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

Human–robot collaboration aims to join the distinctive strengths of humans and robots to compensate for the 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 are 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 article, 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 the payload was increased until participants initiated an assistance request from a Universal Robots model 10 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 toward higher awareness when an operator might require assistance from a robot and ultimately toward a more effective human–robot collaboration.