An incremental learning approach to detect muscular fatigue in human-robot collaboration

Human-Robot Collaboration aims to join distinctive strengths of humans and robots to compensate weaknesses associated with each party, and thus, to enable synergetic effects. Robots are characteristically considered fatigue proof. Hence, they are utilized to assist human operators during heavy pushing and pulling activities. To detect physical fatigue or high payloads held by a human operator, wearable sensors such as Electromyographys (EMGs) are deployed. The EMG data is typically processed via Machine Learning, which includes training models offline before an application in an online system. However, these approaches often demonstrate varying performances between offline and online applications, due to subject specific characteristics within the data. An opportunity to tackle this challenge can be found in Incremental Learning, as these models purely learn online and constantly fine-tune the model’s structure. In this paper, a Mondrian Forest is applied to predict payloads and physical fatigue of human operators during an assistance scenario with a collaborative robot. An experiment was conducted with a total of 12 participants, where payload was increased until participants initiated an assistance request from a UR10 cobot. This allowed for testing whether the Mondrian Forest can accurately predict the payload and fatigue levels from the acquired EMG signals. Overall, the approach demonstrates a promising potential towards higher awareness when an operator might require assistance from a robot, and ultimately towards a more effective Human-Robot Collaboration.