Our research proposes a framework to obtain and analyze real time data concerning the dynamic and natural motion of individuals in manufacturing-like processes that involve human labor. The framework that we propose consists of five main components: a tracking system, a system of sensors, a processor that collects time series data, data processing and discovery module, and an alert or reporting component. Using motion capture cameras, data is collected on a variety of human subjects performing simulated labor-intensive manufacturing operations. This data is analyzed for identification of actual and optimal activity motions. This project has significant potential impact for contribution and advancement of the material handling, logistics, and supply chain industry. This simulation process will enable a company to modify human motion operations that are non-value added from the activity process.
Progress in Material Handling Research
Mendez, Francis; Wierschem, David; and Jimenez, Jesus, "A Motion Capture System Framework for the Study of Human Manufacturing Repetitive Motions" (2018). 15th IMHRC Proceedings (Savannah, Georgia. USA – 2018). 25.