State transition control of a five-fingered pneumatic hand using a neural network

A control method is presented for a five-fingered artificial hand using EMG signals. The artificial hand is driven by pneumatic actuators and has 15 degrees of freedom. It is difficult to discriminate all the finger motions from just the EMG signals. Therefore, we describe typical hand motions using Petri net and control the finger motions based on this model. The proposed method enables the operator to incrementally control the joints of the five fingers based on the discrimination of the discrete hand motion. The operator only needs to specify the kind of discrete hand motion (i.e., spherical grasp, power grip, hook grip, key grip, and precision grip) and does not need to consider how to control each finger. Each state of the Petri net stores the on/off pattern of the 15 solenoid valves that corresponds to the posture of the five-fingered hand. The hand posture is incrementally varied to complete the desired motion, transitioning to the state in the Petri net based on EMG motion discrimination.We conducted experiments using four able-bodied subjects. In the experiment, the above-mentioned five motions were successfully performed using six-channel EMG signals measured from the forearm of the operator.