Fuzzy clustering of human motor motion

Abstract

Acquisition of the behavioural skills of a human operator and recreating them in an intelligent autonomous system has been a critical but rather challenging step in the development of complex intelligent autonomous systems. Development of a systematic and generic method for realising this process by acquiring human postural and motor movements is explored. This is achieved by breaking down the human motion into a number of segments called motion or skill primitives. The proposed methodology is developed based on studying the movement of the human hand. The motion is measured by a dual-axis accelerometer and a gyroscope mounted on the hand. The gyroscope locates the position and configuration of the hand, whereas the accelerometer measures the kinematics parameters of the movement. The covariance and the mean of the data produced by the sensors are used as features in the clustering process. A fuzzy clustering method is developed and applied to identify different movements of the human hand. The proposed clustering approach identifies the sequence of the motion primitives embedded in the data produced from the human wrist movement. A review of the previous work in the area is carried out and the developed methodology is described. An overview of the experimental setup and procedures to validate the approach is given. The results of the validation are analysed critically and some conclusions are drawn.