Workout recommendation engine
Inventors
Rauhala, Kari Kristian • Franchomme, Eric • Peng, Micah Kendrick • Arvidsson, Sam Daniel • Dembowski, Anton • Altin, Gregory John
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Assignees
Pear Health Labs Inc • Pear Sports LLC
MemberPear Health LabsPear Health LabsPear Health Labs develops digital health and fitness software for organizations. Its platforms combine health protocols, assessments, biometric and wearable data, and user feedback to provide adaptive exercise, nutrition, recovery, and wellness guidance. The company also describes integrations with mobile and web applications, electronic health records, and wearable devices. Applications include general wellness, healthy aging, Medicare fitness programs, GLP-1 treatment support, musculoskeletal health, maternal and postpartum health, and military readiness.
Pear Health Labs develops digital health and fitness software for organizations. Its platforms combine health protocols, assessments, biometric and wearable data, and user feedback to provide adaptive exercise, nutrition, recovery, and wellness guidance. The company also describes integrations with mobile and web applications, electronic health records, and wearable devices. Applications include general wellness, healthy aging, Medicare fitness programs, GLP-1 treatment support, musculoskeletal health, maternal and postpartum health, and military readiness.
Publication Number
US-11673025-B2
Publication Date
2023-06-13
Expiration Date
Abstract
A system and method for providing recommendations for a workout by a user. The system includes a workout engine configured to select the workout for the user based on user input and/or historical data, and one or more data sources, each of the one or more data sources providing information associated with the user and/or the workout. The system further includes a recommendation engine configured to receive the information associated with the user and/or the workout from the one or more data sources, and to generate one or more recommendations about the workout and/or the user before, during and/or after the workout by the user, the one or more recommendations being configured to optimize the performance of the workout by the user.
Core Innovation
The invention discloses a workout recommendation engine/system that selects a workout for a user using one or more of a user input or historical data associated with the user. A workout engine selects the workout from the user input and/or historical data, and one or more data sources provide information associated with the user or the workout. A recommendation engine generates recommendations before, during, or after the workout based on the workout and/or user information.
The invention further includes authenticating a movement of the user by generating, based on historical data, an image corresponding to a set of movements of the user. Performance data is received and measured via one or more sensors for the workout having an adjusted duration, and a movement of the user is identified from the performance data. Authentication is performed by comparing at least a portion of the performance data to at least a portion of the set of movements of the image associated with the user.
The system is configured to adjust performance data associated with the workout by generating one or more recommendations that are displayed to the user. The recommendations are configured to respond to real-time monitoring and evaluation of the measured performance data, and the adjusted duration of the workout is set based on information relating to a calendar of the user. The disclosed implementations also reference additional sensing and data sources to support recommendation generation.
Claims Coverage
The independent claim coverage centers on combining workout selection from user input and/or historical data, movement-image generation and movement authentication via comparison, and calendar-based adjusted duration with sensor-measured performance data to generate and display before/during/after workout recommendations configured to adjust performance data. Dependent refinements specify additional data sources and sensing features.
Workout selection from user input or historical data
Selecting a workout for a user based on one or more of a user input or historical data associated with the user.
Movement image generation from historical movement data
Generating, based on historical data, an image corresponding to a set of movements of the user.
Calendar-derived adjusted workout duration
Receiving information relating to a calendar of the user from a mobile device and setting, based on the calendar information, an adjusted duration of the workout.
Sensor-measured performance data and movement identification
Measuring, via a sensor, performance data of the user relating to the workout having the adjusted duration and identifying a movement of the user from the performance data.
Movement authentication via comparison to movement-image data
Authenticating the movement of the user by comparing at least a portion of the performance data to at least a portion of the set of movements of the image associated with the user.
Before/during/after workout recommendations configured to adjust performance data
Generating, at least one of before, during, or after the workout, one or more recommendations associated with one or more of the workout or the user, the one or more recommendations being configured to adjust performance data associated with the workout and displaying the one or more recommendations.
Topographical information as input to the recommendation system
Including topographical information about the workout’s geographical environment as part of the information associated with one or more of the user or the workout from one or more data sources.
Genetic information and/or genetic markers as input
Providing genetic information and/or one or more genetic markers of the user as part of the information associated with one or more of the user or the workout from one or more data sources.
Performance-data-driven workout audio generation
Selecting an audio file associated with the workout based on performance data and generating the workout-associated audio using an audio processor.
Wearable garment with embedded sensors
Forming one or more additional sensors in a wearable garment configured to be worn by the user during the workout.
The claim coverage centers on selecting a workout using user input or historical data, generating an image of a set of user movements from historical data, measuring performance data for a calendar-adjusted workout duration, identifying a movement, and authenticating the movement by comparing the performance data to the movement-image data. The method then generates and displays before/during/after recommendations configured to adjust performance data, with dependent refinements adding topographical and genetic inputs, performance-data-driven audio, and wearable-embedded sensors.
Stated Advantages
Documented Applications
No documented applications found
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