Name
Abstract | ||
|---|---|---|
The aim of this paper is to present a multisensory system that studies abnormal walking patterns to prevent a fall. Due to the growing elderly population, scientific research on smartphone-based gait detection systems has recently become an imperative component in decreasing elderly injuries due to falls. To address the issue of smart gait detection, we propose a gait classification system using smarts hoe sensor data in this paper. We used shoe-worn pressure sensors on the foot and validated algorithms to extract the gait parameters during walking trials in a lab environment. This smarts hoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. To the best of our knowledge, this is the first system which can automatically detect abnormalities in walking patterns. A unique signal classification approach is presented by recognizing the abnormality in a subject's gait, and modeling the dynamics of a system as they are captured in a reconstructed phase space. Based on our experiments, we have found an 89% walking-based classification accuracy to help prevent falls. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/COMPSAC.2015.124 | International Computer Software and Applications Conference |
Keywords | Field | DocType |
Smart gait, Falls, Smartshoe, Smartphone, reconstructed phase spaces (RPS), Gaussian mixture models (GMM) | Computer vision,Population,Wireless,Gait,Computer science,Feature extraction,Real-time computing,Gait analysis,Pressure sensor,Artificial intelligence,Signal classification | Conference |
ISSN | Citations | PageRank |
0730-3157 | 0 | 0.34 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jahangir A. Majumder | 1 | 20 | 5.30 |
| Sheikh Iqbal Ahamed | 2 | 646 | 88.67 |
| Richard J. Povinelli | 3 | 225 | 20.40 |
| Chandana P. Tamma | 4 | 0 | 1.01 |
| Roger O. Smith | 5 | 16 | 1.62 |