human activities refer to the unusual execution of activities, such as deviating from the regular pattern or taking an abnormal duration for each activity. These anomalies should be detected to monitor the health of residents, especially elderly people, who require more surveillance and prompt response during critical times [1; 3]. The detection of anomalies in human activities from the recorded data can provide important indications regarding the health of residents and aid in the timely prevention of health complications. Detecting anomalies in human activities has various applications, such as guiding dementia patients if they miss essential activities like taking meals or medicines and monitoring the health of those staying alone, especially elderly people [4; 5]. Many researchers have proposed various methodologies, including the identification of body positions and actions, as well as the recognition of visual activities, to be significant in the context of anomaly detection [6; 7; 8]. Nonetheless, these approaches exhibit certain limitations. For example, in visual activity recognition, the privacy of the resident is compromised, while in body position and action identification, some devices should be attached to the resident for recording his/her body position and actions, such as standing, sitting, and walking, which is not always convenient [9; 10; 11]. In addition, most research on detecting anomalies in human activity considers a single resident. However, this approach is impractical when multiple residents live together in a house, where the activities of one resident are directly influenced by the activities of others [12; 13; 14]. Artificial intelligence has opened the door to more efficient identification of unusual human activities within smart homes. Leveraging machine learning classifiers such as decision trees, naive Bayes, gradient boosting, random forest, k-nearest neighbors, and Support Vector Machines simplifies the process of recognizing anomalies [7; 15; 16; 17; 18; 19; 20]. The classifiers work by analyzing sensor data, which enables accurate classification of a resident's normal and abnormal activities. This approach has proven to be highly effective in ensuring the safety and security of smart home residents [21]. Later, while deep learning has been effective in classification tasks, researchers have applied neural network (NN)-based techniques such as Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs) for detecting anomalies in human activities [22; 23; 24; 25]. However, multi-activity datasets are often converted into binary class datasets, which can result in imbalanced datasets as only a few abnormal behaviors are generated, and do not represent all possible anomaly situations [26; 27; 28]. In many studies, only normal activities are used for training and a threshold value is computed by estimating the loss to classify data as normal or anomaly [29; 30]. An input is considered an anomaly when the summed loss is greater than the threshold value [22]. Although these techniques classify normal and abnormal activities well for single residents, they have not reported their performance on multi-resident datasets. Furthermore, we have encountered a challenge in our search to identify a comprehensive resource that can assist us in assessing the performance and computational complexity of various classifiers for the task of detecting anomalies related to multiple residents in a human-centric context. In this review study, we conducted thorough experiments over all the popular machine learning classifiers such as decision tree (DT), naive Bayes (NB), gradient boosting (GB), Light Gradient Boosting (LGB), random forest (RF), k-nearest neighbors (KNN), Support Vector Machine (SVM), Linear Regression (LR), and two RNN-based models utilizing Long Short-term Memory (LSTM), and the Gated Recurrent Unit (GRU) for detecting anomalies in two-resident human activities.