Training System Optimizing Load via Cumulative Sensor Analysis
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Solution Overview
Problem
Existing training systems fail to provide personalized and adaptive training advice that considers historical mechanical and physiological data to optimize training sessions, leading to potential injuries from cumulative overload or inadequate load stimulation.
Innovation Solution
A system that uses historical sensor data to calculate cumulative load parameters, determining the frequency and type of training sessions based on mechanical and physiological loads, and adjusts current sessions in real-time to prevent injuries and enhance performance by balancing impact and cardiovascular load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training intensity and frequency are increased to improve performance, then training effectiveness is improved, but risk of injury from cumulative overload increases
Solution Approach 1:
The system continuously monitors mechanical load parameters (acceleration, impact, distance) and physiological parameters (heart rate) during training sessions, storing this data in a log file. The training advice module retrieves historical data to calculate cumulative load and provides real-time feedback, adjusting training recommendations to prevent overload while maintaining effectiveness.
Solution Approach 2:
The system calculates cumulative load parameters based on historical training data before recommending the next training session. By analyzing past mechanical and physiological loads, the system proactively determines safe training intensity and frequency, preventing injury before it occurs rather than reacting after damage has happened.
2Productivity
If training load is increased to stimulate biomechanical loadability, then performance improvement is enhanced, but mechanical stress on the body increases
Solution Approach 1:
The system dynamically adjusts training recommendations based on real-time mechanical load parameters (acceleration, impact peaks, distance) and physiological data. The training advice module modifies training intensity, duration, and type according to the user's current condition and historical response, optimizing performance stimulation while controlling mechanical stress.
Solution Approach 2:
The system changes training parameters (intensity, duration, frequency, type) based on calculated cumulative load parameters and mechanical load history. By varying these parameters dynamically, the system stimulates biomechanical loadability for performance improvement while preventing excessive mechanical stress that could lead to injury.
3Adaptability or versatility
If historical sensor data is collected and analyzed to provide personalized training advice, then training optimization is improved, but system complexity increases
Solution Approach 1:
The system automatically collects, stores, and analyzes mechanical load parameters (acceleration, impact, distance) and physiological data from sensors. The training advice module autonomously processes this historical data to generate personalized training recommendations without requiring manual intervention, simplifying the user interface while maintaining sophisticated analysis capabilities.
Solution Approach 2:
The system uses an intermediary processing layer (the training advice module) that separates data collection from data analysis and recommendation generation. This modular architecture manages complexity by dividing functions into distinct components: sensor data acquisition, historical data storage, cumulative load calculation, and training recommendation generation.
Data Source
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AI summary
System for training optimisation, the system comprising: at least one sensor for measuring a mechanical load parameter which is indicative for a mechanical load of the training a storage module for storing data which are dispatched by the least one sensor in a log file; a training advice module which is arranged for determining a personal training advice related to a load assessment of the user based on at least the data stored in the log file, the data comprising a cumulative load parameter or a training load history determined on the basis of historical sensor data stored in the log file, the training advice comprising the frequency of next training sessions and/or a type of training to be performed in the next training session; at least one output device such as a display, a sound signal, audio output, voice output or a vibrating element for outputting said training advice to said user.