Ability of Wearable Accelerometers-Based Measures to Assess the Stability of Working Postures

With the rapid development and widespread application of wearable inertial sensors in the field of human motion capture, the low-cost and non-invasive accelerometer (ACC) based measures have been widely used for working postural stability assessment. This study systematically investigated the abilities of ACC-based measures to assess the stability of working postures in terms of the ability to detect the effects of work-related factors and the ability to classify stable and unstable working postures. Thirty young males participated in this study and performed twenty-four load-holding tasks (six working postures × two standing surfaces × two holding loads), and forty-three ACC-based measures were derived from the ACC data obtained by using a 17 inertial sensors-based motion capture system. ANOVAs, t-tests and machine learning (ML) methods were adopted to study the factors’ effects detection ability and the postural stability classification ability. The results show that almost all forty-three ACC-based measures could (p < 0.05) detect the main effects of Working Posture and Load Carriage, and their interaction effects. However, most of them failed in (p ≥ 0.05) detecting Standing Surface’s main or interaction effects. Five measures could detect both main and interaction effects of all the three factors, which are recommended for working postural stability assessment. The performance in postural stability classification based on ML was also good, and the feature set exerted a greater influence on the classification accuracy than sensor configuration (i.e., sensor placement locations). The results show that the pelvis and lower legs are recommended locations overall, in which the pelvis is the first choice. The findings of this study have proved that wearable ACC-based measures could assess the stability of working postures, including the work-related factors’ effects detection ability and stable-unstable working postures classification ability. However, researchers should pay more attention to the measure selection, sensors placement, feature selection and extraction in practical applications.