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Continuous Locomotion Mode and Task Identification for an Assistive Exoskeleton Based on Neuromuscular-Mechanical

Yao Liu^1,2,3, Chunjie Chen^1,2,3, Zhuo Wang^1,2

¹Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Bioengineering (Basel, Switzerland)

|February 23, 2024

Summary

This study developed a new interface to identify human walking tasks and modes for assistive exoskeletons. Achieving 98.7% accuracy, it enables better human-machine collaboration and seamless transitions.

Keywords:

assistive exoskeleton human–machine collaboration locomotion modes and tasks neuromuscular–mechanical

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Area of Science:

Biomechanics
Robotics
Human-Computer Interaction

Background:

Human walking exhibits variability in terrain, speed, and load.
Current assistive exoskeletons often overlook locomotion task identification, crucial for effective control.
Seamless human-exoskeleton interaction requires accurate recognition of both locomotion mode and task.

Purpose of the Study:

To develop an interface for identifying human locomotion mode (level/incline) and task (speed/load).
To create a neuromuscular-mechanical fusion algorithm for real-time assistive decision-making.
To optimize algorithm parameters for precise human-machine synchronization.

Main Methods:

Recruited seven able-bodied participants to explore level/incline modes and speed/load tasks.
Investigated optimal algorithms, feature sets (surface electromyography-acceleration), window increments, and lengths.
Utilized a support vector machine classifier for locomotion identification.

Main Results:

Achieved an average identification accuracy of 98.7% ± 1.3%.
Identified optimal parameters: support vector machine, root mean square/waveform length/acceleration features, window length of 170, and window increment of 20.
Demonstrated effective use of surface electromyography-acceleration for locomotion identification.

Conclusions:

The developed interface enables precise and timely identification of human locomotion modes and tasks.
Surface electromyography-acceleration fusion is effective for robust locomotion recognition.
This advancement facilitates improved exoskeleton control for seamless human-machine collaboration and transitions.