Training Machine Resistance Adjustment With Physiological Feedback
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Solution Overview
Problem
Existing training machines lack the ability to adapt resistance levels and user support elements based on real-time physiological data, such as heart rate and skeletal movements, leading to suboptimal workout efficiency and potential injury risks.
Innovation Solution
A training machine assembly equipped with sensors and actuators that automatically adjust resistance and user support elements using heart-rate detection, skeletal analysis, and trajectory tracking, combined with a control system to optimize exercise performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If training machines use fixed resistance levels and static user support elements, then device complexity is reduced, but workout efficiency and adaptability deteriorate
Solution Approach 1:
The training machine implements dynamic adjustment of resistance levels and user support element positions based on real-time physiological data from sensors. The resistance force and support elements transition from static to dynamically adjustable parameters that adapt during exercise execution, resolving the contradiction between adaptability and complexity by making the system responsive only when physiological thresholds are exceeded.
Solution Approach 2:
The system incorporates sensors that continuously monitor physiological parameters (heart rate, skeletal movements) and feed this data back to the control system. This feedback loop enables automatic adjustment of training parameters, achieving adaptability through a closed-loop control mechanism that manages complexity through automated decision-making rather than manual intervention.
2Productivity
If training machines automatically adjust resistance and support elements based on real-time physiological data, then workout efficiency and safety are improved, but device complexity increases
Solution Approach 1:
The training machine performs self-adjustment of resistance and support elements based on automated analysis of physiological data. The system serves itself by automatically detecting when adjustment is needed and executing the adjustment without user intervention, thereby improving workout efficiency while managing complexity through automation rather than requiring complex manual control interfaces.
Solution Approach 2:
The patent replaces manual mechanical adjustment mechanisms with automated systems that use sensors to detect physiological states and actuators to execute adjustments. This substitution of manual mechanical systems with sensor-actuator systems achieves higher productivity through automated, real-time adaptation while consolidating complexity into integrated control modules.
3Adaptability or versatility
If training machines use manual adjustment of resistance levels, then ease of operation is maintained, but adaptability to physiological changes deteriorates
Solution Approach 1:
The training machine automatically adjusts resistance and support elements based on physiological feedback without requiring user intervention. The system serves itself by detecting physiological thresholds and executing adjustments autonomously, thereby achieving real-time adaptability while maintaining ease of operation through complete automation of the adjustment process.
Solution Approach 2:
The system uses physiological sensors to continuously monitor user state and automatically adjusts training parameters based on this feedback. This eliminates the need for manual adjustment while achieving superior adaptability, as the system responds automatically to physiological changes without requiring user awareness or action.
4Object-affected harmful factors
If training machines limit force at reversal points to prevent injury, then safety is improved, but training effectiveness may deteriorate
Solution Approach 1:
The resistance force transitions from static to dynamic, with the control system automatically adjusting the magnitude of resistance based on real-time physiological data and exercise phase. At reversal points, the system dynamically reduces force to prevent injury, while during safe phases, full resistance is applied to maintain training effectiveness, thus resolving the contradiction through context-dependent adaptation.
Solution Approach 2:
The system proactively limits force at reversal points before injury can occur, based on predictive analysis of exercise trajectory and physiological state. By anticipating dangerous conditions and preemptively adjusting resistance, the system prevents injury while maintaining training effectiveness during safe portions of the exercise motion.
Data Source
AI summary
A training machine assembly comprises at least one control device and at least one training resistance. Each of the at least one training resistance comprises at least one training resistance value, such as a force applied towards the user contact element, e.g. a handle. The training resistance value can also comprise a function or a vector, for example a function linking a speed of movement of a user and/or a user contact element and a force applied against said movement. The control device can be a control device for controlling the training machine assembly. The training resistance can comprise an actuator. The actuator can comprise an electric motor. The training resistance can comprise a weight. The training resistance can comprise an element configured to provide a resistance against a movement of the user. The training machine assembly can comprise at least one camera.