# US 12,191,018 B2 · System and method for using artificial intelligence in telemedicine-enabled hardware to optimize rehabilitative routines capable of enabling remote rehabilitative compliance

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- Number: US 12,191,018 B2
- Document ID: US12191018B2
- Status: Granted
- Kind: B2
- Application number: 17/146,705
- Filed: 2021-01-12
- Published: 2021-05-06
- Granted: 2025-01-07
- Earliest claimed: 2019-10-03
- Assignees: ROM Technologies, Inc.
- Applicants: Rom Technologies, Inc.
- Inventors: Steven Mason; Daniel Posnack; Peter Arn; Wendy Para; S. Adam Hacking; Micheal Mueller; Joseph Guaneri; Jonathan Greene
- Web page: https://patentgrep.com/patent/US12191018B2

## CPC

- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- A61H 1/0214: {by rotating cycling movement}
- A61H 1/024: {Knee (A61H1/0255 takes precedence)}
- A63B 22/0605: {performing a circular movement, e.g. ergometers}
- A63B 22/0694: {without integral seat, e.g. portable mini ergometers being placed in front of a chair, on a table or on a bed}
- A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance}
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- A61H 2201/1215: Rotary drive
- A61H 2201/1261: combined with active exercising of the patient
- A61H 2201/164: Feet or leg, e.g. pedal
- A61H 2201/501: connected to external computer devices or networks
- A61H 2201/5043: Displays
- A61H 2201/5061: Force sensors
- A61H 2201/5064: Position sensors
- A61H 2201/5069: Angle sensors
- A61H 2201/5071: Pressure sensors
- A61H 2201/5092: Optical sensor
- A61H 2201/5097: wireless
- A61H 2203/0431: in 90°/90°-position, like on a chair
- A61H 2205/10: Leg
- A63B 71/0622: {Visual, audio or audio-visual systems for entertaining, instructing or motivating the user}
- A63B 2022/0094: {for active rehabilitation, e.g. slow motion devices}
- A63B 2022/0623: {Cranks of adjustable length}
- A63B 2024/0065: {Evaluating the fitness, e.g. fitness level or fitness index}
- A63B 2071/0625: {Emitting sound, noise or music}
- A63B 2071/063: {Spoken or verbal instructions}
- A63B 2071/0655: {Tactile feedback}
- A63B 2071/0675: {Input for modifying training controls during workout}
- A63B 2071/068: {Input by voice recognition}
- A63B 2220/10: Positions
- A63B 2220/17: Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
- A63B 2220/20: Distances or displacements
- A63B 2220/30: Speed
- A63B 2220/40: Acceleration
- A63B 2220/51: Force
- A63B 2220/52: Weight, e.g. weight distribution
- A63B 2220/80: Special sensors, transducers or devices therefor
- A63B 2220/806: Video cameras
- A63B 2220/808: Microphones
- A63B 2220/833: Sensors arranged on the exercise apparatus or sports implement
- A63B 2225/09: Adjustable dimensions
- A63B 2225/093: Height
- A63B 2225/20: with means for remote communication, e.g. internet or the like
- A63B 2225/50: Wireless data transmission, e.g. by radio transmitters or telemetry
- A63B 2230/06: heartbeat rate only
- A63B 2230/207: P-O2, i.e. partial O2 value
- A63B 2230/30: blood pressure (A63B2230/04 takes precedence)
- A63B 2230/42: rate
- A63B 2230/50: temperature
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61H: PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY (electrotherapy, magnetotherapy, radiation therapy or ultrasound therapy A61N)
- A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H)

## Abstract

A computer-implemented system comprising a treatment apparatus, a patient interface, and a processing device is disclosed. The processing device is configured to receive treatment data pertaining to the user during the telemedicine session, wherein the treatment data comprises one or more characteristics of the user; determine, via one or more trained machine learning models, at least one respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data; determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and transmit the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user.

## Claims

### Claim 1

A computer-implemented system, comprising:
a device configured to be used by a user while performing an exercise plan;
an interface comprising an output device configured to present information associated with a communications session; and
a processing device configured to:
receive data pertaining to the user, wherein the data comprises one or more characteristics of the user;
determine, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens, wherein the determining the one or more probabilities is further based on the data; and
transmit the exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.

### Claim 2 (depends on 1)

The computer-implemented system of claim 1, wherein the processing device is configured to determine, via the one or more trained machine learning models, at least one respective measure of benefit that the one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the data, and wherein the respective measure of benefit may be an indication of change.

### Claim 3 (depends on 1)

The computer-implemented system of claim 1, wherein the processing device is further configured to control the device, based on the exercise plan, while the user uses the device.

### Claim 4 (depends on 1)

The computer-implemented system of claim 1, wherein the determining one or more probabilities is further based on:
(i) received feedback from the user, the another user, or both,
(ii) received feedback from the device used by the user, or
(iii) some combination thereof.

### Claim 5 (depends on 1)

The computer-implemented system of claim 1, wherein the processing device is further configured to:
receive user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.

### Claim 6

A computer-implemented, comprising:
a device configured to be used by a user while performing an exercise plan;
an interface comprising an output device configured to present information associated with a communications session; and
a processing device configured to:
receive data pertaining to the user, wherein the data comprises one or more characteristics of the user;
determine, via one or more trained machine learning models, one or more probabilities that the user will comply with one or more exercise regimens, wherein the determining the one or more probabilities is based on the data; and
transmit the exercise plan to a computing device, wherein the exercise plan is generated based on the one or more probabilities,
wherein the processing device is further configured to generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities that the user will comply with each of the one or more exercise regimens.

### Claim 7 (depends on 6)

The computer-implemented system of claim 6, wherein the exercise plan is generated using a non-parametric model, a parametric model, or a combination of both the non-parametric model and the parametric model.

### Claim 8 (depends on 6)

The computer-implemented system of claim 6, wherein the exercise plan is generated using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other mathematically-based prediction model.

### Claim 9 (depends on 1)

The computer-implemented system of claim 1, wherein the processing device is further configured to:
generate the exercise plan, wherein the generating is based on a plurality of factors comprising an amount of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, or some combination thereof.

### Claim 10 (depends on 1)

The computer-implemented system of claim 1, wherein the data further comprises one or more characteristics of the device.

### Claim 11

A computer-implemented method for optimizing an exercise plan for a user to perform using a device, the computer-implemented method comprising:
receiving data pertaining to the user, wherein the data comprises one or more characteristics of the user;
determining, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens; and
transmitting the exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, further comprising determining, via the one or more trained machine learning models, at least one respective measure of benefit the one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the data, and wherein the measure of benefit may be an indication of change.

### Claim 13 (depends on 11)

The computer-implemented method of claim 11, further comprising controlling, based on the exercise plan, the device while the user uses the device.

### Claim 14 (depends on 11)

The computer-implemented method of claim 11, wherein the determining the one or more probabilities is further based on:
(i) received feedback from the user, the another user, or both,
(ii) received feedback from the device used by the user, or
(iii) some combination thereof.

### Claim 15 (depends on 11)

The computer-implemented method of claim 11, further comprising:
receiving user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generating, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.

### Claim 16 (depends on 11)

The computer-implemented method of claim 11, further comprising generating, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities that the user will comply with each of the one or more exercise regimens.

### Claim 17 (depends on 16)

The computer-implemented method of claim 16, wherein, during generation of the exercise plan, the probability of the user complying is weighted more heavily or less heavily than another factor.

### Claim 18 (depends on 16)

The computer-implemented method of claim 16, wherein the exercise plan is generated using a non-parametric model, a parametric model, or a combination of both the non-parametric model and the parametric model.

### Claim 19 (depends on 16)

The computer-implemented method of claim 16, wherein the exercise plan is generated using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other mathematically-based prediction model.

### Claim 20 (depends on 11)

The computer-implemented method of claim 11, further comprising:
generating the exercise plan, wherein the generating the exercise plan based on a plurality of factors comprising an amount of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, or some combination thereof.

### Claim 21 (depends on 11)

The computer-implemented method of claim 11, wherein the data further comprises one or more characteristics of the device.

### Claim 22

A non-transitory, computer-readable medium storing instructions that, when executed, cause a processing device to:
receive data pertaining to a user, wherein the data comprises one or more characteristics of the user;
determine, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens; and
transmit an exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.

### Claim 23 (depends on 22)

The computer-readable medium of claim 22, wherein the processing device is configured to determine, via the one or more trained machine learning models, at least one respective measure of benefit the one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the data, and wherein the measure of benefit may be positive or negative.

### Claim 24 (depends on 22)

The computer-readable medium of claim 22, wherein the processing device is further configured to control, based on the exercise plan, a device while the user uses the device.

### Claim 25 (depends on 22)

The computer-readable medium of claim 22, wherein the determining the one or more probabilities is further based on:
(i) received feedback from the user, the another user, or both,
(ii) received feedback from the device used by the user, or
(iii) some combination thereof.

### Claim 26 (depends on 22)

The computer-readable medium of claim 22, wherein the processing device is further configured to:
receive user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.

### Claim 27 (depends on 22)

The computer-readable medium of claim 22, wherein the processing device is further configured to generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities of the user complying with each of the one or more exercise regimens.

### Claim 28

A system, comprising:
a memory device storing instructions; and
a processing device communicatively coupled to the memory device, the processing device executes the instructions to:
receive data pertaining to a user, wherein the data comprises one or more characteristics of the user;
determine, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens; and
transmit an exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.

### Claim 29 (depends on 28)

The system of claim 28, wherein the processing device is further configured to:
receive user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.

### Claim 30 (depends on 28)

The system of claim 28, wherein the processing device is further configured to generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities of the user complying with each of the one or more exercise regimens.