sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton

Area of Science:

• Biomechanical Engineering
• Occupational Health
• Human-Robot Interaction

Background:

• Industrial lower back pain (LBP) is a significant issue.
• Powered back support exoskeletons (BSEs) aim to prevent LBP.
• Conventional kinematics-triggered assistance (KA) in BSEs suffers from latency, reducing effectiveness.

Purpose of the Study:

• To propose and evaluate a novel surface electromyography (sEMG)-triggered assistance (EA) strategy for BSEs.
• To compare the efficiency and muscle fatigue reduction of EA versus KA.
• To assess the impact of assistance latency on muscle activity.

Main Methods:

• Nine healthy subjects performed lifting tasks with varying external loads and assistance strategies.
• Experiments included external loads, extra latency, and repetitive lifting tests.
• Surface electromyography (sEMG) was used to measure muscle activity and fatigue.

Main Results:

• EA demonstrated earlier assistance compared to KA, with delays decreasing as external loads increased.
• Fast EA (0 ms and 100 ms latency) minimized muscle activity; increased latency led to higher activity.
• EA reduced L1 muscle fatigue by 70.4% during repetitive stoop lifting compared to KA.

Conclusions:

• Fast sEMG-triggered assistance enhances BSE efficiency and is particularly effective under heavy load conditions.
• The proposed EA strategy effectively reduces back muscle fatigue, offering a viable solution for LBP prevention.
• This assistive strategy is readily adaptable for diverse industrial exoskeleton applications.