Forecasting the Range of Possible Human Hand Movement in Consumer Electronics Disassembly Using Machine Learning

Abstract Robotic technology can benefit disassembly operations by reducing human operators' workload and assisting them with handling hazardous materials. Safety consideration and prediction of the human movement are priorities in close collaboration between humans and robots. The point-by-point forecasting of human hand motion, which forecasts one point at each time, does not provide enough information on human movement due to errors between the actual movement and the predicted value. This study provides a range of possible hand movements to increase safety. It applies three machine learning techniques, including long short-term memory (LSTM), gated recurrent unit (GRU), and Bayesian neural network (BNN) combined with bagging and Monte Carlo dropout (MCD), namely, LSTM-bagging, GRU-bagging, and BNN-MCD to predict the possible movement range. The study uses an inertial measurement unit (IMU) dataset collected from the disassembly of desktop computers by several participants to show the application of the proposed method.