Exercise Form Analysis Using Motion Sensors and Neural Networks

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Solution Overview

Problem

At-home workout participants lack feedback on their exercise form, which can lead to reduced effectiveness and increased risk of injury, as they do not receive guidance or comparison with others.

Innovation Solution

A method and system that provides guided exercise routines via a digital network, using wearable devices with motion sensors to track and analyze user performance, and a neural network to calculate performance scores and generate feedback based on expert reviews.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If at-home workout routines are provided without trainer feedback, then convenience and accessibility are improved, but exercise form accuracy and safety deteriorate

Engineering Contradiction:

Improveconvenience of at-home workoutsVSAvoidexercise form accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system implements automatic feedback mechanisms through motion sensors that track user movements and provide real-time form correction, replacing the need for human trainers while maintaining exercise accuracy and safety

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The workout system performs self-assessment of exercise form through integrated sensors and algorithms, allowing the system to automatically evaluate and correct user technique without external trainer intervention

Inventive Principle:

Principle #25Self-service

2Measurement precision

If motion sensors and neural networks are implemented to provide exercise feedback, then exercise form accuracy is improved, but device complexity increases

Engineering Contradiction:

Improveexercise form measurement accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The wearable device integrates multiple functions including motion sensing, data processing, and feedback delivery in a single unit, reducing the need for separate complex systems while maintaining high measurement precision

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system replaces complex mechanical assessment methods with electronic sensors and neural network algorithms, achieving high measurement precision through software-based analysis rather than mechanical means

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20220176201A1Methods and systems for exercise recognition and analysis

Publication Date: 2022.06.09 ALIVE FITNESS LLC

AI summary

A method includes providing information to a user about available guided exercise routines. The method further includes receiving from the user a selection of one of the available guided exercise routines. The method further includes providing digital and audio content comprising the selected guided exercise routine to the user. The method further includes receiving motion data from a at least one motion sensor worn by the user while performing exercises associated with the selected guided exercise routine. The method further includes identifying repetitions of an exercise being performed by the user. The method further includes calculating a performance score based on the motion data received from the motion sensor using a neural network trained using feedback provided by one or more expert reviewers based on review of video of one or more training users performing the exercise. The method further includes displaying the performance score on the display unit.