Training Apparatus Load Control via Displacement Waveform Analysis
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Solution Overview
Problem
Current training apparatuses lack an efficient method to accurately adjust the load during training, relying on human observation which is subjective and increases the burden on trainers, leading to inconsistent and suboptimal training outcomes.
Innovation Solution
A training apparatus equipped with a displacement unit, detection means, and arithmetic means that analyze the displacement waveforms to evaluate the load's appropriateness by identifying peak points, symmetry, and transition tendencies, allowing for automatic or guided load adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trainer visually observes exercise and communicates with the trainee to adjust load, then the load can be adjusted in consideration of comprehensive conditions, but the burden of the trainer is increased and the accuracy cannot be improved above a certain level
Solution Approach 1:
The training apparatus automatically evaluates the trainee's exercise state and determines appropriate load adjustments without requiring continuous manual observation and decision-making by the trainer. The system serves itself by using sensors to detect exercise data, processing units to analyze the data, and automatically providing load adjustment recommendations or controlling the load generation unit.
Solution Approach 2:
The manual observation and judgment system is replaced with an automated electronic system comprising sensors, processing units, and control mechanisms. The mechanical/manual process of visual observation and communication is substituted with electronic detection, data processing, and automated feedback mechanisms that objectively evaluate exercise state and determine load adjustments.
2Productivity
If the load is increased to achieve desired training effect, then the training effectiveness is improved, but the trainee may be unable to perform appropriate training action if the load is too heavy
Solution Approach 1:
The system continuously monitors exercise data through sensors and provides real-time feedback on the trainee's performance state. Based on this feedback, the processing unit evaluates whether the current load is appropriate and automatically adjusts the load generation unit to maintain optimal training intensity, preventing both excessive and insufficient loading.
Solution Approach 2:
The load generation unit is designed to dynamically adjust the training load in real-time based on the trainee's actual performance capability. The system transitions from static predetermined loads to dynamic adaptive loads that automatically respond to changes in the trainee's exercise state, ensuring the load remains within the optimal training zone.
3Adaptability or versatility
If manual observation and communication are used to adjust load, then comprehensive conditions including facial expression can be considered, but the process depends on individual ability of trainers leading to inconsistency
Solution Approach 1:
The subjective human judgment process is replaced with an objective electronic evaluation system. Sensors detect physiological and mechanical parameters, and processing units analyze this data using consistent algorithms, eliminating the variability introduced by different trainers' individual abilities and experiences.
Solution Approach 2:
The system transitions from qualitative subjective assessment to quantitative objective measurement by monitoring specific physiological and mechanical parameters. This parameter-based approach ensures consistent and reproducible load adjustment decisions across different trainers and training sessions.
Data Source
AI summary
The training apparatus includes a training machine, a detection means, and an arithmetic means. The training machine has a displacement unit displaced according to a training action and a load generation unit that imparts a load to a trainee. The detection means detects displacement of the displacement unit. The arithmetic means acquires at least one characteristic value possessed by a waveform corresponding to one unit of a training action which waveform is included in the displacement detected in the detection unit on a time axis, for each waveform, and to evaluate the acquired characteristic values for at least one of variation, transition tendency, and deviation from a predetermined value. The arithmetic means executes, based on the evaluation, at least one of control of the load generation unit, calculation of a load value set in the load generation unit, and notification of a result of the evaluation.