Data-Driven Ergonomic Risk Assessment of Complex Hand-intensive
  Manufacturing Processes
Anand Krishnan Xingjian Yang Utsav Seth Jonathan M. Jeyachandran Jonathan Y. Ahn Richard Gardner Samuel F. Pedigo Adriana Blom-Schieber Ashis G. Banerjee Krithika Manohar
Hand-intensive manufacturing processes, such as composite layup and textile
draping, require significant human dexterity to accommodate task complexity.
These strenuous hand motions often lead to musculoskeletal disorders and
rehabilitation surgeries. We develop a data-driven ergonomic risk assessment
system with a special focus on hand and finger activity to better identify and
address ergonomic issues related to hand-intensive manufacturing processes. The
system comprises a multi-modal sensor testbed to collect and synchronize
operator upper body pose, hand pose and applied forces; a Biometric Assessment
of Complete Hand (BACH) formulation to measure high-fidelity hand and finger
risks; and industry-standard risk scores associated with upper body posture,
RULA, and hand activity, HAL. Our findings demonstrate that BACH captures
injurious activity with a higher granularity in comparison to the existing
metrics. Machine learning models are also used to automate RULA and HAL
scoring, and generalize well to unseen participants. Our assessment system,
therefore, provides ergonomic interpretability of the manufacturing processes
studied, and could be used to mitigate risks through minor workplace
optimization and posture corrections.