## 2 Related Works  
Several studies have classified human activity patterns. These activity patterns have been classified using two major categories of sensor devices: vision sensors and wearable sensors. In vision-based HAR, video cameras are used to capture image data. [PERSON] et al. [16] proposed digital image processing techniques including background subtraction, binarization, and morphological operations. Then, a multilayer feed-forward perceptron network is applied to recognize daily human movements and to conduct HAR in indoor environments with a single static camera. [PERSON] et al. [17] obtained video-based invariant features by applying the R transform to depth silhouettes. These silhouettes are encoded into feature values using depth images. Then, the scaling invariant features are calculated by computing the 2D and 1D features using Radon transform and R transform. Finally, principal component analysis and the Hidden Markov Model are applied to the computed features to train and recognize different human activities. [PERSON] et al. [18] focused on the shapelet-based method to recognize four different types of human activities. They evaluated their proposed approach on two public datasets. Experimental results showed their proposed approach was efficient to handle complex activities. Instead of relying on image data, many researchers have designed wearable sensor technologies for activity monitoring and classification. [PERSON] et al. (2019) presented a multi-feature (time and frequency) domain to enhance the classification of eight different human activities from inertial sensors installed in smartphones. [PERSON] et al. (2019) proposed a two-layer diversity-enhanced multi-classifier recognition method from one triaxle accelerometer to classify four different activities. Furthermore, they extracted three-domain features (time, frequency, and AR coefficients) to optimize the performance of the multi-classifier recognition system. [PERSON] et al. (2019) proposed a multifused model to maximize the optimal features values. The extracted features values are then optimized and classified using adaptive moment estimation and a maximum entropy Markov Model. This method achieved an accuracy of 90.91% over the MHEALTH dataset. [PERSON] et al. (2019) introduced a masked reconstruction-based BERT model for human activity recognition activities. The activities are pre-trained as a self-supervised approach. The transformer encoder architecture is also applied for continuous data from body worn sensors and achieved an accuracy of 79.86% over a MOTIONSENSE dataset. [PERSON] et al. (2019) implemented convolution neural network for wearable sensors human activity recognition data. The authors evaluated the implemented methodology on an MHEALTH dataset by using the \"leave one subject out\" validation protocol; they achieved an accuracy rate of 83%. [PERSON] et al. (2019) proposed a physical activity detection model based on Mel Frequency Cepstral Coefficients (MFCC) and statistical features. The extracted features were then optimized and classified with a PSO and SVM algorithm. The implemented methodology was later evaluated over a MOTIONSENSE dataset, giving an accuracy of 87.50%. [PERSON] et al. (2020) presented Logical Hidden Semi-Markov Models (LHSMMs), which are a combination of Logical Hidden Markov Models (LHMMSs) and Hidden Semi-Markov Models, to segment the duration of each activity. Moreover, a comparison of LHSMMs and LHMMs proved that the given method is more robust and has higher probability results than the LHMM method. Optical sensors like digital and bumblebee cameras can be used to improve human lifestyles. However, there are limitations to the use of optical sensors for detecting human activities. With those sensors, the detection of subject's movements is restricted to a particular range and they have privacy issues, e.g., recording in private places like restrooms or intruding on the user's personal life.