Abstract
Recent developments in sensor technologies enable physical activity recognition (PAR) as an essential tool for smart health monitoring and for fitness exercises. For efficient PAR, model representation and training are significant factors contributing to the ultimate success of recognition systems because model representation and accurate detection of body parts and physical activities cannot be distinguished if the system is not well trained. This paper provides a unified framework that explores multidimensional features with the help of a fusion of body part models and quadratic discriminant analysis which uses these features for markerless human pose estimation. Multilevel features are extracted as displacement parameters to work as spatiotemporal properties. These properties represent the respective positions of the body parts with respect to time. Finally, these features are processed by a maximum entropy Markov model as a recognition engine based on transition and emission probability values. Experimental results demonstrate that the proposed model produces more accurate results compared to the state-of-the-art methods for both body part detection and for physical activity recognition. The accuracy of the proposed method for body part detection is 90.91% on a University of Central Florida’s (UCF) sports action dataset and, for activity recognition on a UCF YouTube action dataset and an IM-DailyRGBEvents dataset, accuracy is 89.09% and 88.26% respectively.
Highlights
Assistive technologies for human locomotion tracking provide independent mobility, social participation and health benefits [1]
We explored body part detection accuracies with respect to ground truth
A comparison of overall results shows that the proposed method achieved a significant improvement with recognition results as high as 89.09% and 88.26% over other methods as shown in University of Central Florida (UCF) YouTube Actions (%)
Summary
Assistive technologies for human locomotion tracking provide independent mobility, social participation and health benefits [1] These benefits have emerged as a major research gain in worldly application domains such as violence detection, home automation systems, customer surveillance, virtual reality and physical fitness [2,3]. The tracking and recognition of people’s physical activities remain problematic due to the human body’s articulated nature, degrees of freedom between joints, partial occlusion and varying scales normalization [4]. Several modules such as rigid body configuration, body-part landmarks, homograph estimation, and optimal feature descriptors are introduced to minimize these difficulties. Human bodies vary a lot in shape and size
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