Virtual Trainer Optimizer Using Motion Sensing
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Solution Overview
Problem
Current virtual training systems lack the ability to determine and optimize the exertion level of exercisers, leading to inefficient workouts and potential health risks, as they cannot adjust the intensity of exercises based on individual fitness levels or health conditions.
Innovation Solution
A computer-implemented virtual training method and system that uses image capturing devices to sense and process exerciser motion, determining exertion levels and adjusting the exercise routine via audio and video instructions to maintain an optimum exertion level, incorporating modules for cardio-respiratory monitoring, fall detection, and anomaly detection to ensure safety and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a virtual trainer system uses generic video instructions without customization, then the system cost is reduced, but the workout efficiency and safety decrease due to lack of form inspection and exertion level monitoring
Solution Approach 1:
The system enables self-service by allowing the exerciser to independently monitor and adjust their own exertion level through real-time feedback from sensors and video analysis, eliminating the need for expensive personal trainers while maintaining workout efficiency and safety
Solution Approach 2:
The system implements continuous feedback loops where sensors and video analysis monitor form and exertion level, providing real-time corrections and adjustments to the exerciser, enabling customization without human intervention and maintaining high workout efficiency at low cost
2Reliability
If a virtual trainer system inspects form and monitors exertion level like a personal trainer, then workout safety and efficiency improve, but the system complexity and cost increase
Solution Approach 1:
The system achieves multi-functionality by integrating form inspection, exertion level monitoring, real-time feedback, and workout customization into a single unified virtual trainer platform, improving safety without proportionally increasing complexity through shared hardware and software resources
Solution Approach 2:
The system uses sensors and video analysis algorithms as intermediaries to objectively measure form and exertion level, replacing the need for human trainer intervention while maintaining high safety standards through automated detection and feedback mechanisms
3Productivity
If the exerciser performs at higher exertion level to maximize fitness progress, then workout effectiveness increases, but the risk of injury and health complications increases
Solution Approach 1:
The system implements real-time feedback on exertion level through sensor data and video analysis, providing immediate warnings and corrections when the exerciser approaches dangerous intensity thresholds, enabling safe maximization of fitness progress through continuous monitoring and adjustment
Solution Approach 2:
The system applies preliminary anti-action by proactively detecting and preventing dangerous exertion levels before they cause injury, using predictive algorithms and real-time monitoring to stop or modify workouts before harm occurs, thereby protecting health while maximizing safe fitness progress
Data Source
AI summary
This disclosure provides an augmented Virtual Trainer (VT) method and system. According to an exemplary system, a video based physiological metric system is integrated with a VT system to provide health and/or safety related data associated with a user of the VT system. According to an exemplary embodiment, the disclosed augmented VT system modifies an exercise routine based on the physiological metrics and/or provides audio signals to the user.