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 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.