Unsupervised Segmentation and Labeling for Smartphone Acquired Gait Data
AffiliationNew Mexico State University, Klipsch School of Elec. & Comp. Eng.
Sandia National Laboratories
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AbstractAs the population ages, prediction of falls risk is becoming an increasingly important research area. Due to built-in inertial sensors and ubiquity, smartphones provide an at- tractive data collection and computing platform for falls risk prediction and continuous gait monitoring. One challenge in continuous gait monitoring is that signi cant signal variability exists between individuals with a high falls risk and those with low-risk. This variability increases the di cultly in building a universal system which segments and labels changes in signal state. This paper presents a method which uses unsu- pervised learning techniques to automatically segment a gait signal by computing the dissimilarity between two consecutive windows of data, applying an adaptive threshold algorithm to detect changes in signal state, and using a rule-based gait recognition al- gorithm to label the data. Using inertial data,the segmentation algorithm is compared against manually segmented data and is capable of achieving recognition rates greater than 71.8%.
SponsorsInternational Foundation for Telemetering