Abstract:
The goal of this investigation is to develop a multi-degree of freedom (DOF) prosthesis controller that uses myoelectric signals as control inputs and which has been dimensionally optimized using Principal Component Analysis (PCA). Currently available multi-DOF hand prostheses cannot be fully utilized because there are fewer control inputs than the number of degrees of freedom (i.e. – joints) that need to be controlled. Based on work from the field of neuroscience it has been shown that grasping is a ‘low dimensional’ task. Santello et al. used PCA to quantify the principal components (patterns of joint movements) involved in grasping. It was found that grasping tasks involving a number of everyday items could be described by only two principal components. This implies that multi-DOF hand postures can be controlled using only two degrees of control. Therefore, a PCA-based myoelectric prosthetic hand controller can drive grasping postures with only two independent control sites. This is an encouraging finding since current clinical practice indicates two, or three, independent control sites can be located on the residual limb of a typical person with a transradial amputation.
The following paper discusses the design and development of a PCA-based myoelectric prosthetic hand controller. Also, the results of a validation experiment are shared.
