An Intelligent Wearable Insole System for Machine Learning-Based Detection of High-Risk Postures in Static Symmetrical Load-Lifting Tasks Publisher Pubmed



Varmazyar M ; Jafari A H ; Karbasi F ; Garosi E
Source: Scientific Reports Published:2026

Abstract

Work-related musculoskeletal disorders (WMSDs) remain a major occupational health concern worldwide, with manual material handling, particularly lifting, being a primary contributor to low-back pain. Conventional ergonomic interventions often rely on observational assessments and lack continuous, objective, and automated monitoring in real-time conditions. This study presents an intelligent wearable insole system that integrates plantar-pressure and an inertial measurement unit with embedded machine-learning algorithms for the automated classification of low-risk/high-risk lifting postures in real-time, providing immediate visual/auditory feedback. A two-phase protocol was implemented. In Phase 1, twenty-three participants performed thirty-six static symmetric lifting tasks under three load conditions, with a 13-dimensional feature vector recorded per trial, comprising 12 force-sensitive resistor plantar-pressure readings and one inertial measurement unit-derived trunk flexion angle, and ground-truth labels assigned using the UTAH back compressive force criteria. In Phase 2, five machine learning models (logistic regression, support vector machine, k-nearest neighbors, decision tree, and random forest) were trained on the 13-dimensional feature vectors using a participant-wise 78%/22% train–test split with 5-fold cross-validation. The best-performing model was then deployed on the assembled intelligent insole and validated in real time with ten independent participants. Among the evaluated models, logistic regression demonstrated the best overall performance, achieving 91.17% offline classification accuracy, 93.75% sensitivity, 87.83% specificity, and an AUC of 0.94, with trunk flexion angle as key predictor. Upon deployment on the smart insole, real-time validation achieved 88% accuracy, with a moderate to strong correlation (r = 0.69) between classified postures and estimated UTAH back compressive forces. The proposed system enables automated, continuous monitoring of static lifting posture and timely feedback without the need for expert observation. This approach offers a practical and scalable solution for ergonomic risk assessment and prevention of lifting-related WMSDs in industrial and manufacturing environments. © The Author(s) 2026.