A Wearable Upper Limb Exoskeleton System and Intelligent Control Strategy

Area of Science:

• Robotics
• Biomechanics
• Human-Computer Interaction

Background:

• Heavy lifting tasks often cause upper limb muscle fatigue and injuries.
• Wearable assistive devices can mitigate these issues by providing external support.
• Existing solutions may lack adaptability or comfort for prolonged use.

Purpose of the Study:

• To develop and evaluate a lightweight, wearable upper limb exoskeleton (ULE) robot system.
• To implement an intelligent control system for automatic auxiliary force provision.
• To assess the effectiveness of the ULE in reducing muscle fatigue during lifting tasks.

Main Methods:

• Development of a ULE robot with a flexible cable transmission system bypassing the shoulder.
• Integration of the K-nearest neighbors (KNN) algorithm for posture identification and fuzzy PID control for auxiliary force.
• Evaluation of muscle fatigue using electromyogram (EMG) signals from brachioradialis, triceps brachii, and biceps brachii muscles under varying load conditions (5-15 kg).

Main Results:

• The KNN algorithm achieved a test accuracy of 94.59% in identifying handling postures.
• The ULE system demonstrated a reduction in average peak EMG signal values by 19-30% across tested muscles and loads.
• The exoskeleton provided significant muscle fatigue reduction during lifting, holding, and squatting actions.

Conclusions:

• The developed wearable upper limb exoskeleton robot effectively reduces muscle fatigue and provides practical assistance during heavy lifting operations.
• The intelligent control system, combining KNN and fuzzy PID, enables accurate and automatic provision of auxiliary force.
• The lightweight and wearable design ensures comfort and minimal impact on the wearer's range of motion.