An alternative approach to measure quantity and smoothness of the human limb motions

Abstract

The present paper is devoted to the problem of measuring and modelling the changes in human motor functions. Nowadays, overwhelming majority of techniques for motion analysis and gesture recognition are based on feature extraction, pattern recognition and clustering. An alternative approach to measure and model changes in motor functions is proposed. Unlike feature extraction or pattern recognition techniques, the proposed approach concentrates its attention on the total quantity and smoothness of the human limb movements. The latter constitutes the main distinctive feature of the proposed technique. When changes of human motor functions are caused by learning of a new motor activity, amount and smoothness of the movements may provide necessary information to measure the effectiveness of the training technique. The notion “motion mass” is introduced as a measure associated with the motion, which describes how much and how smoothly certain joints have moved. Practical example of learning the ball throwing is used to demonstrate the ability of the proposed approach to measure the changes in motor functions and distinguish their performance on different stages of the learning process.