Feature Detection and Biomechanical Analysis to Objectively Identify High Exposure Movement Strategies When Performing the EPIC Lift Capacity test.

Purpose The Epic Lift Capacity (ELC) test is used to determine a worker's maximum lifting capacity. In the ELC test, maximum lifting capacity is often determined as the maximum weight lifted without exhibiting a visually appraised "high-risk workstyle." However, the criteria for evaluating lifting mechanics have limited justification. This study applies feature detection and biomechanical analysis to motion capture data obtained while participants performed the ELC test to objectively identify aspects of movement that may help define "high-risk workstyle". Method In this cross-sectional study, 24 participants completed the ELC test. We applied Principal Component Analysis, as a feature detection approach, and biomechanical analysis to motion capture data to objectively identify movement features related to biomechanical exposure on the low back and shoulders. Principal component scores were compared between high and low exposure trials (relative to median exposure) to determine if features of movement differed. Features were interpreted using single component reconstructions of principal components. Results Statistical testing showed that low exposure lifts and lowers maintained the body closer to the load, exhibited squat-like movement (greater knee flexion, wider base of support), and remained closer to neutral posture at the low back (less forward flexion and axial twist) and shoulder (less flexion and abduction). Conclusions Use of feature detection and biomechanical analyses revealed movement features related to biomechanical exposure at the low back and shoulders. The objectively identified criteria could augment the existing scoring criteria for ELC test technique assessment. In the future, such features can inform the design of classifiers to objectively identify "high-risk workstyle" in real-time.

Is deep squat movement strategy related to floor-to-waist height lifting strategy: implications for physical employment testing.

Generalised predictive tests may be viable screening tools to evaluate job candidate workability if movement strategy used in assessment is consistent with movement strategy used in work. This study investigated if deep squat (DS) kinematics could predict floor-to-waist height lifting kinematics. Participants performed three DS repetitions, and 10 lifts of both a 10 kg and 20 kg box. Whole body kinematics were collected to calculate knee, hip and low back angles, and coordination as measured by relative phase angles. Movement features of lower extremity control, including knee and hip angles and coordination, were significantly correlated (r = 0.43-0.85) between the DS and lifting. However, low back movement features, measures linked to injury risk, were not significantly correlated between the DS and lifting. These findings do not support the DS as a suitable movement screen to predict lifting strategy, specifically when considering low back control. Practitioner summary: This study investigated whether lifting strategy could be inferred from deep squat performance. Knee and hip movement strategies were associated between the deep squat and lifting. However, inconsistencies in low back control between the deep squat and lifting limit the deep squat's injury risk assessment potential.