. 6 A is another example algorithm flow chart for sleeve calibration.

FIG. 6 B is a diagram of a device input storage matrix.

FIG. 7 shows an example algorithm flow chart for swelling detection and exercise detection.

FIG. 8 is a diagram for a machine learning algorithm, in particular a convolutional neural network, which uses collected patient data to improve rehabilitation procedures and outcomes.

FIGS. 9 A to 9 C are example interfaces.

FIGS. 10 A to 10 D are further example interfaces.

FIG. 11 was flexion/extension walking pattern derived from example volunteer wearing the device. The resistances of each flex/stretch sensor and their positions or anatomical locations are noted. Note, the highly reproducible peak/valley signal dynamics between each step.

FIG. 12 is independent confirmation of knee joint motion dynamics for a single step using an external 3D motion capture camera from George Mason University's (GMU's) rehabilitation center âSMARTLabâ.

FIG. 13 is precision studies between range of motion (ROM) for a subject wearing the sleeve. The resistance of the flex sensor was calibrated to flexion/extension angles of the knee. Comparison is shown for the resistance of the flex sensor to the movement between the listed angles measured independently using a goniometer. Note the high precision along the full ROM.

FIG. 14 shows temperature accuracy and precision measured by the NTC thermistor over the dynamic range of 32-42 degrees Celsius.

FIG. 15 shows infrared thermography comparing a leg with thromboembolism to the normal contralateral knee. Note, the temperature resolution of the thermistor exceeds the resolution required to detect abnormal temperature differences.

FIG. 16 is (top) calibration graphical interface of the prompts for completing device calibration and (below) the data displayed indicates that the software can independently detect the correct type of exercise task performed during the calibration procedure.

FIG. 17 is changes observed in knee flexion angular displacement in degrees across the two tasks: side step, pivot and landing techniques (rearfoot, forefoot). Angular displacement curves during the stance phase. Each curve represents the mean normative value of the entire sample.

DETAILED DESCRIPTION

Referring now the drawings, the drawings depict and characterize exemplary medical apparatus and related methods of operation and use. At least in some embodiments, the medical apparatus may be characterized as a wearable, which implies the device is configured to be worn by a subject (e.g., a patient) and has electronics such as certain biosensors (sensors capable of measuring certain signals relating to physiology, for example). Some of the embodiments are especially configured to b

CROSS-REFERENCE TO RELATED APPLICATIONS

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under grant number AR068436 awarded by the National Institutes of Health. The government has certain rights in the invention.

FIELD OF THE INVENTION

BACKGROUND

SUMMARY

An objective of some embodiments is to provide end users with a device or system of sensors and software that can collect meaningful physiological measurements for rehabilitative purposes.

Exemplary systems will improve patient outcome in terms of joint mobility and range of motion while reducing recovery time following a knee injury or knee surgery.

A wearable's microelectronics may include sensors, data storage, battery, and wireless communication antennas (e.g., WiFi and/or Bluetooth).

Exemplary wearables may be customized in shape, fit, size, etc. for different joints or parts of the body including but not limited to shoulder, ankle, hip, wrist, elbow, neck/spin (cervical).

Exemplary wearables may configured for use with humans, equine (e.g. race and working horses), and other species (e.g., canine, feline, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 A and 1 B are an exemplary wearable.

FIGS. 2 A and 2 B are examples of exemplary patella sensors.

FIG. 3 illustrates information flow and production from data collection to analysis to output of joint information for users.

FIG. 4 A is a block diagram generally illustrating data collection, signal processing, and transmission.

FIG. 4 B is a diagram illustrating the supply of information to a variety of different users, including the wearer of the wearable via a mobile app or computer as well as including a medical cohort.

FIG. 5 is an example algorithm flow chart for sleeve calibration.

FIG. 6 A is another example algorithm flow chart for sleeve calibration.

FIG. 6 B is a diagram of a device input storage matrix.

FIG. 7 shows an example algorithm flow chart for swelling detection and exercise detection.

FIG. 8 is a diagram for a machine learning algorithm, in particular a convolutional neural network, which uses collected patient data to improve rehabilitation procedures and outcomes.

FIGS. 9 A to 9 C are example interfaces.

FIGS. 10 A to 10 D are further example interfaces.

FIG. 14 shows temperature accuracy and precision measured by the NTC thermistor over the dynamic range of 32-42 degrees Celsius.

DETAILED DESCRIPTION

FIG. 1 A depicts a wearable 100 , in particular a wearable configured as a sleeve sized to fit over a human leg and grip the leg using elastic compression. The wearable 100 may be a joint rehabilitation soft flexible wrap or sock knee legging, for example. The term wrap includes sleeves, socks, and like terms. These terms may sometimes be used interchangeably. These terms may be used to refer to a device which is configured to fit over a joint. The wearable 100 may be characterized as a portable joint monitoring system configured for prolonged use, e.g., use during which an end user may conduct everyday tasks away from home or the office of a clinician or therapist. While the wearable 100 is especially well suited for knee rehabilitation, variations of the invention may be configured for other joints (e.g., elbow, wrist, ankle, hips, shoulder, etc.) without deviating from what is disclosed herein. Embodiments may be configured for different sized patients (e.g., smaller sizes for children and larger sizes for adults) and different categories of patient (e.g., human, equine, dog, etc.).

The wearable 100 may be configured to non-invasively measure and/or monitor a number of different attributes a patient, including but not limited to patient movements and joint health. To make measurements and perform monitoring the wearable 100 may comprise a plurality of sensors 102 , a processor 103 with decision making logic for processing and extracting useful information from the sensor data, and output mechanisms for instructing, alerting, or otherwise communicating with a user (e.g., the end user or a medical practitioner). An exemplary wearable 100 may provide or assist in providing a doctor-patient joint monitoring system to not only promote joint improvement, but also provide alerts to potential serious (e.g., potentially fatal) common complications such as venous thrombosis and post-surgical infection and swelling. Alerts and alarms may be individualized.

The wearable 100 comprises a textile material 101 . The textile material 101 may be elastic to affix the wearable to a patient in a state of use. A wearable 100 may however have additional or alternative affixing features such as hook-and-loop fasteners (e.g., VelcroÂ®) which offer flexible and adjustable sizing. The textile material 101 may be a medically approved biomaterial and may be waterproof. For patient comfort and improved patient compliance, the textile material 101 may be configured to minimize or eliminate discomfort sometimes associated with form-fitting wearables. For example the textile material 101 may be relatively soft. The textile material 101 may consist of or comprise an antibacterial mesh that provides breathability and minimizes a risk of infections.

The wearable 100 may be water resistant or waterproof. Since it may be worn for extended durations of time (e.g., at least an hour, at least a day, at least a week), exposure to moisture from the environment or simply from user sweat is expected. Sensors may be encapsulated, e.g. silicone encapsulated.

An exemplary wearable 100 comprises one or more of (in some cases all of) the following sensors 102 : accelerometer(s), gyroscope(s), heart rate monitor(s), inertial measurements unit(s), stretch/flex sensor(s), pressure sensor(s), and temperature sensor(s). Some individual sensors may perform more than one of the aforementioned functionalities. Groups of sensors may be configured together as one or more sensor arrays.

Sensors of the wearable 100 may be permanently incorporated with the textile 101 , removably attachable with the textile 101 , or some combination thereof. Whether integrated or embedded or removable, most and preferably all of the sensors are sized and arranged to permit the user to conduct their ordinary daily movements and on the go activities unencumbered (e.g., walk, exercise, drive, run, etc.).

Data from the sensors is processed and interpreted to determine one or more of: joint kinematics, biomechanical forces, patient movement, effusion (swelling), heart rate, infection, thromboembolism, misalignment, impact, loading forces (e.g., step impact), biomechanical acceleration forces (level of acceleration of a joint), range/direction/frequency of motion, temperature differentials, muscle activity, joint deviations (e.g., patellar deviations)

Data may be collected by one or more of the sensors continuously, periodically, at regular or irregular interval, intermittently, or cyclically. In any case, longitudinal data is advantageous when the wearable 100 is used for long term treatments or rehabilitation goals. Collected data may be physiological, biomechanical, or some combination of these. Data may be collected, processed, and output in substantially real time.

Following are a few example sensors, measurements, and assessment that may be made with a wearable 100 . Knee kinematics and biomechanical measurements may be assessed using elastomeric resistive stretch sensors and/or flex sensors measuring ROM (degree of flexion/extension), joint angles, planes of motion (sagittal, frontal), step width, length, frequency, and gait speed may be used to evaluate gait assessment. Knee effusion (symmetric and asymmetric) may be assessed using circumferential elastomeric resistive stretch sensors measuring degree of surrounding joint tissue effusion may be used to assess phantom model with adjustable degrees of effusion. Temperature inhomogeneities may be assessed using an array of NTC thermistors measuring positional temperature differentials and rate of temperature changes in relation to a ground state temperature surrounding the patella. Patellar contours may be assessed using force/pressure sensors measuring patellar deviations and proper alignment of device. Inertial motion may be assessed using a 9 DOF IMU measuring ROM, step angle/plane, step time, traversed distance/terrain.

FIG. 1 B shows the sleeve 100 while worn by a human about the knee. The textile material 101 is not shown to permit clear depiction of sensor arrangement. A number of thermistors (and/or other types of temperature sensors) (circles 107 in FIG. 1 B ) are positioned in an array about the sleeve 100 and, correspondingly, the patient's knee. The thermistor array wraps circumferentially about the joint and span surface area (of the sleeve 100 and of the patient) of the joint itself as well as to all sides of the joint (anterior, posterior, lateral, medial, proximal, distal). The collective temperature readings from the thermistors may be used by the controller to produce a thermal heat map. The temperature measurements are interpolated and converted to the heat map. The heat map may itself be an output. The heat map may also be further processed to identify a presence or lack of an internal condition of the patient. For example, the heat map may be processed to identify an anomaly such as an infection or thrombosis.

Temperature measurement profiles such as thermal maps from the surface of the knee and surrounding regions of the leg may be used to assess physiologic temperature differences caused by anatomical structures, muscle blood flow, vascular structures and tissue frictional heating.

For knee applications, the sleeve 100 may include a pressure sensitive patellar position monitor or, more generally, a patella sensor 105 , sometimes referred to herein as a âpatellar donutâ as shorthand (though it needn't necessarily be âdonutâ shaped in all embodiments).

FIGS. 3 A and 3 B show aspects of a patella sensor 105 arranged on a subject's leg (which includes a knee with a patella P). The patella sensor 105 comprises one or more pressure sensors 202 and temperature sensors 201 configured to detect pressure and temperature, respectively, about the circumference of the patient's patella (generally at a plurality of localities, including at least positions superior, inferior, lateral, and medial to the subject's patella). Additional sensors at additional locations may also be included. For example, some embodiments include a second temperature sensor at a backside of the knee for measuring a temperature T 2 which may be compared with one or more temperatures T 1 sensed at the circumference of the patella P. Generally, a patella sensor 105 may include sensing elements for one or more (e.g., all) of temperature, pressure, dielectric constant, impedance, and deformation. FIG. 2 A includes four non-limiting alternatives for configurations of patella sensors 105 , labeled respective as

patella sensors

1051 , 1052 , 1053 , and 1054 . As illustrated, a patella sensor 105 may be donut shaped, circular, enclosed, open, pyramidal, diamond shaped, V shaped, U shaped, or some other configuration. A patella sensor 105 may be used together with, for example, one or more circumferential stretch sensors S 1 . Subdiagram 210 shows how a change in the relative alignment/positions of the patella P and patella sensor 105 is sensed by one or sensing elements (e.g., pressure sensors 202 ), indicated in 210 by shading within sensor 105 . The sensed change in alignment can trigger an alert or instruction that adjustment of the sleeve 100 is required.

The left side of FIG. 2 B is illustrative for an exemplary method for diagnosing embolism or infection in a subject's leg. Such a method may comprise placing a wrap/ sleeve 100 about a knee of a subject, the wrap comprising a plurality of first sensors positionable to sense temperatures T 1 about a circumference of a patella P of the knee and at least one second sensor positionable to sense temperature T 2 at a back of the knee; comparing temperatures from the first and second temperature sensors; and identifying a presence of embolism or infection in the subject's leg based on the comparison.

The right side of FIG. 2 B is illustrative for an exemplary method for positioning a wearable sensing device on a subject's leg. Such a method may comprise placing a wrap/ sleeve 100 with a patella sensor 105 about a knee of a subject, the wrap comprising a plurality of sensors for measuring one or more physical or physiological parameters; urging alignment of a depression in the wrap with a patella of the knee, at least some of the plurality of sensors being arranged about a circumference of the depression; and monitoring or confirming an alignment of the patella with the depression based on one or more of (i) changes in an output of at least one of the circumferential sensors and (ii) differences among outputs of two or more of the circumferential sensors.

In use the patellar sensor 105 is configured to guide or contribute to guidance of a user aligning the device in the correct individualized orientation. The patellar force sensor is also used during the device operation to detect abnormal or pathologic deviations of the patellar position or orientation that may reflect injury or abnormal directions of patellar forces during rehabilitation. In some exemplary embodiments, the patellar donut is a deformable circumferential pillow or cushion molded to an individual's patella to conform or surround the patella to measure patellar physical deviations in all planes of motion with embedded sensors. The patellar donut 105 provides the sleeve 100 with measurements of patellar deviations. In an exemplary configuration, circumferential elastomer conductive resistive stretch sensors are positioned superior and inferior to the patella to detect variations of joint effusion. Additionally, the stretch sensors may be combined with (silicon encapsulated) flex sensors positioned medially, laterally, and anteriorly to measure ROM and planar motions. In some embodiments the patellar donut is divided radially into series of chambers 106 filled with conductive fluid or gel ( FIG. 1 B shows a patellar donut 105 having 12 chambers 106 ; other embodiments may have fewer than 12 or greater than 12, e.g., 4, 6, 8, 10, 16, or 32 chambers). Relative differences in the resistance/impedance levels within each chamber reflects the conformation of the patellar sensor for calibration. Exemplary applications and uses for a patella sensor 105 are described below in Example 3.

Temperature wrap-around knee thermal mapping by a wearable device and patella displacement force mapping by the wearable device together permit physiologic monitoring of joint tissues and, with respect to the knee, patellar dynamics and patellofemoral pain. An important measurement set is the relative differences in temperature over the surface of the joint matched with stretch and force sensors corresponding to the temperature reading locations. By pairing these measurements a pathologic swelling may be detected by sensing an increased temperature associated with increase stretch over the underlying swollen region.

The sensors of the wearable 100 may include one or more inertial measurement units (IMUs). An IMU may be, for example, a 9 degree of freedom (DOF) IMU. The IMU may be configured to detect forces and motion in various planes. FIG. 1 B depicts two exemplary positions for two respective IMUs 109 . One IMU is proximal to the joint, and another IMU is distal to the joint. IMUs are particularly well suited for detection of gait abnormalities.

Power for a wearable 100 may be provided by, for example, a power supply such as a lithium ion gel battery. Sensors may be low power (e.g., low voltage) to permit extended use between battery charges. Power supplies, e.g., batteries may be interchangeable, or an integrated power supply may be used which is recharged by wire or wirelessly (e.g., by inductive charging).

The wearable 100 may include one or more embedded or removable storage devices (e.g., solid state memory). The storage devices may be volatile and/or non-volatile. The storage may comprise executable instructions which, when executed by the controller, cause it perform functionalities described herein, among other possible functions. For example a controller executing stored instructions may provide processing and determinations based on data being collected from the sensors in real time. The controller, executing stored instructions, may for example detect localized elevations in body temperature by monitoring the temperatures sensed by the temperature sensors at the at least two different positions on the wrap, detect swelling by monitoring the strain sensed by the at least one stretch sensor, identify a risk of thrombosis or infection when one or more of localized elevation in body temperature and swelling are detected, and/or provide an alert to one or more of the patient and a heath care professional of the identified risk.

FIG. 3 illustrates information flow and production from data collection 301 , to analysis, to output of joint information for users 303 . While original data points (e.g., temperature readings 310 from individual thermistors) may be provided to users, the processed information (e.g., heat map 311 ) derived from the original measurements may often be of greater value and is advantageous as an element of final output to users.

The wearable 100 may comprise an âonboardâ computer or controller, e.g., a processor or microprocessor and supporting circuitry like data storage devices and a power supply e.g., battery. The controller may be, for example, a CPU or microprocessor (a fully operational integrated computer chip). The controller may be located in a superior region of the device. The controller may be fully programmable and encrypted to allow for modifications or personalization of rehabilitation exercises for the user and physician.

FIG. 4 A is a block diagram generally illustrating data collection 411 , signal processing 422 , and transmission 433 . Data collection 411 may be mainly or entirely âon deviceâ that is to say performed by sensors attached or integrated with the wearable 100 . Signal processing 422 , on the other hand, may be performed on device, off device, or some combination thereof. For example, processor(s) and storage may be part of the wearable 100 and perform signal processing 422 . Alternatively or in addition, one or more processors and storage media may be off device (e.g., in a user's mobile device like a smartphone, in a cloud based server, etc.) and perform some or all of signal processing 422 . In these cases the signal processing 422 and its supporting structures are remote from and not on the wearable 100 . In short, a wearable 100 may collect physiological and medical data and in real time transmit that data in unprocessed or processed form to remote devices via radio telemetry or direct download onto a protected server, for example.

In some embodiments the wearable 100 has wireless capabilities, e.g., antenna and supporting circuitry for transmitting data collected and/or determined by the wearable 100 wirelessly. Suitable wireless standards and technology are known and may be employed, including but not limited to WiFi and Bluetooth.

Secure network technology may be used for longitudinal remote monitoring and wireless transmission of data between, for example, an attending physician and the patient. Patient data, raw or processed, may be stored in one or more of the wearable 100 , end user devices (like a patient's smartphone or computer), local servers, remote servers, cloud servers, or some combination thereof. Data may be stored for a fixed amount of time, e.g., one month, and then automatically removed. This can be particularly advantageous for storage on a wearable 100 where storage space comes at a premium to ensure the size, weight, power consumption, and cost are minimized. Stored data may of course be encrypted for patient privacy concerns.

While physical and physiological data may be transmitted to medical support personnel like doctors and therapists, these entities may transmit data back to the end user. For example, rehab program and exercise regiments may be updated over the course of a patient using a wearable 100 . Real time data may be compared with historic data to track progress over time and update medical care over time. In short the networked communication system may be used for individualized care without a patient making physical trips to a doctor or therapist's office for periodic sessions.

FIG. 4 B is a diagram illustrating the supply of information to a variety of different users, including the wearer of the wearable 100 via a mobile app 401 or computer 402 as well as including a medical cohort 403 . âUsersâ may include subjects that actually wear a wearable 100 , caretakers, medical practitioners (e.g., doctors, nurses, technicians), therapists (e.g., physical therapists), and others.

Data and other outputs of a wearable 100 may be supplied to one or more users using any of a variety of interfaces. Generally it is advantageous that interfaces be presented in friendly and easy to understand configurations. Devices suited for user interface are man, including but not limited to smartphones, tablets, display, projectors, general purpose computers, special purpose computers, and other wearables (e.g., a smartwatch). A wearable 100 may itself have a user interface attached or imbedded therewith but needn't in all cases. Given the prevalence of smartphones, a wireless communication link between a wearable 100 and smartphone may be used which allows the wearable 100 influence or control over a user interface displayed our otherwise output on the smartphone.

User interface output may come from sources other than the wearable 100 . For example, specialists such as doctors or rehabilitation specialists may send information over a network to an end user interface, e.g., an interface of the person wearing a wearable 100 . For example exercise routines may be provided for an end user to perform while wearing the wearable 100 . Data may be collected during the exercise which is then transmitted back to the specialist for interpretation or monitoring.

FIG. 5 shows an example algorithm flow chart for sleeve calibration. Separate wearables 100 may be calibrated to individual patients. A patient's individual sleeve may learn the behavior of that specific patient's joint over time. During an initial consultation with a doctor or therapist, a patient may undergo calibration tests. The medical attendant may instruct the patient to perform a gait assessment, or a series of exercises such as squatting, or leg lifting while sitting, or other join movement sequences that provide information about movement in a specific direction or associated with a specific tendon or rehabilitation goal. The patient will perform the series of motions while wearing the sleeve. Calibration exercises may collect the following parameters: initial gait style, torsion ability, range of motion values, and step length, for example. While the patient performs the assessment, the device receives and stores sensor values. Each assessment may be calibrated for a number of times.

FIG. 6 A shows another example algorithm flow chart for sleeve calibration. FIG. 6 B is a diagram of a device input storage matrix. Once calibration has ended, the wearable's onboard programming may calculate an average of the collected sensor values and determine thresholds based on the set of calibrations with a set standard deviation of two above the range (above or below) of threshold values for the exercise. The application's graphical interface may compare real-time data to the historical record of the joint for easy analysis and understanding of progress.

Calibration exercises may automatically correct for temperature differences and hysteresis following rapid movement. Personalized baselines may be established for longitudinal monitoring relating to success or failure to reach rehabilitation goals, or to provide out-of-bound alerts associated with impending medical complications. These include swelling, abnormal range of joint motion, high acceleration associated with a fall, and temperature imbalances associated with thromboembolism or infection.

FIG. 7 shows an example algorithm flow chart for swelling detection and exercise detection. The detection methods use current real time sensor outputs together with a constant variable matrix generated during calibration (see FIGS. 5 and 6 ). The constant variable matrix is used to generate patient specific thresholds. The real time sensor readings are compared to these thresholds to detect abnormalities. For example, the process may detect localized elevations in body temperature by monitoring the temperatures sensed by the temperature sensors at the at least two different positions on the wearable 100 . The process may detect swelling by monitoring the strain sensed by the at least one stretch sensor of the wearable 100 . The process may identify a risk of thrombosis or infection when one or more of localized elevation in body temperature and swelling are detected.

Real-time changes in the data falling out of expected ranges compared to historical data generate notifications or alerts to the patient and the doctor. These warnings indicate an injury impact, a life-threatening complication, or a severe abnormal direction or range of movement by the jointâfactors which can prolong recovery or chance of reinjury and prolonged non-deployable status. During rehabilitation, the device may coach the patient to use the joint and notify the physician about the status of target goals. A common source of pain is anterior knee pain. The patellar monitoring sensor will record patellar deviations that correlate with time periods in which the anterior knee pain is felt. The timing of the pain can in turn be correlated with the readings of the other sensors. The outcome can be a change in the rehabilitation schedule that minimizes or prevents the pain.

The application's graphical interface compares real-time data to the historical record of the joint for easy analysis and understanding of progress. Real-time changes in the data falling out of expected ranges compared to historical data generate alerts to the patient and the doctor. These warnings indicate an injury impact, a life-threatening complication, or a severe abnormal direction or range of movement by the jointâfactors which can prolong recovery or chance of re-injury and prolonged non-deployable status.

FIG. 8 is a diagram for a machine learning algorithm, in particular a convolutional neural network, which uses collected patient data to improve rehabilitation procedures and outcomes. While a default or individualized standard course of rehabilitation may be recommended for an individual patient clinical study, the data from groups of patients correlated with outcomes may be analyzed with machine learning to develop new classes of rehabilitation regimens for future patients. Such a specialized recovery plan may be the result of correlating physiologic measurements, patient behavior factors, and training inputs, that most influence recovery.

Convolutional neural networks (CNN) can be trained on data involving the frequency of various exercises performed by patients and the duration of recovery to identify which exercise plans result in a quicker recovery. In this manner wearables 100 may help future patients with histories similar to past patients, by, for example, suggesting rehabilitation plans that are expected to be the most beneficial to the patient's recovery, and offering specific corrections to a patient's activity or inactivity that may hinder their recovery process.

Progress toward rehabilitation may be computed based on sensor data from a subject's wearable and reference data. Data retrieved from sensors (e.g., of a wearable 100 ) may be compared with reference data in a databank which specifies parameter thresholds corresponding with rehabilitation progress levels. A computation of progress toward rehabilitation based on data retrieved from sensors may determine whether data for one or more physical or physiological parameters approach a stored normal or baseline measurement for similar subjects or a baseline measurement for the particular subject obtained prior to injury or surgery. Improvement to within 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1% of the normal or baseline measurement can be an indication of rehabilitation of the joint.

Embodiments may involve integration and linkage with patient's medical data. Data sets from multiple users may be analyzed to improve rehabilitation care procedures and monitoring. These data sets may include information such as a patient's basic medical history, progress meeting goals of exercises prescribed by the doctors, and the rate of patient progress throughout rehabilitation. The full record of monitored sensor data, and time to recovery will be part of the patient record.

FIGS. 9 A- 9 C and FIGS. 10 A- 10 D show non-limiting example interfaces. An exemplary system provides for a rapid, easy to understand interface for the patient as well as an individual view and multi-patient dashboard for a supporting medical team. Real time and historical data may be presented in a friendly easy to understand interface on the patient's smartphone. Outputs on a user interface may include reminders and encouragements based on customized rehabilitation regimens loaded by a medical team.

Patient prompts and encouragements may be output through a patient interface to achieve preset milestones. The software may interact with patients to encourage coaching, milestones, and rehab assignments designed by a specialist. For example, on a phone app, the patient may be coached through walking and knee motion exercises, while simultaneously monitoring relevant physiologic parameters such as number and rate of steps, range and direction of motion, tissue temperature imbalances, swelling, and acceleration force. Physicians and physical therapists may add patient data, such as height and weight, and a personalized rehabilitation plan, with specific goals and exercises, to the phone application. Integrated software will calibrate the sensors to the baseline measurements during initial fitting of the device.

Advanced predictive algorithms may instruct, manage, and track patient rehabilitation. Precise, encrypted, quantitative data is collected by the sleeve and transmitted via Bluetooth, radio telemetry, or WiFi using secure military software to both the physician and patient's applications for rehabilitation guidance and alar

CLAIMS

Claims ( 18 )

What is claimed is:

1. A device, comprising

a wrap for a joint of a patient's body, wherein the joint is a knee, elbow, wrist, hip, ankle, or shoulder;

temperature sensors attached or attachable to the wrap, the temperature sensors comprising a first temperature sensor configured to sense a first temperature in a first position on the wrap and a second temperature sensor configured to sense a second temperature in a second position on the wrap, wherein the first position corresponds with a back of the joint and the second position corresponds with a front of the joint; and

at least one processor and storage, the storage comprising executable instructions which, when executed by the processor, cause the at least one processor to detect an elevation between the first and second temperatures indicative of an embolism and determine a diagnostic output of an embolism based on the elevation.

2. The device of claim 1 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to determine a therapeutic output based on the elevation.

3. The device of claim 1 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to provide an alert of a health risk implicated by the elevation.

4. The device of claim 3 , wherein the health risk is an embolism.

5. The device of claim 1 , wherein the joint is a knee.

6. The device of claim 5 , wherein the second position corresponds with a patella of the knee.

7. The device of claim 1 , further comprising one or more stretch sensors.

8. The device of claim 7 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to detect a change indicative of swelling at the joint from signals of the one or more stretch sensors.

9. The device of claim 8 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to determine a diagnostic or therapeutic output based on a combination of the elevation and the change indicative of swelling at the joint.

10. A method, comprising

measuring a first temperature at a back of a joint with a first temperature sensor, wherein the joint is a knee, elbow, wrist, hip, ankle, or shoulder;

measuring a second temperature at a second position with a second temperature sensor, wherein the second position corresponds with a front of the joint;

comparing the first and second temperatures from the first and second temperature sensors; and

determining a diagnostic output of an embolism from the temperature comparison.

11. A device, comprising

a first temperature sensor configured to measure a first temperature at a back of a joint, wherein the joint is a knee, elbow, wrist, hip, ankle, or shoulder;

a second temperature sensor configured to measure a second temperature at a second position different than the back of the joint; and

at least one processor and storage, the storage comprising executable instructions which, when executed by the at least one processor, cause the at least one processor to detect an elevation between the first and second temperatures indicative of an embolism and determine a diagnostic output of an embolism from the elevation.

12. The device of claim 11 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to determine a therapeutic output based on the elevation.

13. The device of claim 11 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to provide an alert of a health risk implicated by the elevation, wherein the health risk is an embolism.

14. The device of claim 11 , wherein the joint is a knee.

15. The device of claim 14 , wherein the second position corresponds with a patella of the knee.

16. The device of claim 11 , further comprising one or more stretch sensors.

17. The device of claim 16 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to detect a change indicative of swelling at the joint from signals of the one or more stretch sensors.

18. The device of claim 17 , wherein the storage comprises further executable instructions which, when executed by the at least one processor, cause the at least one processor to determine a diagnostic or therapeutic output based on a combination of the elevation and the change indicative of swelling at the joint.

US17/651,661

2017-10-24

2022-02-18

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

Active

2038-11-05

US12011291B2

( en )

Priority Applications (1)

Application Number

Priority Date

Filing Date

Title

US17/651,661

US12011291B2

( en )

2017-10-24

2022-02-18

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

Applications Claiming Priority (3)

Application Number

Priority Date

Filing Date

Title

US201762576361P

2017-10-24

US16/169,295

US11284838B2

( en )

2017-10-24

2018-10-24

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

US17/651,661

US12011291B2

( en )

2017-10-24

2022-02-18

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

Related Parent Applications (1)

Application Number

Title

Priority Date

Filing Date

US16/169,295

Continuation

US11284838B2

( en )

2017-10-24

2018-10-24

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

Publications (2)

Publication Number

Publication Date

US20220167918A1

US20220167918A1 ( en )

2022-06-02

US12011291B2

true

US12011291B2 ( en )

2024-06-18

Family

ID=66170302

Family Applications (2)

Application Number

Title

Priority Date

Filing Date

US16/169,295

Active

2040-01-22

US11284838B2

( en )

2017-10-24

2018-10-24

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

US17/651,661

Active

2038-11-05

US12011291B2

( en )

2017-10-24

2022-02-18

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

Family Applications Before (1)

Application Number

Title

Priority Date

Filing Date

US16/169,295

Active

2040-01-22

US11284838B2

( en )

2017-10-24

2018-10-24

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

Country Status (1)

Country

Link

( 2 )

US11284838B2

( en )

Families Citing this family (91)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US11051967B2

( en )

2016-10-07

2021-07-06

Df2, Llc

Functional fracture brace for femur fractures

WO2018081795A1

( en )

2016-10-31

2018-05-03

Zipline Medical, Inc.

Systems and methods for monitoring physical therapy of the knee and other joints

US11284838B2

( en )

2017-10-24

2022-03-29

George Mason University Research Foundation, Inc.

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

US20200297269A1

( en )

2017-12-04

2020-09-24

Riccardo Vieri

Apparatus for tracking user activity

US11024195B2

( en )

2018-04-10

2021-06-01

Imam Abdulrahman Bin Faisal University

Diagnostic and therapeutic system for manual therapy

GB2574074B

( en )

2018-07-27

2020-05-20

Mclaren Applied Tech Ltd

Time synchronisation

US20210186436A1

( en )

2018-08-07

2021-06-24

NEC Corporator

Joint disorder risk evaluation device, system, method, and program

JP2022501051A

( en )

2018-09-18

2022-01-06

ãã¼ã¹ãã¯ã¼ ãã¯ããã¸ã¼ãº ã¤ã³ã³ã¼ãã¬ã¤ãããHorsepower Technologies, Inc.

Equipment, performance analysis systems and methods that can be attached to horses

US12048666B2

( en )

2018-11-12

2024-07-30

Hyperice Ip Subco, Llc

System and process for determining pressure settings for a percussive massage applicator

EP3923782A1

( en )

2019-02-15

2021-12-22

KCI Licensing, Inc.

Systems and methods for differentially detecting subtle onset of infection, activity level, and periwound hydration for enhanced wound monitoring

US11304650B1

( en )

2019-03-20

2022-04-19

University Of South Florida

Systems and methods for heel-to-shin testing

US11801423B2

( en )

2019-05-10

2023-10-31

Rehab2Fit Technologies, Inc.

Method and system for using artificial intelligence to interact with a user of an exercise device during an exercise session

US11904207B2

( en )

2019-05-10

2024-02-20

Rehab2Fit Technologies, Inc.

Method and system for using artificial intelligence to present a user interface representing a user's progress in various domains

US11833393B2

( en )

2019-05-15

2023-12-05

Rehab2Fit Technologies, Inc.

System and method for using an exercise machine to improve completion of an exercise

US11497452B2

( en )

2019-06-20

2022-11-15

The Hong Kong Polytechnic University

Predictive knee joint loading system

US12402804B2

( en )

2019-09-17

2025-09-02

Rom Technologies, Inc.

Wearable device for coupling to a user, and measuring and monitoring user activity

US11071597B2

( en )

2019-10-03

2021-07-27

Rom Technologies, Inc.

Telemedicine for orthopedic treatment

WO2022216498A1

( en )

2021-04-08

2022-10-13

Rom Technologies, Inc.

Method and system for monitoring actual patient treatment progress using sensor data

US12555667B2

( en )

2019-10-03

2026-02-17

Rom Technologies, Inc.

Systems and methods for using AI/ML and for cardiac and pulmonary treatment via an electromechanical machine related to urologic disorders and antecedents and sequelae of certain urologic surgeries

US12246222B2

( en )

2019-10-03

2025-03-11

Rom Technologies, Inc.

Method and system for using artificial intelligence to assign patients to cohorts and dynamically controlling a treatment apparatus based on the assignment during an adaptive telemedical session

US11282599B2

( en )

2019-10-03

2022-03-22

Rom Technologies, Inc.

System and method for use of telemedicine-enabled rehabilitative hardware and for encouragement of rehabilitative compliance through patient-based virtual shared sessions

US12347543B2

( en )

2019-10-03

2025-07-01

Rom Technologies, Inc.

Systems and methods for using artificial intelligence to implement a cardio protocol via a relay-based system

US12562243B2

( en )

2019-10-03

2026-02-24

Rom Technologies, Inc.

System and method for processing medical claims using biometric signatures

US12220201B2

( en )

2019-10-03

2025-02-11

Rom Technologies, Inc.

Remote examination through augmented reality

US11955220B2

( en )

2019-10-03

2024-04-09

Rom Technologies, Inc.

System and method for using AI/ML and telemedicine for invasive surgical treatment to determine a cardiac treatment plan that uses an electromechanical machine

US11955222B2

( en )

2019-10-03

2024-04-09

Rom Technologies, Inc.

System and method for determining, based on advanced metrics of actual performance of an electromechanical machine, medical procedure eligibility in order to ascertain survivability rates and measures of quality-of-life criteria

US12020800B2

( en )

2019-10-03

2024-06-25

Rom Technologies, Inc.

System and method for using AI/ML and telemedicine to integrate rehabilitation for a plurality of comorbid conditions

US12230382B2

( en )

2019-10-03

2025-02-18

Rom Technologies, Inc.

Systems and methods for using artificial intelligence and machine learning to predict a probability of an undesired medical event occurring during a treatment plan

US12150792B2

( en )

2019-10-03

2024-11-26

Rom Technologies, Inc.

Augmented reality placement of goniometer or other sensors

US11515021B2

( en )

2019-10-03

2022-11-29

Rom Technologies, Inc.

Method and system to analytically optimize telehealth practice-based billing processes and revenue while enabling regulatory compliance

US11270795B2

( en )

2019-10-03

2022-03-08

Rom Technologies, Inc.

Method and system for enabling physician-smart virtual conference rooms for use in a telehealth context

US12427376B2

( en )

2019-10-03

2025-09-30

Rom Technologies, Inc.

Systems and methods for an artificial intelligence engine to optimize a peak performance

US12230381B2

( en )

2019-10-03

2025-02-18

Rom Technologies, Inc.

System and method for an enhanced healthcare professional user interface displaying measurement information for a plurality of users

US12539446B2

( en )

2019-10-03

2026-02-03

Rom Technologies, Inc.

Method and system for using sensors to optimize a user treatment plan in a telemedicine environment

US11139060B2

( en )

2019-10-03

2021-10-05

Rom Technologies, Inc.

Method and system for creating an immersive enhanced reality-driven exercise experience for a user

US11282608B2

( en )

2019-10-03

2022-03-22

Rom Technologies, Inc.

Method and system for using artificial intelligence and machine learning to provide recommendations to a healthcare provider in or near real-time during a telemedicine session

US12420143B1

( en )

2019-10-03

2025-09-23

Rom Technologies, Inc.

System and method for enabling residentially-based cardiac rehabilitation by using an electromechanical machine and educational content to mitigate risk factors and optimize user behavior

US20230245750A1

( en )

2019-10-03

2023-08-03

Rom Technologies, Inc.

Systems and methods for using elliptical machine to perform cardiovascular rehabilitation

US12589279B2

( en )

2019-10-03

2026-03-31

Rom Technologies, Inc.

Systems and methods of using artificial intelligence and machine learning for generating an alignment plan capable of enabling the aligning of a user's body during a treatment session

US12020799B2

( en )

2019-10-03

2024-06-25

Rom Technologies, Inc.

Rowing machines, systems including rowing machines, and methods for using rowing machines to perform treatment plans for rehabilitation

US12327623B2

( en )

2019-10-03

2025-06-10

Rom Technologies, Inc.

System and method for processing medical claims

US12420145B2

( en )

2019-10-03

2025-09-23

Rom Technologies, Inc.

Systems and methods of using artificial intelligence and machine learning for generating alignment plans to align a user with an imaging sensor during a treatment session

US11317975B2

( en )

2019-10-03

2022-05-03

Rom Technologies, Inc.

Method and system for treating patients via telemedicine using sensor data from rehabilitation or exercise equipment

US12548656B2

( en )

2019-10-03

2026-02-10

Rom Technologies, Inc.

System and method for an enhanced patient user interface displaying real-time measurement information during a telemedicine session

US11087865B2

( en )

2019-10-03

2021-08-10

Rom Technologies, Inc.

System and method for use of treatment device to reduce pain medication dependency

US12224052B2

( en )

2019-10-03

2025-02-11

Rom Technologies, Inc.

System and method for using AI, machine learning and telemedicine for long-term care via an electromechanical machine

US12191018B2

( en )

2019-10-03

2025-01-07

Rom Technologies, Inc.

System and method for using artificial intelligence in telemedicine-enabled hardware to optimize rehabilitative routines capable of enabling remote rehabilitative compliance

US11282604B2

( en )

2019-10-03

2022-03-22

Rom Technologies, Inc.

Method and system for use of telemedicine-enabled rehabilitative equipment for prediction of secondary disease

US12380984B2

( en )

2019-10-03

2025-08-05

Rom Technologies, Inc.

Systems and methods for using artificial intelligence and machine learning to generate treatment plans having dynamically tailored cardiac protocols for users to manage a state of an electromechanical machine

US11325005B2

( en )

2019-10-03

2022-05-10

Rom Technologies, Inc.

Systems and methods for using machine learning to control an electromechanical device used for prehabilitation, rehabilitation, and/or exercise

US12154672B2

( en )

2019-10-03

2024-11-26

Rom Technologies, Inc.

Method and system for implementing dynamic treatment environments based on patient information

US12176089B2

( en )

2019-10-03

2024-12-24

Rom Technologies, Inc.

System and method for using AI ML and telemedicine for cardio-oncologic rehabilitation via an electromechanical machine

US11265234B2

( en )

2019-10-03

2022-03-01

Rom Technologies, Inc.

System and method for transmitting data and ordering asynchronous data

US12100499B2

( en )

2020-08-06

2024-09-24

Rom Technologies, Inc.

Method and system for using artificial intelligence and machine learning to create optimal treatment plans based on monetary value amount generated and/or patient outcome

US12478837B2

( en )

2019-10-03

2025-11-25

Rom Technologies, Inc.

Method and system for monitoring actual patient treatment progress using sensor data

US11955221B2

( en )

2019-10-03

2024-04-09

Rom Technologies, Inc.

System and method for using AI/ML to generate treatment plans to stimulate preferred angiogenesis

GB2588236B

( en )

2019-10-18

2024-03-20

Mclaren Applied Ltd

Gyroscope bias estimation

US12562271B2

( en )

2019-11-06

2026-02-24

Rom Technologies, Inc.

System for remote treatment utilizing privacy controls

US11826613B2

( en )

2019-10-21

2023-11-28

Rom Technologies, Inc.

Persuasive motivation for orthopedic treatment

US12424319B2

( en )

2019-11-06

2025-09-23

Rom Technologies, Inc.

System for remote treatment utilizing privacy controls

GB2586526B

( en )

2019-12-20

2021-08-11

Digital & Future Tech Limited

System and method for monitoring body movement

US11107591B1

( en )

2020-04-23

2021-08-31

Rom Technologies, Inc.

Method and system for describing and recommending optimal treatment plans in adaptive telemedical or other contexts

CN111436938B

( en )

2020-04-30

2023-11-14

å¦é¨ä¸ç¿æä¼åç§ææéå¬å¸

Limb swelling monitoring device and method

US20220008237A1

( en )

2020-06-19

2022-01-13

Kinisi Inc

Wearable System for Evaluating Joint Performance and Function

US12514727B2

( en )

2020-06-19

2026-01-06

Kinisi Inc

Wearable system for evaluating joint performance and function

RU2748271C1

( en )

2020-07-02

2021-05-21

Apparatus for determining stiffness of patellar ligamentous apparatus and method of use thereof

US12367960B2

( en )

2020-09-15

2025-07-22

Rom Technologies, Inc.

System and method for using AI ML and telemedicine to perform bariatric rehabilitation via an electromechanical machine

FR3114020B1

( en )

2020-09-15

2022-12-02

Genourob

Method and apparatus for the detection and therapeutic monitoring of knee ligament injuries

US12515104B2

( en )

2020-10-28

2026-01-06

Rom Technologies, Inc.

Systems and methods for using machine learning to control a rehabilitation and exercise electromechanical device

AU2021371301A1

( en )

2020-10-30

2023-06-15

Mako Surgical Corp.

Robotic surgical system with cut selection logic

EP4262605A1

( en )

2020-12-15

2023-10-25

MAKO Surgical Corp.

Dynamic gap capture and flexion widget

KR20230154257A

( en )

2021-03-07

2023-11-07

ë¦¬í´ë ìì´ì´ ì¸í¬.

Devices, systems, and methods for monitoring and characterizing user's actions via flexible circuitry

CN113143458A

( en )

2021-03-15

2021-07-23

åäº¬çå·¥å¤§å¦

Method and device for evaluating aortic dissection distal laceration occlusion treatment scheme

US20220314072A1

( en )

2021-03-30

2022-10-06

Rehab2Fit Technologies, Inc.

Adjustment of exercise based on artificial intelligence, exercise plan, and user feedback

WO2022225679A1

( en )

2021-04-22

2022-10-27

Bard Peripheral Vascular, Inc.

Wearable device for monitoring fluid overload with built-in sensors

USD1044829S1

( en )

2021-07-29

2024-10-01

Mako Surgical Corp.

Display screen or portion thereof with graphical user interface

US12414703B2

( en )

2021-08-02

2025-09-16

Mozarc Medical Us Llc

Medical device system for remote monitoring and inspection

TWI787982B

( en )

2021-08-31

2022-12-21

è²¡åæ³äººè³è¨å·¥æ¥çé²æ

Sports injury sensing system and method

USD1053901S1

( en )

2021-11-05

2024-12-10

Howmedica Osteonics Corp.

Display screen or portion thereof with graphical user interface

USD1067239S1

( en )

2021-11-05

2025-03-18

Howmedica Osteonics Corp.

Display screen or portion thereof with animated graphical user interface

USD1084008S1

( en )

2021-11-05

2025-07-15

Howmedica Osteonics Corp.

Display screen or portion thereof with graphical user interface

USD1120934S1

( en )

2021-11-05

2026-03-31

Howmedica Osteonics Corp.

Display screen or portion thereof with graphical user interface

USD1110333S1

( en )

2021-11-05

2026-01-27

Howmedica Osteonics Corp.

Display screen or portion thereof with animated graphical user interface

US20230355137A1

( en )

2022-05-03

2023-11-09

X-ray Optics, Inc.

Compact wireless range of motion measurement system and method

CN115040114B

( en )

2022-06-23

2024-08-27

å¹¿è¥¿å¤§å¦

A remote rehabilitation system and training method based on virtual reality and human-computer interaction

JP2025528192A

( en )

2022-08-12

2025-08-26

ã¨ãã¼ã¸ã£ ã³ã¼ãã¬ã¼ã·ã§ã³

Non-invasive wearables for physiological assessment of physical and cognitive readiness for military missions

WO2024039844A1

( en )

2022-08-18

2024-02-22

Noble International, Llc

Compression sleeve for quantitative measurements

WO2024107939A1

( en )

2022-11-16

2024-05-23

Accu-Therm Systems, Inc.

Non-invasive sensor apparatus and method for assessing cardiac performance of the feet

TWI846580B

( en )

2023-08-29

2024-06-21

åç«æ¸è¯å¤§å¸

Gait monitoring and healthcare system

CN118975799B

( en )

2024-10-21

2024-12-31

æéææ§(åäº¬)ç§ææéå¬å¸

Knee joint pressure sensing device and system

CN120954102B

( en )

2025-10-11

2026-01-23

ä¸å½äººæ°è§£æ¾åæ»å»é¢ç¬¬äºå»å¦ä¸å¿

A method for assessing joint range of motion during postoperative rehabilitation training in elderly patients

Citations (17)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US6002965A

( en )

1998-06-10

1999-12-14

Katz; Amiram

Self applied device and method for prevention of deep vein thrombosis

US6360615B1

( en )

2000-06-06

2002-03-26

Technoskin, Llc

Wearable effect-emitting strain gauge device

US20070273504A1

( en )

2006-05-16

2007-11-29

Bao Tran

Mesh network monitoring appliance

US20080161731A1

( en )

2006-12-27

2008-07-03

Woods Sherrod A

Apparatus, system, and method for monitoring the range of motion of a patient's joint

US20090281412A1

( en )

2007-12-18

2009-11-12

Searete Llc, A Limited Liability Corporation Of The State Of Delaware

System, devices, and methods for detecting occlusions in a biological subject

US20120184878A1

( en )

2011-01-13

2012-07-19

Bijan Najafi

Intelligent device to monitor and remind patients with footwear, walking aids, braces, or orthotics

US20120203078A1

( en )

2011-02-08

2012-08-09

Aulisa Medical Technologies, Inc.

Wireless Patient Monitoring System

US20140109304A1

( en )

2012-10-19

2014-04-24

Avery Audrey Kwan

Intelligent protective gear bracing mechanism

WO2014209916A1

( en )

2013-06-23

2014-12-31

Hunter William L

Devices, systems and methods for monitoring knee replacements

US20150088043A1

( en )

2009-07-15

2015-03-26

President And Fellows Of Harvard College

Actively controlled wearable orthotic devices and active modular elastomer sleeve for wearable orthotic devices

US20150328032A1

( en )

2014-05-13

2015-11-19

New York University

Smart knee fixture and system for measuring knee balancing

US9314159B2

( en )

2012-09-24

2016-04-19

Physio-Control, Inc.

Patient monitoring device with remote alert

WO2016058053A1

( en )

2014-10-17

2016-04-21

Epworth Research Institute

A screening test for detection of deep vein thrombosis

US20160242646A1

( en )

2015-07-07

2016-08-25

Padraic R. Obma

Noninvasive medical monitoring device, system and method

US20170042467A1

( en )

2014-04-25

2017-02-16

Massachusetts Institute Of Technology

Feedback Method And Wearable Device To Monitor And Modulate Knee Adduction Moment

US20170367644A1

( en )

2016-06-27

2017-12-28

Claris Healthcare Inc.

Apparatus and Method for Monitoring Rehabilitation from Joint Surgery

US11284838B2

( en )

2017-10-24

2022-03-29

George Mason University Research Foundation, Inc.

Non-invasive wearable biomechanical and physiology monitor for injury prevention and rehabilitation

Family Cites Families (5)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US5733292A

( en )

1995-09-15

1998-03-31

Midwest Orthopaedic Research Foundation

Arthroplasty trial prosthesis alignment devices and associated methods

WO2009014644A1

( en )

2007-07-20

2009-01-29

Ossur Hf

Prosthetic or orthopedic device having feedback

US9387096B2

( en )

2009-06-17

2016-07-12

Ossur Hf

Feedback control systems and methods for prosthetic or orthotic devices

US11039752B2

( en )

2012-09-17

2021-06-22

Accu-Therm Systems, Inc.

Non-invasive sensor apparatus and method for assessing cardiac performance

US20170086671A1

( en )

2015-09-25

2017-03-30

Zimmer, Inc.

Intelligent orthopedic apparatus, systems, and methods

2018

2018-10-24

US16/169,295

patent/US11284838B2/en

active

Active

2022

2022-02-18

US17/651,661

patent/US12011291B2/en

active

Active

Patent Citations (17)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US6002965A

( en )

1998-06-10

1999-12-14

Katz; Amiram

Self applied device and method for prevention of deep vein thrombosis

US6360615B1

( en )

2000-06-06

2002-03-26

Technoskin, Llc

Wearable effect-emitting strain gauge device

US20070273504A1

( en )

2006-05-16

2007-11-29

Bao Tran

Mesh network monitoring appliance

US20080161731A1

( en )

2006-12-27

2008-07-03

Woods Sherrod A

Apparatus, system, and method for monitoring the range of motion of a patient's joint

US20090281412A1

( en )

2007-12-18

2009-11-12

Searete Llc, A Limited Liability Corporation Of The State Of Delaware

System, devices, and methods for detecting occlusions in a biological subject

<a href="/pa

Non-invasive wearable biomechanical and physiology monitor for injury … — George Mason Research Foundation, Inc. (US12011291B2)

Related documents