Multi-Sensor-Based Action Monitoring and Recognition via Hybrid Descriptors and Logistic Regression

In the fields of body-worn sensors and computer vision, current research is being done to track and detect falls and activities of daily living using the automatic recognition of human actions. In the area of human–machine communication, different combinations of sensors and communication technologies are often used to capture human action. Many researchers have also worked with artificial intelligent systems to detect actions, understand scenes, and implement systems that are more efficient in human action recognition. Although effective approaches are needed to detect outdoor activities with the combination of human actions, feature extraction can be quite a complicated task in a human activity recognition system development. Thus, this paper proposed a solution to detect human activities via hybrid descriptors based on robust features and accurate results. In this study, complex backgrounds, including multiple humans in video frames, were detected. First, inertial signal and video frames are pre-processed using denoising techniques, after which the frames are used to remove the background by detecting human motions and extracting the silhouettes. Then, these silhouettes are further used to extract the human body key points to make the human skeleton. Then the time and frequency domain features are extracted for inertial signals, and geometric features are extracted for the skeleton body points. Finally, multiple feature sets are combined and fed into a zero order optimization model, after which logistic regression is utilized to recognize each action. The proposed system has been evaluated on three benchmark datasets, including, the UP Fall dataset, the University of Rzeszow Fall dataset, and the SisFall dataset and proved its significance by achieving accuracy of 91.51%, 92.98%, and 90.23%, on the aforementioned datasets respectively.