## 5 Conclusions and Future Directions  
In our study, we utilized various machine learning and deep learning models to identify anomalies in human activities. Since the ARAS multi-resident activity dataset only includes normal activities, we generated 50,000 anomalies by considering all possible unusual sensor combinations. We took into account that the activities of one resident directly influence the activities of the other resident and, therefore, used one resident's activity as the input when identifying anomalies in the other resident's activities. To evaluate the performance of the applied classifiers, we examined the effects of five-fold cross-validations and different splitting ratios. Our findings revealed that the Gated Recurrent Unit (GRU) provided the highest performance results, followed by the decision tree (DT), while Gaussian naive Bayes (GNB) yielded the lowest performance results. We also discovered that the splitting ratios of 80:20, 70:30, and 60:40 did not significantly impact performance, except for the k-nearest neighbor (KNN) and GNB classifiers. Additionally, we investigated the computational costs of all applied classifiers from training to prediction. Our findings showed that the Long Short-term Memory (LSTM)-based model took the longest time, followed by the GRU, while the GNB classifier took the least. The study provides a comprehensive guide for researchers, including performance and computational costs, to compare machine learning and deep learning classifiers for anomaly detection in human activities. Our findings can provide valuable insights for future researchers in anomaly detection of human activities. Our analysis revealed performance differences among classifiers, indicating that future studies should focus on optimizing models or exploring alternative architectures to address our limitations. For certain uses, the decision tree (DT) and Gaussian naive Bayes (GNB) classifiers can be made better, or new models can be devised that perform better than the Gated Recurrent Unit (GRU). Our paper also investigates how data splitting ratios affect classifiers such as k-nearest neighbor (KNN) and GNB, which could lead to optimal data-partitioning algorithms. Researchers can optimize model training and prediction based on the computational costs of classifiers. Additionally, exploring ways to reduce the time complexity of the LSTM and GRU models could be beneficial. Further studies can examine the trade-offs between model performance and computational efficiency to help practitioners select the most suitable model for their requirements. Our future objectives include improving anomaly-generation methods, refining existing classifiers, exploring alternative architectures, and optimizing computational costs. These efforts will contribute to developing robust and efficient anomaly-detection systems for human activities. Conceptualization, M.M.R., D.G. and S.B. ; methodology, M.M.R., D.G. and S.B. ; software, M.M.R., D.G. and S.S.; validation, S.B. and M.F. ; formal analysis, S.B. and M.F. ; investigation, M.M.R., D.G. and S.B. ; resources, D.G., S.B. and M.F. ; data curation, M.M.R. and S.S.; writing--original draft preparation, M.M.R. and D.G. ; writing--review and editing, S.B. and M.F. ; visualization, D.G., S.B., S.S. and M.F. ; supervision, S.B. and M.F. ; project administration, S.B. and M.F. All authors have read and agreed to the final version of the manuscript. This research received no external funding. This research investigated activity recognition using the ambient sensing (ARAS) dataset, which is available at: [[https://www.cmpe.boun.edu.tr/aras/](https://www.cmpe.boun.edu.tr/aras/)]([https://www.cmpe.boun.edu.tr/aras/](https://www.cmpe.boun.edu.tr/aras/)), accessed on 1 March 2024.