Real-time Estimation of the Strength Capacity of the Upper Limb for Physical Human-Robot Collaboration

Abstract

In physical Human-Robot Collaboration (pHRC), having an estimate of the operator's strength capacity can help implement control strategies. Currently, the trend is to integrate devices that can measure physiological signals. This is not always a viable option, especially for highly dynamic tasks. For pHRC tasks, the physical interaction point usually occurs at the operator's hand. Therefore, a musculo-skeletal model was used to have a real-time estimation of the strength capacity of the operator's upper limb. First, the model has been simplified to reduce the complexity of the problem. The model was used to obtain offline estimations of the strength capacity, that were then curve-fitted to enable real-time estimation. An experiment was carried out to compare the results of the approximated model with human data. Results suggest that this method for estimating the strength capacity of the upper limb is a viable solution for real-time applications.