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Classifying Lifting-Lowering Height and Load Level using Inertial Sensor-derived Kinematics: An Initial Study

Heavy lifting-lowering is associated with an increased risk of work-relevant low back disorders. Quantifying exposures to lifting-lowering activity is an important step toward developing effective intervention strategies. However, assessment of biomechanical exposures to lifting is non-trivial due to the multiple factors besides load magnitude that affect the risk of low back disorder from lifting. Risk factors include task parameters such as lifting frequency, duration, load moment arm characterized by the horizontal and vertical lifting height, asymmetry angle, and associated movement kinematics (e.g., trunk lateral velocity, trunk twisting velocity, and trunk sagittal angle) (Shannon, 1978; Herrin et al., 1974; Waters et al., 1994; Davis & Marras, 2005). Assessing biomechanical exposures and low back disorder risk associated with lifting-lowering is particularly challenging in non-repetitive work conditions where the magnitude, repetition and duration of activity may differ between workers and within worker across time. The aim of this study was to develop and assess a statistical classification algorithm that uses kinematic measures calculated from body-worn inertial sensor data obtained from a diverse cohort of participants as predictors for classifying load magnitude and vertical height in manual lifting and lowering task. The statistical classification algorithm incorporates a priori biomechanical knowledge about the effects of external loads on movement kinematics to inform the selection of predictor variables and the hierarchical design of the algorithm.