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Carrying heavy hand loads frequently and for long durations is a known risk factor for low back disorders. Two-handed anterior carry is a common carrying posture performed regularly at work places, but shows the largest increase in anterior-posterior shear loading compared to other carrying postures such as one-handed carry, backpack carry, and shoulder carry. A two-handed load of just 11.3 kg causes spinal shear loads to exceed the recommended exposure limits and may potentially damage spinal tissues (Rose, Mendel, & Marras, 2013). Prediction of load levels remotely using the wearable sensors could help quantify biomechanical exposures from load carriage in situ particularly in jobs where the duration and magnitudes of loads carried vary across time (e.g., warehousing, and mail delivery). The mode and magnitude of load carriage produces biomechanical adaptations reflected in changes in posture and gait patterns, specifically in the movement coordination between the torso and pelvis (Lim & D'Souza, under review). The mean relative phase angles is a measure of coordination between multiple body segments during complex, multi-joint movements (Burgess-Limerick, Abernethy, & Neal, 1993). The objective of this study was to build and validate a statistical prediction algorithm that uses measures of thoracic-pelvic coordination, namely, mean relative phase angles, computed from body-worn inertial sensor data for classifying hand-load levels in a two-handed anterior load carrying task.

Inertial Sensor-based Measurement of Thoracic-Pelvic Coordination Predicts Hand-Load Levels in Two-handed Anterior Carry