Human-Exoskeleton Coupling Simulation for Lifting Tasks with Shoulder, Spine, and Knee-Joint Powered Exoskeletons

Area of Science:

• Biomechanics and Robotics
• Human-Computer Interaction
• Ergonomics and Occupational Safety

Background:

• Manual lifting tasks pose significant risks of musculoskeletal injuries.
• Exoskeleton technology offers potential solutions for reducing physical strain.
• Optimizing exoskeleton assistance requires accurate human-robot interaction models.

Purpose of the Study:

• To develop and validate a 2D human skeletal model integrated with knee, spine, and shoulder exoskeletons.
• To predict optimal lifting motions and exoskeleton torque support using inverse dynamics optimization.
• To minimize human joint torques during lifting tasks with exoskeleton assistance.

Main Methods:

• A 2D human skeletal model utilizing Denavit-Hartenberg (DH) representation for kinematics and dynamics.
• Inverse dynamics optimization integrating electromechanical dynamics of DC motors in exoskeletons.
• SNOPT (gradient-based optimizer) used to minimize squared normalized human joint torques under physical and task constraints.

Main Results:

• Exoskeleton assistance substantially reduced human joint torque requirements.
• Comparison of joint angle, torque, and ground reaction force (GRF) profiles with and without exoskeleton support.
• Experimental data showed significant muscle activation reduction (up to 35%) with knee exoskeleton use.

Conclusions:

• The developed model effectively predicts optimal lifting strategies and exoskeleton torque.
• Integrated knee, spine, and shoulder exoskeleton support significantly lowers physical exertion.
• Exoskeletons show promise in reducing injury risk and improving efficiency in manual handling tasks.