Publications:Gait Event Detection in Real-World Environment for Long-Term Applications : Incorporating Domain Knowledge into Time-Frequency Analysis
Property "Publisher" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user.
| Title | Gait Event Detection in Real-World Environment for Long-Term Applications : Incorporating Domain Knowledge into Time-Frequency Analysis |
|---|---|
| Author | |
| Year | 2016 |
| PublicationType | Journal Paper |
| Journal | IEEE transactions on neural systems and rehabilitation engineering |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1109/TNSRE.2016.2536278 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:909015 |
| Abstract | Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humansâ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments. © Copyright 2016 IEEE |
