Smart Gym Caloric Tracking via Skeletal Analysis
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Solution Overview
Problem
Traditional fitness trackers are unable to accurately track caloric expenditure during free weight and floor exercises due to their limited ability to monitor total body movement and weight changes, rendering them ineffective for comprehensive fitness tracking.
Innovation Solution
A smart gym system equipped with cameras and weight sensors that capture skeletal movements and weight changes, using deep learning and computer vision to analyze joint movements and calculate caloric expenditure, while also enabling virtual coaching and augmented reality training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fitness trackers use single-point movement sensing, then device complexity is reduced, but measurement precision for total body movement and caloric expenditure is insufficient
Solution Approach 1:
The system divides the body into multiple skeletal segments (head, torso, arms, legs) with multiple joints, and places sensors at key locations to measure movement of each segment independently. This segmentation allows accurate tracking of complex multi-joint exercises while keeping each sensor unit relatively simple.
Solution Approach 2:
The patent combines multiple sensor types (accelerometers, gyroscopes, magnetometers) into an integrated wearable unit that can simultaneously measure linear acceleration, angular velocity, and orientation. This merging of sensing functions into a single device achieves comprehensive movement tracking without requiring multiple separate systems.
2Adaptability or versatility
If traditional fitness trackers monitor only biometric data, then ease of operation is maintained, but adaptability to track diverse exercise types (free weight, floor exercises) is limited
Solution Approach 1:
The system automatically identifies exercise types and calculates caloric expenditure by analyzing movement patterns and skeletal configurations without requiring manual input from the user. The device self-determines the exercise being performed based on sensor data, eliminating the need for users to select from predefined exercise lists.
Solution Approach 2:
The system dynamically adjusts its measurement parameters and analysis algorithms based on the detected exercise type. Different exercise modalities (free weight, floor exercises, machine exercises) trigger different processing routines, allowing the system to adapt its complexity level to match the exercise being performed.
3Measurement precision
If traditional trackers lack skeletal configuration analysis, then device complexity is minimized, but measurement precision for tracking weight movement and repetitions is insufficient
Solution Approach 1:
The system transitions from measuring movement at a single point to analyzing movement across multiple dimensions by tracking the three-dimensional positions and orientations of multiple body joints simultaneously. This spatial dimensionality enables accurate detection of skeletal configurations and weight movements that single-point sensors cannot detect.
Data Source
AI summary
An apparatus for a smart gym is described herein. The smart gym includes a platform and at least one camera. The platform comprises at least a weight sensor. The at least one camera is configured to capture movements on the platform. In some cases, an exercise is identified by comparing the joint movement to a known joint movement associated with the exercise.