Adaptive Active Training System with Physiological Feedback
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Solution Overview
Problem
Existing training systems, such as passive gait rehabilitation methods and limited biofeedback-based systems, fail to effectively adjust training intensity based on a user's physiological signals, leading to suboptimal training outcomes.
Innovation Solution
An adaptive active training system comprising a motion module, sensing module, and control module that adjusts training intensity by sensing physiological signals, calculating threshold values, and dynamically modifying these values based on user feedback to ensure appropriate training intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If passive training method is used with motor-driven lower limb support frame, then the training can be performed with automated motion control, but the training effect is poor compared to active training method
Solution Approach 1:
The system employs real-time feedback by detecting physiological signals (electromyographic signals) from the user's muscles during training. The control module continuously monitors these signals and adjusts the motor-driven training apparatus accordingly, creating a closed-loop control system that adapts to the user's actual physical state rather than following a predetermined passive motion pattern
Solution Approach 2:
The training system transitions from static, pre-programmed motion trajectories to dynamic, real-time motion control. The motor-driven lower limb support frame adjusts its motion parameters (speed, amplitude, frequency) dynamically based on the detected physiological signals, allowing the training intensity and characteristics to change adaptively during the training session
2Ease of manufacture
If standard model of walking trajectory is used to guide user movement, then the training can be standardized, but the model is not applicable to every individual due to differences between individuals
Solution Approach 1:
The system modifies the standard walking trajectory parameters (amplitude, frequency, speed, timing) in real-time based on individual physiological responses. By detecting electromyographic signals and analyzing muscle activation patterns, the control module adjusts motion parameters to match each user's unique physical characteristics and capabilities, transforming a standardized model into a personalized training program
3Ease of operation
If training intensity is fixed without physiological signal monitoring, then the system is simpler to operate, but the training intensity cannot be adjusted to prevent excessive or insufficient loads
Solution Approach 1:
The training system performs self-adjustment by automatically monitoring the user's physiological state through electromyographic signal detection and autonomously modifying training parameters. The control module processes the physiological data and adjusts motor control signals without requiring manual intervention from a trainer or the user, enabling the system to self-optimize training intensity based on real-time feedback
Data Source
AI summary
An adaptive active training system includes a motion module, a sensing module and a control module. The motion module includes a training unit and a motor connected to the training unit. The motor is configured to bring the training unit to move along a motion trajectory. The sensing module is configured to sense a physiological signal of a user when the user uses the training unit. The control module is connected to the motion module and the sensing module. The control module is configured to calculate a position of the training unit on the motion trajectory, obtain a threshold value corresponding to the position based on a motion model, and determine whether a magnitude of the physiological signal is greater than the threshold value.