Myoelectric prosthetic hands create the possibility for amputees to control their prosthetics like native hands. However, user acceptance of the extant myoelectric prostheses is low. Unnatural control, lack of sufficient feedback, and insufficient functionality are cited as primary reasons. Recently, although many multiple degrees-of-freedom (DOF) prosthetic hands and tactile-sensitive electronic skins have been developed, no non-invasive myoelectric interfaces can decode both forces and motions for five-fingers independently and simultaneously. This paper proposes a myoelectric interface based on energy allocation and fictitious forces hypothesis by mimicking the natural neuromuscular system. The energy-based interface uses a kind of continuous “energy mode” in the level of the entire hand. According to tasks itself, each energy mode can adaptively and simultaneously implement multiple hand motions and exerting continuous forces for a single finger. Also, a few learned energy modes could extend to the unlearned energy mode, highlighting the extensibility of this interface. We evaluate the proposed system through off-line analysis and operational experiments performed on the expression of the unlearned hand motions, the amount of finger energy, and real-time control. With active exploration, the participant was proficient at exerting just enough energy to five fingers on “fragile” or “heavy” objects independently, proportionally, and simultaneously in real-time. The main contribution of this paper is proposing the bionic energy-motion model of hand: decoding a few muscle-energy modes of the human hand (only ten modes in this paper) map massive tasks of bionic hand.
Real Time Gesture Measurement of Human Hand for Robotics Arm
