Biomechanical Motion Monitoring With AI Feedback for Fall Risk
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Solution Overview
Problem
Current systems for individuals with physical disabilities or elderly users using prosthetic limbs lack real-time, intelligent feedback tailored to their specific motion patterns, failing to promptly detect inappropriate actions or potential hazards such as falls, leading to reduced quality of life and increased risk of injury.
Innovation Solution
A system comprising a processor that preprocesses motion data from biomechanical sensors, analyzes it using generative artificial intelligence, and provides real-time instructions via visual or audio notifications based on predefined criteria, utilizing a recurrent neural network to detect inappropriate movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time motion analysis using generative AI is implemented, then user safety and detection precision are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent introduces a processor as an intermediary component that bridges the biomechanical sensors and the generative AI model. This processor preprocesses sensor data into standardized formats before feeding them to the AI model, and another processor generates natural language feedback from AI predictions. This intermediary approach enables complex AI-based safety monitoring without requiring the entire system to be equally complex, thus resolving the contradiction between improved user safety and reduced device complexity.
2Speed
If real-time feedback is provided to users, then responsiveness and user safety are improved, but information processing time and system complexity increase
Solution Approach 1:
The patent implements preliminary action by preprocessing motion data in real-time as it is collected from sensors, rather than waiting to process bulk data. The system continuously transforms raw sensor readings into standardized formats and feeds them to the generative AI model incrementally. This allows the system to maintain high responsiveness by providing feedback as soon as inappropriate movements are detected, without waiting for complete data sets, thus resolving the contradiction between fast feedback and processing time.
3Measurement precision
If motion data is continuously monitored and analyzed, then detection precision is improved, but energy consumption increases
Solution Approach 1:
The patent applies local quality by selectively processing and analyzing only the specific motion parameters that are relevant to detecting inappropriate movements, rather than continuously analyzing all sensor data at full resolution. The system focuses computational resources on critical movement patterns identified by the generative AI model, transforming and analyzing only the necessary portions of motion data. This selective approach maintains high detection precision for safety-critical movements while reducing overall energy consumption compared to continuous full-scale monitoring.
Data Source
AI summary
A system includes a processor that is configured to preprocess motion data received from biomechanical sensors, analyze the preprocessed motion data using a generative artificial intelligence to detect inappropriate movements or risks, and provide instructions to a user based on analysis results by the generative artificial intelligence.