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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. 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. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognitiondoi:10.3390/s22010323 pmid:35009865 pmcid:PMC8749555 fatcat:3zrdns3pcfecvpxgmdsngdsgmm