Adaptive Training Program Adjustment via Real-Time Physiological Assessment
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Solution Overview
Problem
Existing physical exercise training programs are not responsive to an individual's current physiological state, leading to potential injuries and setbacks, as they often rely on generic data and are not conveniently adaptable to daily changes in a user's functional state.
Innovation Solution
A system and method that uses a sensor and transmitter unit connected to a mobile device to assess the user's functional state and workload in real-time, adjusting the training program accordingly to provide a customized and effective workout experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic training programs based on historical empirical data are used, then training structure is provided, but the program is not responsive to individual physiological state changes
Solution Approach 1:
The training program transitions from a static, pre-defined schedule to a dynamic system that continuously adapts based on real-time physiological assessments. The processor automatically modifies training parameters (intensity, duration, type) according to current functional state measurements, making the program responsive to daily variations in the user's condition.
Solution Approach 2:
The system implements continuous feedback loops where physiological state is assessed through multiple measurements (heart rate variability, HR response to load, power output, speed, distance). This feedback is processed to determine current functional state, which then feeds back into automatic adjustment of the training program, creating a closed-loop adaptive system.
2Measurement precision
If multiple assessments are conducted to analyze training program and physiological state, then assessment accuracy is improved, but time consumption increases
Solution Approach 1:
The system combines multiple assessment methods (HRV analysis, HR response to load testing, power output measurement, speed and distance tracking) into a unified assessment framework. These measurements are integrated and processed together to comprehensively determine functional state, achieving high assessment accuracy through combination rather than sequential separate tests.
Solution Approach 2:
The system performs preliminary assessments continuously during normal activity (resting HRV, baseline measurements) so that when formal training sessions occur, the functional state is already characterized. This eliminates the need for extensive pre-training assessments and allows immediate program adjustment based on accumulated data.
3Reliability
If functional state assessment is performed continuously, then training safety is improved, but device complexity increases
Solution Approach 1:
The system uses a multi-functional wearable device that combines multiple sensing capabilities (heart rate monitoring, acceleration sensors, GPS tracking, power measurement) into a single platform. This universal device performs both continuous safety monitoring and training performance tracking, reducing overall system complexity compared to separate specialized devices.
Solution Approach 2:
The system automatically processes assessment data, determines functional state, and adjusts training programs without requiring user interpretation or manual intervention. The processor autonomously analyzes the complexity of functional state changes and modifies training parameters accordingly, making the safety monitoring self-executing rather than requiring user expertise.
Data Source
AI summary
A system and method for training program generation and modification of that program based on assessed functional state and/or workload performance. User-interface logic preferably operating on a mobile device permits a user to record bio-signals indicative of functional state. Assessment-adjustment logic, that may be located at a distance, conducts body system assessments from the received bio-signal data and produces training session targets based on the current functional state of the user. User training objective data may be input through the user-interface logic. Workload performance may be monitored and the training session targets modified based on measured past performance to improve future performance. Various embodiments are disclosed.