Difference between revisions of "Publications:A Symbol-Based Approach to Gait Analysis From Acceleration Signals : Identification and Detection of Gait Events and a New Measure of Gait Symmetry"
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| − | |Name=Sant'Anna, Anita | + | |Name=Sant'Anna, Anita (anisan) (0000-0002-3495-2961) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941));Wickström, Nicholas (nicholas) (0000-0002-4143-2948) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941)) |
|Title=A Symbol-Based Approach to Gait Analysis From Acceleration Signals : Identification and Detection of Gait Events and a New Measure of Gait Symmetry | |Title=A Symbol-Based Approach to Gait Analysis From Acceleration Signals : Identification and Detection of Gait Events and a New Measure of Gait Symmetry | ||
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Latest revision as of 21:42, 30 September 2016
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| Title | A Symbol-Based Approach to Gait Analysis From Acceleration Signals : Identification and Detection of Gait Events and a New Measure of Gait Symmetry |
|---|---|
| Author | |
| Year | 2010 |
| PublicationType | Journal Paper |
| Journal | IEEE transactions on information technology in biomedicine |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1109/TITB.2010.2047402 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:345706 |
| Abstract | Gait analysis can convey important information about one’s physical and cognitive condition. Wearable inertial sensor systems can be used to continuously and unobtrusively assess gait during everyday activities in uncontrolled environments. An important step in the development of such systems is the processing and analysis of the sensor data. This paper presents a symbol-based method used to detect the phases of gait and convey important dynamic information from accelerometer signals. The addition of expert knowledge substitutes the need for supervised learning techniques, rendering the system easy to interpret and easy to improve incrementally. The proposed method is compared to an approach based on peak-detection. A new symbol-based symmetry index is created and compared to a traditional temporal symmetry index and a symmetry measure based on cross-correlation. The symbol-based symmetry index exemplifies how the proposed method can extract more information from the acceleration signal than previous approaches |
