Despite significant advancements in the visual side of virtual reality (VR) due to improved headset technology, physical movement and sensation in VR have yet to be effectively implemented. The physical presence of one’s body is very important for VR’s uses in medical treatments, however, so the development of a force-tracking system could be key in increasing VR’s relevance. A restraint system with two degrees of freedom (DOF) on the arm was developed, and when combined with vibrational haptics on the hand it allowed for intuitive movement of a virtual arm and a body transfer illusion. Future work is needed in better haptics, more DOFs, and other sensing options for muscular cocontraction.
Electromyography (EMG) sensors can be added to a variety of devices for detecting nerve signals in muscles, but there is a need for better signal analysis techniques and more flexible models for interpreting gestures. Using a machine learning technique called deep learning, the researcher developed a model for detecting gesture changes in EMG data with a high level of accuracy. This kind of model could be used for allowing users to control prosthetics or assistive exoskeletons with a high degree of precision. Future work should be done to develop even better models and make these medical devices more effective.