AI Form Feedback Exercise Cabinet with 3D Camera
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Solution Overview
Problem
Conventional fitness equipment lacks real-time feedback and guidance for users to maintain proper form during exercises, leading to potential injuries and ineffective workouts.
Innovation Solution
A free-standing A-frame exercise equipment cabinet equipped with a computer vision and machine learning-based system that uses a 3D camera and sensors to provide real-time feedback on form, count repetitions, and recommend weight adjustments, integrating a touchscreen display for interactive workouts and a leaderboard system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fitness equipment is used, then the equipment structure remains simple, but real-time form feedback and guidance are lacking leading to potential injuries
Solution Approach 1:
The patent implements real-time feedback through computer vision cameras that capture user movements, machine learning models that analyze form correctness, and immediate visual/audio feedback displayed on screens. This closed-loop feedback system continuously monitors exercise form and provides corrective guidance, resolving the contradiction by adding intelligence rather than mechanical complexity.
Solution Approach 2:
The patent replaces traditional mechanical feedback mechanisms (such as physical guides or instructors) with optical and computational systems. Computer vision cameras and machine learning algorithms substitute for human trainers, providing automated form analysis without requiring complex mechanical structures or physical intervention.
2Productivity
If no real-time feedback system is implemented, then the equipment remains simple, but workout effectiveness decreases due to improper form
Solution Approach 1:
The system enables users to self-correct their form through automated computer vision analysis and real-time feedback. The machine learning model acts as a virtual trainer that continuously evaluates user movements and provides guidance without requiring external intervention, allowing users to independently improve workout effectiveness.
Solution Approach 2:
The system performs preliminary analysis of proper exercise form through machine learning models trained on correct movement patterns. Before users execute exercises, the system establishes reference models of proper form, enabling real-time comparison and correction during exercise execution, thus preventing ineffective movements before they occur.
3Reliability
If manual form monitoring is used, then the system remains simple, but continuous real-time guidance is unavailable leading to form degradation
Solution Approach 1:
The patent implements continuous monitoring through multiple cameras operating at high frame rates, with machine learning models analyzing every movement phase in real-time. The system maintains uninterrupted form assessment throughout the entire exercise routine, providing continuous corrective feedback rather than periodic or manual checks, ensuring form accuracy is maintained throughout the workout.
Solution Approach 2:
The patent introduces computer vision algorithms and machine learning models as intermediaries between the user and the feedback system. These intelligent intermediaries automatically capture, analyze, and translate raw camera data into actionable form feedback, eliminating the need for direct human observation while maintaining high accuracy in form assessment.
4Measurement precision
If repetitive exercises are performed without tracking, then the workout routine remains simple, but repetition accuracy and progression are difficult to measure
Solution Approach 1:
The patent replaces manual repetition counting with automated computer vision tracking. Machine learning models analyze video data to detect and count repetitions, measuring exercise volume with high precision. This substitution of mechanical/manual counting with optical and computational methods resolves the contradiction by adding intelligent tracking without significant mechanical complexity.
Data Source
AI summary
An artificial intelligence-based exercise apparatus comprising an exercise support member configured to support a user; a plurality of exercise resistance components configured to provide resistance in one or more of a vertical direction, a horizontal direction, and an angled direction; a three-dimensional camera; and a computer component portion configured to include one or more processors and memory. The memory stores instructions that, when executed by the one or more processors, cause the apparatus to perform tracking, using the three-dimensional camera and a machine learning model, the user's motion over a period of time; detecting, based on the plurality of exercise resistance components, a rate of motion of the user over the period of time; and dynamically adjusting, based on the tracked user's motion over the period of time and the detected rate of motion of the user over the period of time, a resistance value of at least one of the one or more exercise components in any of the vertical direction, the horizontal direction, and the angled direction.