Evaluation of a neural network-based control strategy for a cost-effective externally-powered prosthesis.

This paper presents a control strategy that compensates for the nonlinearity in the inexpensive sensors and hardware of a cost effective prosthetic hand. The control strategy uses neural network-based force control and sensory feedback to detect disturbance induced by slippage. The neural network approach is chosen over other nonlinear models because it is easy to implement and it offered the additional advantage of having its parameters easily adjusted over the life span of the device. The proposed strategy was evaluated on a functional multi-digit underactuated prosthetic hand. The initial and incremental forces exerted from each finger were adjusted to balance the amount of disturbance and the deformation of the objects. Experiments were conducted to test the performance of the protocol in situations encountered in activities of daily living. The displacement of each object under three grasping configurations was measured as a performance criterion while the object's mass was changed. The results showed that with the adjusted parameters for each grasping configuration, the control strategy was able to detect the dynamic changes in mass of the object and was also able to successfully adjust the grasping force before the object drops from the hand.