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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

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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.

Keywords:

assistive robotics human-machine interface machine learning motion intention detection rehabilitation robotics

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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.