Comparison of myocontrol and force control based on Fitts' law Model

In recent years, many types of input modalities have been developed and used for a great variety of new devices and machines. To enhance the performance of the human-machine systems, well-designed human-machine interface (HMI's) between the user and the machine are essential. Biosignal-based HMI's have been appearing as an alternative to physical HMI's that have been conventionally used. As a type of biosignal control, the electromyography (EMG) has been investigated as an input modality for prostheses, computers, and robotic exoskeletons. In this study, myocontrol is analyzed through direct and numerical comparison with force control. Mycontrol and force control of visual pointing tasks were tested with EMG and force signals provided as visual feedback, and the controllability of each control mode was evaluated based on Fitts' law paradigm, which is a general estimation method of speed and accuracy of various movements. The experimental results show that both myocontrol and force control can be modeled using Fitts' law, even when different types of signals were provided as visual feedback. Among the control modes, myocontrol and force control showed high controllability when force signal was used as visual feedback.