A Simple, Low-Cost and Efficient Gait Analyzer for Wearable Healthcare Applications

The aging population is projected to rise significantly due to continuous improvements in healthcare, personal and environmental hygiene, nutrition, and education. This large aging demographic may cause adverse socio-economic impacts in terms of the costs associated with healthcare and social services. In order to support the healthcare needs of the elderly in a cost-effective manner, affordable, non-invasive, easy-to-use, and reliable predictive diagnostic and monitoring solutions are required. Therefore, walking or gait, being a good indicator of our overall health status may be exploited as a simple, noninvasive, and reliable metric for health assessment. In this paper, we report on a simple, low-cost, and non-invasive gait analyzer that can quantitatively identify the healthy gait corresponding to gender and age, and can thereby evaluate an individual's gait with respect to the baseline characteristics of his/her peer group. The analyzer uses low-cost, wireless, and miniature micro-electromechanical sensor-based inertial motion sensors to obtain acceleration and angular velocity of walking from both legs. Upon constructing a database of walking signals from 74 healthy subjects aged 18-65 years, we employed the computationally efficient discrete wavelet packet analysis method to extract a set of temporal, statistical, and energy features. The features obtained from the apparently healthy subjects were classified using the support vector machine, forming two distinct clusters in the baseline gait characteristics corresponding to gender and age. This simple and inexpensive gait analyzer can potentially be transformed into a portable and continual remote monitoring tool to evaluate and early diagnose the decline of the musculoskeletal or cognitive health of the user, thus facilitating healthy aging at home.