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Human action recognition has been one of the most active fields of researchin computer vision for last years. Two dimensional action recognition methodsare facing serious challenges such as occlusion and missing the third dimensionof data. Development of depth sensors has made it feasible to track positionsof human body joints over time. This paper proposes a novel method of actionrecognition which uses temporal 3D skeletal Kinect data. This method introducesthe definition of body states and then every action is modeled as a sequence ofthese states. The learning stage uses Fisher Linear Discriminant Analysis (LDA)to construct discriminant feature space for discriminating the body states.Moreover, this paper suggests the use of the Mahalonobis distance as anappropriate distance metric for the classification of the states of involuntaryactions. Hidden Markov Model (HMM) is then used to model the temporaltransition between the body states in each action. According to the results,this method significantly outperforms other popular methods, with recognitionrate of 88.64% for eight different actions and up to 96.18% for classifyingfall actions.

Recognizing Involuntary Actions from 3D Skeleton Data Using Body States