Gesture Recognition with a 3-D Accelerometer. Wu, J H, Pan, G, Zhang, D Q, Qi, G D, & Li, S J 5585:25–38. Paper doi abstract bibtex Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naive Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.
@article{wuGestureRecognition3D2009,
title = {Gesture {{Recognition}} with a 3-{{D Accelerometer}}},
volume = {5585},
issn = {0302-9743},
url = {https://www.thomsoninnovation.com/tip-innovation/%5Cnhttps://www.thomsoninnovation.com/tip-innovation/recordView.do?datasource=WOK&category=LIT&selRecord=1&totalRecords=1&databaseIds=WOS&idType=uid/recordid&recordKeys=000270444600003/WOS:000270444600003},
doi = {10.1007/978-3-642-02830-4_4},
abstract = {Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naive Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38\% for the 4 direction gestures and 95.21\% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93\% for 4 gestures and 89.29\% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.},
journaltitle = {Ubiquitous Intelligence and Computing, Proceedings;5585: 25-38 2009},
date = {2009},
pages = {25--38},
author = {Wu, J H and Pan, G and Zhang, D Q and Qi, G D and Li, S J},
file = {/home/dimitri/Nextcloud/Zotero/storage/GQAZSX9Z/Wu et al. - 2009 - Gesture recognition with a 3-d accelerometer.pdf}
}
Downloads: 0
{"_id":"qxqAyyjDyWLxvbkgs","bibbaseid":"wu-pan-zhang-qi-li-gesturerecognitionwitha3daccelerometer","authorIDs":[],"author_short":["Wu, J H","Pan, G","Zhang, D Q","Qi, G D","Li, S J"],"bibdata":{"bibtype":"article","type":"article","title":"Gesture Recognition with a 3-D Accelerometer","volume":"5585","issn":"0302-9743","url":"https://www.thomsoninnovation.com/tip-innovation/%5Cnhttps://www.thomsoninnovation.com/tip-innovation/recordView.do?datasource=WOK&category=LIT&selRecord=1&totalRecords=1&databaseIds=WOS&idType=uid/recordid&recordKeys=000270444600003/WOS:000270444600003","doi":"10.1007/978-3-642-02830-4_4","abstract":"Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naive Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.","journaltitle":"Ubiquitous Intelligence and Computing, Proceedings;5585: 25-38 2009","date":"2009","pages":"25–38","author":[{"propositions":[],"lastnames":["Wu"],"firstnames":["J","H"],"suffixes":[]},{"propositions":[],"lastnames":["Pan"],"firstnames":["G"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["D","Q"],"suffixes":[]},{"propositions":[],"lastnames":["Qi"],"firstnames":["G","D"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["S","J"],"suffixes":[]}],"file":"/home/dimitri/Nextcloud/Zotero/storage/GQAZSX9Z/Wu et al. - 2009 - Gesture recognition with a 3-d accelerometer.pdf","bibtex":"@article{wuGestureRecognition3D2009,\n title = {Gesture {{Recognition}} with a 3-{{D Accelerometer}}},\n volume = {5585},\n issn = {0302-9743},\n url = {https://www.thomsoninnovation.com/tip-innovation/%5Cnhttps://www.thomsoninnovation.com/tip-innovation/recordView.do?datasource=WOK&category=LIT&selRecord=1&totalRecords=1&databaseIds=WOS&idType=uid/recordid&recordKeys=000270444600003/WOS:000270444600003},\n doi = {10.1007/978-3-642-02830-4_4},\n abstract = {Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naive Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38\\% for the 4 direction gestures and 95.21\\% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93\\% for 4 gestures and 89.29\\% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.},\n journaltitle = {Ubiquitous Intelligence and Computing, Proceedings;5585: 25-38 2009},\n date = {2009},\n pages = {25--38},\n author = {Wu, J H and Pan, G and Zhang, D Q and Qi, G D and Li, S J},\n file = {/home/dimitri/Nextcloud/Zotero/storage/GQAZSX9Z/Wu et al. - 2009 - Gesture recognition with a 3-d accelerometer.pdf}\n}\n\n","author_short":["Wu, J H","Pan, G","Zhang, D Q","Qi, G D","Li, S J"],"key":"wuGestureRecognition3D2009","id":"wuGestureRecognition3D2009","bibbaseid":"wu-pan-zhang-qi-li-gesturerecognitionwitha3daccelerometer","role":"author","urls":{"Paper":"https://www.thomsoninnovation.com/tip-innovation/%5Cnhttps://www.thomsoninnovation.com/tip-innovation/recordView.do?datasource=WOK&category=LIT&selRecord=1&totalRecords=1&databaseIds=WOS&idType=uid/recordid&recordKeys=000270444600003/WOS:000270444600003"},"downloads":0},"bibtype":"article","biburl":"https://raw.githubusercontent.com/dlozeve/newblog/master/bib/all.bib","creationDate":"2020-01-08T20:39:39.030Z","downloads":0,"keywords":[],"search_terms":["gesture","recognition","accelerometer","wu","pan","zhang","qi","li"],"title":"Gesture Recognition with a 3-D Accelerometer","year":null,"dataSources":["3XqdvqRE7zuX4cm8m"]}