As 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%.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/624183 |
| Date | 11 1900 |
| Creators | Martinez, Matthew, De Leon, Phillip L. |
| Contributors | New Mexico State University, Klipsch School of Elec. & Comp. Eng., Sandia National Laboratories |
| Publisher | International Foundation for Telemetering |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Proceedings |
| Rights | Copyright © held by the author; distribution rights International Foundation for Telemetering |
| Relation | http://www.telemetry.org/ |
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