Work-related musculoskeletal disorders (WMSDs) are a common occupational health problem in industries, and they can lead to decreased productivity and a reduced quality of life for workers. Exoskeletons, as an emerging technology, have the potential to solve this challenge by assisting arm movements and reducing muscle effort during load lifting tasks. In this paper, a passive exoskeleton based on a variable stiffness mechanism (VSM) torque generator is proposed and evaluated. This exoskeleton can provide adjustable torque curves and accommodate three degrees of freedom (DOFs) while remaining compact and lightweight. The workspace analysis shows that the workspace of this exoskeleton is sufficient for most industrial manual handling tasks. The experimental results demonstrate that the exoskeleton effectively reduces muscle effort during overhead reaching and load-lifting tasks, highlighting its effectiveness for repetitive tasks in industrial settings.

Walking is a key movement of interest in biomechanics, yet gold-standard data collection methods are time- and cost-expensive. This paper presents a real-time, multimodal, high sample rate lower-limb motion capture framework, based on wireless wearable sensors and machine learning algorithms. Random Forests are used to estimate joint angles from IMU data, and ground reaction force (GRF) is predicted from an instrumented insole, while joint moments are predicted from angles and GRF using deep learning based on the ResNet-16 architecture. All three models achieve good accuracy compared to the literature, and the predictions are logged at 1 kHz with a minimal delay of 23 ms for 20s worth of input data.