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HAR is not a novel concept and several studies have been conducted in this domain. Therefore, in this paper, we developed an indoor monitoring system for physical activity recognition. People are recommended to stay at home and avoid going outside for exercises or other physical activities in these situations. Many countries enforced lockdown in the country to control the spread of COVID disease which limits the participation of people in healthy activities.
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To prevent the spread of Coronavirus infection, many safeties measure has been taken worldwide such as home confinement, banning gatherings and visiting crowded public places, and avoiding outdoor activities. According to the recent report of the World Health Organization (WHO), there are almost 270 million positive cases and 5.3 million deaths occurs till now due to COVID-19 disease. The outbreak of Coronavirus has begun in December 2019, and it spread out by human-to-human interaction which results in huge loss of human life. Due to the current situation of COVID-19, the government of many countries-imposed lockdowns and home confinement which constrained the people to stay at home and avoid physical activities in public places. The involvement of the elder people in particular physical activities has positive effects on mental state, satisfaction, quality of life, and physical well-being. Numerous studies demonstrated the positive influence of physical activities on people’s quality of life, especially for elderly people. However, human actions are the collection of various joints that move over time and these joint data can be used for the recognition of activity. In addition, RGBD data from the Kinect sensor may be utilized to create a human skeleton model with body joints. Kinect-based action recognition tackles the light-environment problem and accurately tracks the skeleton joints during activity, and it also offers a variety of information, such as depth and skeleton information, that a standard video camera failed to provide. To avoid the problem of light variation, a low-cost RGB-D camera, such as Microsoft Kinect, has been made possible the recent advancement in activity recognition. Recognition of activity through common cameras may be a problem of difficulty in recognition due to low light environment or darkness. The current literature studies focus to recognize activities using video sequences collected by standard RGB cameras and surveillance cameras.
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Instead of focusing on wearable sensor based HAR, numerous studies incorporated video sensor technologies like RGB cameras to monitor and recognize human activity. In wearable sensors based HAR, many sensors are attached to a subject’s body for a prolonged period, which is cumbersome for the subject’s body and the subject can’t move comfortably because of many wire connections, as well as it is expensive in terms of energy consumption and device configuration. In the light of literature, activity recognition is performed based on wearable sensors and vision sensors. HAR gained more attention from researchers in video analysis and its different applications in various domains such as indoor gym physical activities, surveillance systems, and health care systems. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Many Artificial intelligence-based models are developed for activity recognition however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction.