Joint pain detection by gait analysis for elderly healthcare

This Lower part of skeletal structure of humans (hereafter, lower body) plays a fundamental role in maintaining balance and gait of a person. Naturally, when the lower body joints are affected by pain, the gait of a person is changed. In this paper we inspected the correspondence of gait patterns to different lower body joint pains. It was found that gait patterns can indeed be used as non-intrusive bio-markers for detecting selective body joint pains. From the computational point of view, our work has three major standpoints. First, we used a well-established statistical technique known as differencing to enable body structure and location independent recognition of gait. Second we proposed a scheme of selecting body joints which are most informative of body pain by assigning maximum score to body joint trajectories which are unique to different classes of joint pain but similar for the same class of joint pain. Third, we employ differential evolution algorithm for compression of motion trajectories by a ratio of 1:5 by selecting only important samples which maximize the entropy of the motion trajectory. We apply the above three techniques for data enhancement and use it as a feature extraction technique. It is a well-established fact that efficient classification is dependent on judicious selection of feature-space data which captures essential information hidden in the data while minimizing its dimensionality. Thus our approach of data enhancement by the above steps outperforms an established traditional feature extraction scheme by a wide margin. The average classification accuracy obtained by us is 87.91%.