Taori, SakshiLim, Sol2025-04-282025-04-282024-090003-6870S0003-6870(24)00062-0 (PII)https://hdl.handle.net/10919/126237We used an armband with embedded surface electromyography (sEMG) electrodes, together with machine-learning (ML) models, to automatically detect lifting-lowering activities and classify hand loads. Nine healthy participants (4 male and 5 female) completed simulated lifting-lowering tasks in various conditions and with two different hand loads (2.3 and 6.8 kg). We compared three sEMG signal feature sets (i.e., time, frequency, and a combination of both domains) and three ML classifiers (i.e., Random Forest, Support Vector Machine, and Logistic Regression). Both Random Forest and Support Vector Machine models, using either time-domain or time- and frequency-domain features, yielded the best performance in detecting lifts, with respective accuracies of 79.2% (start) and 86.7% (end). Similarly, both ML models yielded the highest accuracy (80.9%) in classifying the two hand loads, regardless of the sEMG features used, emphasizing the potential of sEMG armbands for assessing exposure and risks in occupational lifting tasks.11 page(s)application/pdfenIn CopyrightEMGLifting and loweringLifting load classificationWearable armbandHandHumansElectromyographyTask Performance and AnalysisLiftingWeight-BearingAdultFemaleMaleYoung AdultHealthy VolunteersMachine LearningSupport Vector MachineWearable Electronic DevicesArticle - Refereedhttps://doi.org/10.1016/j.apergo.2024.104285119Lim, Sol [0000-0001-5569-9312]387970131872-9126