Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning

Abstract

To enhance lifting-load estimation accuracy in industrial upper-limb assistive exoskeletons, this study proposes a machine learning-based approach using insole pressure sensors. Unlike traditional methods that rely on electromyography (EMG), force sensors, or posture data, insole pressure sensors provide a non-invasive, posture-independent, and stable solution suitable for long-term use. Lifting load data ranging from 2 to 10 kg (0.5 kg intervals) were collected from five subjects. Two data representations were investigated: channel-based vectors and map-based images. For the channel-based approach, conventional regression models (SVR, MLP, and Elastic Net) were trained on pooled data from all subjects to assess inter-subject generalization, specifically testing the ability to infer load levels unseen during training. In parallel, a preliminary feasibility study was conducted for the map-based deep learning model (MobileNetV2) using inner-subject data. Results indicate that the channel-based SVR achieved the most balanced accuracy and generalization performance, with a mean absolute error of 0.547 kg. These findings demonstrate the feasibility and advantages of using insole pressure data for variable load estimation, supporting control strategies in industrial exoskeleton applications.