This study introduces the Smart Evaluation of Work Ability (SEWAbility), an AI-powered video analysis system designed to support objective job analysis by deriving task-specific job demands from real-world sewing task videos. SEWAbility processes overhead surveillance footage from factory environments through a hierarchical analytical pipeline. Videos are first clustered into work tasks using 88-dimensional handcrafted features capturing upper-extremity motion trajectories and joint angle statistics. Each task is subdivided into work cycles and further into work elements, from which Repetitive Motion Pattern (RMP) features are extracted. Based on an analysis of 21 sewing videos across three task categories (A: tops, B: beddings, C: bottoms), SEWAbility effectively distinguished task types. In the video of work task A1, seven work cycles of sewing activity were correctly identified, and 18 repetitive work elements were extracted from the first work cycle. The resulting RMP features provided objective descriptions of motion trajectories, joint speeds, and ranges of motion, yielding interpretable and task-specific biomechanical benchmarks. By enabling scalable and systematic quantification of job demands, SEWAbility demonstrates the feasibility of deriving task-specific biomechanical benchmarks from workplace videos. While this preliminary study analyzed data from a single sewing worker with a physical disability, the broader dataset from more workers will allow future validation. In this way, SEWAbility holds promise to support data-driven employment recommendations and vocational rehabilitation planning for individuals with disabilities.
Hu et al. (Tue,) studied this question.