Gait Type Analysis Using Dynamic Bayesian Networks
This paper focuses on gait abnormality type identification—specifically, recognizing antalgic gait. Through experimentation, we demonstrate that detecting an individual’s gait type is a viable biometric that can be used along with other common biometrics for applications such as...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/10/3329 |
| _version_ | 1811279806530060288 |
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| author | Patrick Kozlow Noor Abid Svetlana Yanushkevich |
| author_facet | Patrick Kozlow Noor Abid Svetlana Yanushkevich |
| author_sort | Patrick Kozlow |
| collection | DOAJ |
| description | This paper focuses on gait abnormality type identification—specifically, recognizing antalgic gait. Through experimentation, we demonstrate that detecting an individual’s gait type is a viable biometric that can be used along with other common biometrics for applications such as forensics. To classify gait, the gait data is represented by coordinates that reflect the body joint coordinates obtained using a Microsoft Kinect v2 system. Features such as cadence, stride length, and other various joint angles are extracted from the input data. Using approaches such as the dynamic Bayesian network, the obtained features are used to model as well as perform gait type classification. The proposed approach is compared with other classification techniques and experimental results reveal that it is capable of obtaining a 88.68% recognition rate. The results illustrate the potential of using a dynamic Bayesian network for gait abnormality classification. |
| first_indexed | 2024-04-13T01:02:23Z |
| format | Article |
| id | doaj.art-af6a8655b7ba42ff9b56fabed9f84ec7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T01:02:23Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-af6a8655b7ba42ff9b56fabed9f84ec72022-12-22T03:09:26ZengMDPI AGSensors1424-82202018-10-011810332910.3390/s18103329s18103329Gait Type Analysis Using Dynamic Bayesian NetworksPatrick Kozlow0Noor Abid1Svetlana Yanushkevich2Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaThis paper focuses on gait abnormality type identification—specifically, recognizing antalgic gait. Through experimentation, we demonstrate that detecting an individual’s gait type is a viable biometric that can be used along with other common biometrics for applications such as forensics. To classify gait, the gait data is represented by coordinates that reflect the body joint coordinates obtained using a Microsoft Kinect v2 system. Features such as cadence, stride length, and other various joint angles are extracted from the input data. Using approaches such as the dynamic Bayesian network, the obtained features are used to model as well as perform gait type classification. The proposed approach is compared with other classification techniques and experimental results reveal that it is capable of obtaining a 88.68% recognition rate. The results illustrate the potential of using a dynamic Bayesian network for gait abnormality classification.http://www.mdpi.com/1424-8220/18/10/3329gaitdynamic Bayesian networkMicrosoft Kinect sensorbiometricshuman identification |
| spellingShingle | Patrick Kozlow Noor Abid Svetlana Yanushkevich Gait Type Analysis Using Dynamic Bayesian Networks Sensors gait dynamic Bayesian network Microsoft Kinect sensor biometrics human identification |
| title | Gait Type Analysis Using Dynamic Bayesian Networks |
| title_full | Gait Type Analysis Using Dynamic Bayesian Networks |
| title_fullStr | Gait Type Analysis Using Dynamic Bayesian Networks |
| title_full_unstemmed | Gait Type Analysis Using Dynamic Bayesian Networks |
| title_short | Gait Type Analysis Using Dynamic Bayesian Networks |
| title_sort | gait type analysis using dynamic bayesian networks |
| topic | gait dynamic Bayesian network Microsoft Kinect sensor biometrics human identification |
| url | http://www.mdpi.com/1424-8220/18/10/3329 |
| work_keys_str_mv | AT patrickkozlow gaittypeanalysisusingdynamicbayesiannetworks AT noorabid gaittypeanalysisusingdynamicbayesiannetworks AT svetlanayanushkevich gaittypeanalysisusingdynamicbayesiannetworks |