Statistical Decision Making in the Real-Time Control of an Arm Aid for the Disabled

In a multidegree of freedom manipulator there can exist the problem of how to coordinate all the actuators to produce movement in a space of lower dimension. In this paper statistical decision procedures are used to select an elbow position of an externally powered arm aid to deal with this problem. The decision makers are derived from a set of exoskeletal goniometer movement recordings of typical daily tasks. Both pattern recognition and regression techniques are employed. Pattern recognition is used to decide which class of tasks the human is currently performing based on the desired end-point position and the position of the three hand axes: wrist supination, wrist flexion, and prehension. A regression function with end-point position arguments is then used to estimate a parameter called the ``elbow angle.´´ The combination of the elbow angle and the desired end-point position is transformed geometrically to obtain the correct actuator positions of the assistive device. Both decision schemes are implemented in a real-time algorithm for the computer control of a modified Rancho electric arm.