Wearable Intermediary for Accurate Bodyweight Exercise Tracking
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Solution Overview
Problem
Current systems struggle to accurately and efficiently track and store training data for users performing exercises with or without exercise machines, especially bodyweight exercises, due to the need for expensive digital image processing and computational resources, leading to incomplete and unreliable data.
Innovation Solution
A system comprising a central data processing unit, image acquisition devices, and memory units that acquire and process images to determine and store training data for multiple users in a training space, allowing for the tracking of exercises with or without machine usage, using image processing algorithms and user identification codes to provide complete and accurate data without relying on expensive computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital image processing algorithms are used to monitor training space and acquire training data from images, then training data can be obtained for bodyweight exercises and exercises without machine usage, but computational cost and processing complexity increase significantly
Solution Approach 1:
The system segments the training space into multiple zones and assigns specific image acquisition devices to each zone. This allows distributed data collection where each device handles only its local area, reducing the computational burden on any single processing unit while maintaining comprehensive coverage of all bodyweight exercise areas.
Solution Approach 2:
The patent introduces wearable devices as intermediaries that users wear during exercise. These devices directly capture exercise data (such as movement, heart rate, repetitions) without requiring complex image processing of the entire training space. The wearable acts as a mediator between the user's physical activity and the central system, dramatically reducing computational requirements while maintaining data accuracy.
2Reliability
If multiple cameras are deployed to monitor large number of users training simultaneously, then complete training data can be captured for all users, but computational resources and processing time increase exponentially
Solution Approach 1:
The training space is divided into multiple monitored zones, each with its own image acquisition device. Each device independently processes only the users within its specific zone, rather than all users across the entire facility. This segmentation maintains complete data capture for each user while distributing computational energy consumption across multiple lower-power devices.
Solution Approach 2:
Wearable devices perform self-service by autonomously capturing and processing exercise data locally on the user's body. The wearable independently tracks movements, counts repetitions, and monitors physiological parameters without requiring external camera systems to process every aspect of each user's exercise. This eliminates the need for high computational energy to analyze multiple camera feeds for each user.
3Reliability
If users manually upload exercises performed via electronic devices, then training data can be recorded for bodyweight exercises, but time consumption and user effort increase significantly
Solution Approach 1:
The wearable device automatically and continuously captures exercise data during the workout without requiring user intervention. It self-services by detecting movements, counting repetitions, and recording physiological parameters in real-time. After the exercise session, the data is automatically transmitted to the training program system, eliminating the time-consuming manual upload process entirely while maintaining complete traceability of all exercises performed.
4Measurement precision
If sophisticated digital image processing algorithms are implemented to process images from multiple cameras, then accurate training data can be extracted, but system cost and computational requirements become prohibitive
Solution Approach 1:
Wearable devices serve as intermediaries that directly capture exercise metrics with high precision sensors built into the device itself. Rather than using complex image processing algorithms to infer exercise data from camera images, the wearable directly measures movements, heart rate, and other physiological parameters. This intermediary approach achieves measurement precision equivalent to or better than image processing while dramatically reducing system implementation cost.
Solution Approach 2:
The system uses relatively simple, low-cost image acquisition devices positioned throughout the training space rather than expensive high-resolution cameras with sophisticated processing requirements. Combined with wearable devices that perform the heavy measurement lifting, the overall system achieves accurate training data extraction at a fraction of the cost of a comprehensive multi-camera image processing system.
Data Source
AI summary
A system for determining and storing training data of a plurality of users in a training space has at least one central data processing unit, at least one central memory unit and at least one image acquisition device. The central data processing unit identifies, for each user, when the user performs a physical exercise identified for the user within a set training program, a next physical exercise for the user to be performed, based on a respective user identification code and at least one of: whether a determined value representative of a time elapsed since the user started performing the physical exercise reaches a respective time target provided by the physical exercise, a determined value representative of a position of the user within the training space, whether a determined value representative of a training parameter provided by the physical exercise being performed by the user reaches a respective training target.