Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots
Sibo Yang1, Neha P Garg2, Ruobin Gao3
1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel, Switzerland)
|March 30, 2023
Summary
This study introduces a new controller for upper-limb assistive robots that predicts hand position using early movement signals. Inertial Measurement Units (IMUs) alone are effective for detecting motion intention, improving robot usability.
Area of Science:
- Robotics
- Human-Robot Interaction
- Biomedical Engineering
Background:
- Upper-limb assistive devices often lack intuitive control, hindering their practical application.
- Effective human-robot interaction is crucial for seamless operation of assistive technologies.
Purpose of the Study:
- To develop a novel learning-based controller for assistive robots that intuitively predicts desired end-point positions using onset motion.
- To evaluate the efficacy of multi-modal sensing (IMUs, EMG, MMG) for motion intention detection.
Main Methods:
- Implemented a multi-modal sensing system with IMUs, EMG, and MMG sensors.
- Collected kinematic and physiological data during reaching and placing tasks from five healthy subjects.
- Utilized traditional regression and deep learning models (including RNNs) to predict hand position from onset motion data.
Main Results:
- Inertial Measurement Unit (IMU) data alone proved sufficient for accurate motion intention detection, comparable to using additional EMG or MMG sensors.
- Recurrent Neural Network (RNN) models demonstrated effectiveness in predicting target positions within short onset windows for reaching and longer horizons for placing tasks.
Conclusions:
- The proposed prediction model using IMUs offers a viable and efficient method for motion intention detection in assistive robots.
- This approach can significantly enhance the usability and intuitiveness of upper-limb assistive and rehabilitation robots.
