# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
## 5 Conclusions  
The primary objective of this study was to address the challenge of continuous patient monitoring while ensuring privacy. The proposed methodology involved the use of Movella DOT sensors, which provided a privacy-preserving alternative to AI-based video surveillance. This sensor-based surveillance system proved to be relatively effective in monitoring patients' movements in bed. The experiments conducted in this study involved the evaluation of three distinct models: DTR, GBR, and BR. The performance of these models was assessed using metrics such as Accuracy, R\({}^{2}\) score, and MSE. Among these, the ensemble model, BR, outperformed both DTR and GBR in terms of accuracy, R\({}^{2}\) score, and MSE. The confusion matrix heatmap reveals some misclassifications, particularly between the labels \"Breathing\" and \"Seizure\". These misclassifications can be attributed to the inherent similarities between these two movements, which involve rhythmic patterns that differ primarily in frequency rather than magnitude. This is a known limitation of machine learning models, which can struggle to differentiate between classes with similar features. Despite these misclassifications, the overall performance of the model is reasonable, as evidenced by the majority of predictions falling on the diagonal, indicating correct predictions. The results demonstrated the effectiveness of the proposed methodology. The BR model achieved an accuracy of 0.950, an R\({}^{2}\) score of 0.996 for the training data, and an R\({}^{2}\) score of 0.959 for the test data. The MSE for the training data was 0.012, and for the test data, it was 0.112. The discussion highlighted the utility of sensor-based surveillance and the potential of integrating AI systems with EHR systems. The integration of EHR data can enhance the predictive power of the models and allow for the creation of personalized care plans. However, the study recognized the limitations imposed by the limited size of the dataset. Data collection in this domain can be challenging due to privacy considerations and the sensitive nature of the information. To address the issue of imbalanced data, the SMOT was utilized. Future work will focus on expanding the dataset and exploring other ML models. The quality of synthetic data generated by SMOT could also be further investigated. Despite these limitations, the study provides useful insights into the potential of sensor-based surveillance in healthcare settings. As with any ML model, a certain degree of misclassification is anticipated, particularly in the healthcare domain where the complexity and variability of human physiological pat terns can lead to overlaps between different conditions. However, these misclassifications do not detract from the clinical applicability of ML models. Instead, they underscore the necessity of using ML as a supportive tool, not a replacement, for human judgement. In real-time monitoring environments, the model's results should not be relied upon absolutely, but rather used to assist healthcare personnel in making more informed decisions. Strategies such as setting thresholds for alarm activation, incorporating feedback mechanisms, and providing training to healthcare personnel on the interpretation of the model's outputs could help mitigate such errors. All authors ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and M.T.) contributed to all aspects of this work. This includes but is not limited to conceptualization, methodology, software management, validation, formal analysis, investigation, resources management, data curation, writing--original draft preparation, writing--review and editing, and visualization. The supervision, and project administration were handled by [PERSON] All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.