With the developments of collaborative robots in manufacturing, physical interactions between humans and robots represent a vital role in performing tasks collaboratively. Most conducted studies focused on robot motion planning and control during the execution of a task. However, for effective task distribution and allocation, human physical and psychological status are essential. In this research, a hardware setup and support software for a set of wearable sensors and a data acquisition framework, are developed. This can be used to develop more efficient Human-Robot collaboration strategies. The developed framework is intended to recognise the human mental state and physical activities. Subsequently, a robot could effectively and naturally perform the given task with the human. Besides, the collected data through the developed hardware enables online classification of human intentions and activities; therefore, robots can actively adapt to ensure the safety of the human while delivering the required task.

With collaborative robots and the recent developments in manufacturing technologies, physical interactions between humans and robots represent a vital role in performing collaborative tasks. Most previous studies have focused on robot motion planning and control during the execution of the task. However, further research is required for direct physical contact for human-robot or robot-robot interactions, such as co-manipulation. In co-manipulation, a human operator manipulates a shared load with a robot through a semi-structured environment. In such scenarios, a multi-contact point with the environment during the task execution results in a convoluted force/toque signature that is difficult to interpret. Therefore, in this paper, a muscle activity sensor in the form of an electromyograph (EMG) is employed to improve the mapping between force/torque and displacements in co-manipulation tasks. A suitable mapping was identified by comparing the root mean square error amongst data-driven models, mathematical models, and hybrid models. Thus, a robot was shown to effectively and naturally perform the required co-manipulation with a human. This paper’s proposed hypotheses were validated using an unseen test dataset and a simulated co-manipulation experiment, which showed that the EMG and data-driven model improved the mapping of the force/torque features into displacements.