Biomechanical Design Optimization of Passive Exoskeletons through Surrogate Modeling on Industrial Activity Data

Passive exoskeletons are unpowered wearable robotic devices aimed at providing biomechanical assistance. They can be applied in industries such as manufacturing, construction and logistics to reduce repetitive stress injuries among workers. Their design process typically considers a static user, with muscle outputs computed later during dynamic tasks to evaluate performance. Attempting to reduce human muscle effort at this stage requires manual redesign. Instead, we propose a parameter optimization approach that minimizes muscle effort rates during realistic dynamic tasks in the design stage itself. We extract human kinematics in assembly tasks from an industry-oriented motion capture dataset, and compute the induced joint torques. Using a passive exoskeleton for shoulder joint gravity compensation from the literature as a baseline, we optimize its design parameters through a multi-objective Pareto Local Search, minimizing the muscle effort rates during these tasks. As the estimation of muscle outputs through biomechanical simulation techniques is computation-ally expensive, we train ensemble regression models for each muscle of interest during the task motions. These models serve as surrogates for the objective function in the design optimization procedure, speeding up search in the parameter space. The resulting exoskeleton with optimized design param-eters reduces estimated muscle effort rates by an average of 5.73% and peak of 35.1 % compared to default parameters, and an average of 14.5% and peak of 32.2% compared to not wearing an exoskeleton in overhead assembly tasks. A larger peak reduction compared to default parameters may be due to hindrance in motion caused by device. This approach may be adapted to other exoskeletons and applications, improving biomechanical assistance by design.