Online context recognition in multisensor systems using dynamic time warping
In this paper we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society Press
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/4001 |
| _version_ | 1848744390942523392 |
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| author | Ko, Ming West, Geoff Venkatesh, Svetha Kumar, Mohan |
| author2 | M Palaniswami |
| author_facet | M Palaniswami Ko, Ming West, Geoff Venkatesh, Svetha Kumar, Mohan |
| author_sort | Ko, Ming |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy. |
| first_indexed | 2025-11-14T06:00:43Z |
| format | Conference Paper |
| id | curtin-20.500.11937-4001 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:00:43Z |
| publishDate | 2005 |
| publisher | IEEE Computer Society Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-40012018-03-29T09:05:21Z Online context recognition in multisensor systems using dynamic time warping Ko, Ming West, Geoff Venkatesh, Svetha Kumar, Mohan M Palaniswami In this paper we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy. 2005 Conference Paper http://hdl.handle.net/20.500.11937/4001 10.1109/ISSNIP.2005.1595593 IEEE Computer Society Press restricted |
| spellingShingle | Ko, Ming West, Geoff Venkatesh, Svetha Kumar, Mohan Online context recognition in multisensor systems using dynamic time warping |
| title | Online context recognition in multisensor systems using dynamic time warping |
| title_full | Online context recognition in multisensor systems using dynamic time warping |
| title_fullStr | Online context recognition in multisensor systems using dynamic time warping |
| title_full_unstemmed | Online context recognition in multisensor systems using dynamic time warping |
| title_short | Online context recognition in multisensor systems using dynamic time warping |
| title_sort | online context recognition in multisensor systems using dynamic time warping |
| url | http://hdl.handle.net/20.500.11937/4001 |