Wearability of robotic devices will enable novel forms of human intention recognition through haptic signals and novel forms of communication and cooperation between humans and robots. Specifically, wearable haptics will enable devices to communicate with humans during their interaction with the environment they share. The proposed tactile system is extremely wearable, effective, inexpensive, and completely wireless. There are in fact no workspace restrictions apart from the ones related to the gesture recognition technique.

Brain-Computer Interface (BCI) uses electrophysiological measures of brain function to enable individuals to communicate with the external world, bypassing normal neuromuscular pathways. While it has been suggested that this control can be applied for neuroprostheses, few studies have demonstrated practical BCI control of a prosthetic device. In this project, an electroencephalogram (EEG)-based motor imagery BCI is presented to control movement of a prosthetic hand. The hand was instrumented with force and angle sensors to provide haptic feedback and local machine control. Using this system, subjects demonstrated the ability to control the prosthetic's grasping force with accuracy comparable to an EMG-based control scheme.