VISION-BASED LIFTING LOAD ESTIMATION FOR PREVENTING LIFTING INJURIES

Heavy and repetitive lifting tasks are commonly observed across many industries; however, the poor ergonomics of these tasks contribute to work-related musculoskeletal injuries

worldwide. Identifying when these tasks increase injury risks is essential for reducing workplace injuries. Current injury risk assessment tools require trained ergonomists to measure

worker posture, task repetition, and force exertion. While repetition and posture are easily

observable, the level of force exerted by the worker remains difficult to estimate without intrusive measurement techniques such as surface Electromyography(sEMG) sensors. In study

A, a video-based method for lifting risk estimation that can measure injury risks due to varying force levels without the need for intrusive sensors is proposed. The proposed method is

demonstrated with lifting tasks commonly observed in the workplace. The method consists

of a novel set of computer vision algorithms that monitor workers’ body motion, posture, and

facial expressions using only videos capturing the lifts. Extracted features were normalized

and used by machine learning models for classifying safety and risk levels determined by validated metrics of injury risk, i.e., lifting index and perceived physical effort (Borg scale). In

addition, this study discovered novel lifting risk indicators by investigating the relationships

between extracted features and lifting risks through interpretable machine learning and statistical inference techniques. In study B, a prototype decision support system that aims to

help people perform lifting risk assessment is developed. The proposed system implements

the video-based method from study A. A usability study is conducted to investigate the effect

of the decision support system on user performance and confidence, and demonstrates the

effectiveness of the proposed system. In summary, this thesis (a) proposes a non-intrusive

method for lifting risk assessment, (b) discovers novel lifting risk indicators, and (c) develops

a decision support system for helping people perform lifting risk assessment.