Prosthesis with Biosignal Prediction and Adaptive Impedance Control
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Solution Overview
Problem
Current prosthetic devices driven by actuators are expensive and limited in functionality, failing to provide economical and efficient replacement options for missing extremities with advanced control capabilities.
Innovation Solution
A prosthesis equipped with sensors for environmental and biosignal feedback, a prediction unit for autonomous motion control, and a control unit for regulating actuators, enabling natural and autonomous control of prosthetic links through a combination of sensor data, biosignal processing, and adaptive impedance regulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advanced actuators and sensors are integrated into the prosthesis to expand functionality, then the prosthesis achieves improved control capabilities and expanded functionality, but the procurement costs increase significantly
Solution Approach 1:
The prosthesis is divided into modular components including proximal and distal prosthetic links, actuators, sensors, and control units. This segmentation allows for standardized production of individual modules that can be assembled, reducing overall manufacturing complexity and cost while maintaining advanced functionality through selective module integration
Solution Approach 2:
The prosthesis incorporates multi-functional actuators and sensors that serve multiple purposes - actuators provide both actuation and structural support, sensors simultaneously monitor multiple parameters (position, force, acceleration), and the control unit executes multiple control algorithms. This multi-functionality reduces the total number of components needed, lowering procurement costs while expanding functionality
2Ease of operation
If multiple sensors and control units are integrated to enable autonomous control, then the prosthesis achieves natural control of movements, but the device complexity increases
Solution Approach 1:
Multiple control algorithms and sensor processing functions are merged into a single control unit that receives inputs from various sensors (joint sensors, output sensors, acceleration sensors, force sensors) and coordinates actuator movements. This consolidation reduces the complexity of distributed control systems while maintaining natural control capabilities through integrated processing
Solution Approach 2:
The control unit implements feedback mechanisms that continuously monitor sensor data (motor position, motor speed, output position, force) and adjust actuator commands accordingly. This feedback loop enables natural control by automatically compensating for deviations from desired trajectories without requiring complex manual intervention, simplifying the user interface while maintaining sophisticated control
3Measurement precision
If sensor data is processed in real-time for predictive control, then the prosthesis achieves improved response accuracy, but the computational load and processing time increase
Solution Approach 1:
The control unit performs preliminary processing of sensor data by filtering, smoothing, and pre-computing control commands before execution. Historical data is used to predict optimal control actions in advance, reducing real-time computational requirements while maintaining high response accuracy through predictive modeling rather than purely reactive control
Data Source
AI summary
Prosthesis including prosthetic links driven by actuators, first sensors that sense current state ZUS(t); second sensors that sense biosignals SIGBIO(t); third sensors that sense data DUMG(t); processing device; and memory storing instructions that, when executed by the processing device, perform operations including: determining based on SIGBIO(t), ZUS(t), and DUMG(t), model MA(t) of an action A, and predicting motions Beweg(MA(t)), dependent on MA(t) for a time period; determining a decision E to replace A with an action A′(E) based on SIGBIO(t), ZUS(t), DUMG(t), and Beweg(MA(t)) according to an evaluation scheme, wherein A′(E) can define a reflexive and/or protective motion, and if A′(E) does not define the reflexive and/or protective motion, then determining model MA′(t) of A′(E) and predicting motions Beweg(MA′(t)), dependent on MA′(t), for the time period; deriving control signals Sig(t) based on Beweg(MA(t)) or Beweg(MA′(t)), or based on the reflexive and/or protective motion, and controlling/regulating the actuators based on Sig(t).