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Continuous Multi-DoF Wrist Kinematics Estimation Based on a Human-Machine Interface With
Enhao Zheng1, Jingzhi Zhang1,2, Qining Wang3
1The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Frontiers in Neurorobotics
|October 18, 2021
Summary
This study introduces a novel electrical impedance tomography (EIT) interface for continuous wrist angle estimation. The system accurately measures deep muscle spatial information, achieving high precision in multiple degree-of-freedom (DoF) tasks.
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Rehabilitation Robotics
Background:
- Existing muscle-signal-based sensors have limited measurement scope.
- Continuous wrist angle estimation is crucial for human-computer interaction and rehabilitation.
Purpose of the Study:
- To propose a novel electrical impedance tomography (EIT) interface for multiple degree-of-freedom (DoF) continuous wrist angle estimation.
- To assess the efficacy of the EIT interface in capturing deep muscle spatial information.
Main Methods:
- Developed a soft elastic fabric sensing band integrating an EIT interface.
- Designed an estimation algorithm utilizing kernel functions for mutual correlation extraction and regularization for optimal coefficient selection.
- Evaluated the method on 12 healthy subjects performing six basic wrist joint motions.
Main Results:
- Achieved an average root-mean-square error of 7.62° for 3-DoF estimation.
- Obtained an average R² of 0.92, indicating high accuracy.
- Demonstrated comparable performance to state-of-the-art surface electromyography (sEMG) methods in multi-DoF tasks.
Conclusions:
- The EIT interface offers a promising new direction for non-invasive, continuous wrist angle estimation.
- The developed method effectively inspects deep muscle spatial information, extending sensor capabilities.
- Future research will focus on further optimizing this EIT-based approach for enhanced performance.
