is the action in the video?\" In recent years, researchers have focused on \"dense sensing\", where many sensors are distributed in the environment to capture human activities [2]. Recently, various methods have been introduced to solve the human activity recognition problem with efficient accuracy. The study by [PERSON] et al. (2020) improved human activity recognition based on sensor data. This model employs a hybrid feature selection model using sequential floating forward search (SFFS) to select the most representative features. It makes use of a multiclass support vector machine (SVM) to classify the observations into its labels. This model achieved higher classification accuracy than traditional machine learning classifiers [3]. The study by [PERSON] et al. (2018) presented a technique for recognizing human activities based on a multi-label classification of various accelerometer sensor positions. In order to achieve high accuracy, this technique focuses on determining the optimal sensor placement in relation to the performed activity.It can recognize six different types of activities. Furthermore, it compares among traditional multi-class and multi-label classification methods such as rotation forest [4], SVM [5], and Iterative Dichotomizer 3 (ID3) [6]. However, this model relies on traditional machine learning classifiers, so it searches for the one that gives the highest classification accuracy. Recently, ([PERSON] and [PERSON] 2020) suggested a novel HAR approach aimed at personalizing human activity recognition based on deep triplet embedding. The subject triplet selection makes use of triplet neural networks for personalized activity recognition. Therefore, it improves the performance of the training process. However, the latency time increased as the size of the input data increased [7]. Also, ([PERSON] and [PERSON] 2021) suggested that Resnet and 3D CNN recognize human activities in real-time. And the accuracy of recognition can be improved by combining ResNet with 3D CNN. As a result, a technique for identifying, tracking, and detecting real-time human movements has been created [8]. The study by [PERSON] et al. (2013) describes a system for identifying and reporting unusual behaviors. The suggested approach uses the situation awareness paradigm to identify unusual behavior. The architecture adds temporal information, including duration and frequency, to the recognition process [9]. [PERSON] and [PERSON]'s (2017) offer a method for recognizing that Alzheimer's disease is the most prevalent kind of dementia. Patients with this type of dementia may exhibit symptoms like restless sleep, muscle rigidity, or other standard Alzheimer's movement anomalies. They have concentrated on the alterations connected to sleep problems. The fact that sleep disorders are not well-known makes it challenging to create software that can recognize them. Using deep learning algorithms, they were able to solve the issue of automatically identifying sleep abnormalities in Alzheimer's disease patients [10]. [PERSON] et al. (2017) show the development of an intelligent human activity identification system. Each level of the suggested system employed a variety of digital image processing techniques, including background subtraction, binarization, and morphological operations. Based on the human activity features database that was taken from the frame sequences, a strong neural network was constructed. A multi-layer feed forward perceptron network was used to categorize the dataset's activities [11]. This article aims to determine the set of human activities based on a predefined, finite set of activities. Well-known classification models could be used to solve this problem. Therefore, the process of recognition consists of two phases: the training and testing phases. In the training phase, well-known samples of human activities are used to generate a supervised model. This model can differentiate among the characteristics of human activities.