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Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning
Kieran Little1, Bobby K Pappachan1, Sibo Yang1
1Robotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel, Switzerland)
|January 15, 2021
Summary
This study found that combining physiological and kinematic signals significantly improves upper limb motion intention detection for human-machine interfaces. Kinematic signals are crucial for accurate elbow flexion angle prediction in assistive robotics.
Area of Science:
- Robotics
- Biomedical Engineering
- Machine Learning
Background:
- Human-machine interfaces (HMIs) for assistive robots rely on accurate motion intention detection.
- Predicting upper limb motion involves user signals, feature extraction, and algorithms.
Purpose of the Study:
- To explore machine learning techniques for upper limb motion prediction.
- To evaluate the impact of different signals and features on elbow flexion angle prediction accuracy.
Main Methods:
- Trained 10 different machine learning algorithms using features from physiological and kinematic signals.
- Assessed prediction accuracy based on mean velocity and peak amplitude of elbow trajectories.
Main Results:
- Prediction accuracy was low using only physiological signals but significantly improved with the addition of kinematic signals.
- Regularization algorithms showed consistent performance, while neural networks excelled with selected key features.
Conclusions:
- Kinematic signals are essential for reliable elbow trajectory prediction in HMIs.
- The findings aid in developing advanced upper limb motion intention detection models for assistive robots.
