Walking-age analyzer for healthcare applications.

Abstract

This paper describes a walking-age pattern analysis and identification system using a 3-D accelerometer and a gyroscope. First, a walking pattern database from 79 volunteers of ages ranging from 10 to 83 years is constructed. Second, using feature extraction and clustering, three distinct walking-age groups, children of ages 10 and below, adults in 20-60s, and elders in 70s and 80s, were identified. For this study, low-pass filtering, empirical mode decomposition, and K-means were used to process and analyze the experimental results. Analysis shows that volunteers' walking-ages can be categorized into distinct groups based on simple walking pattern signals. This grouping can then be used to detect persons with walking patterns outside their age groups. If the walking pattern puts an individual in a higher "walking age" grouping, then this could be an indicator of potential health/walking problems, such as weak joints, poor musculoskeletal support system or a tendency to fall.