# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
## 4 Discussion
Therefore, additional preprocessing steps such as data augmentation or noise filtering were not deemed necessary. However, it is acknowledged that in real-life scenarios, additional preprocessing methods may be required to account for potential artifacts or outliers. This is an area of focus for future research. Sensor placement, noise levels, and sampling rates are pivotal factors that can influence the accuracy and precision of ML models. The positioning of the sensor can affect the quality of the data collected, as different placements may capture varying aspects of the movements. Noise levels can introduce an element of variability in the data, potentially leading to misclassification of movements. The sampling rates can impact the granularity of the data, with higher rates providing more detailed data that could enhance the model's performance. However, in the context of this study, the movements were simulated by a mannequin in a controlled environment, which mitigated the influence of these factors on the model's performance. The constraints imposed by the limited size of the dataset are recognized in this study. Data collection in this domain can be challenging due to privacy considerations and the sensitive nature of the information. Although the dataset is of limited size, it is regarded as representative and provides useful insights. It has been utilized to train an ML model capable of detecting and classifying different types of movements in bed-based patients. To address the issue of imbalanced data, the SMOT was utilized. While the quality of synthetic data may raise concerns, it is important to highlight that SMOT is a widely accepted approach for handling imbalanced datasets in ML. Its effectiveness in enhancing the performance of ML models has been demonstrated in numerous studies [26; 27]. Furthermore, patient variability was not specifically addressed in this study, indicating an area for future research. This study primarily relies on internal validation techniques, such as K-fold cross-validation and learning curves, to assess the model's performance. The learning curves of the BR model illustrate the convergence of the training and validation curves, with the validation curve consistently staying slightly below the training curve. This pattern indicates that the model, in spite of its complexity, does not appear to be overfitting. Nonetheless, it is acknowledged that the generalizability of the model is a subject that requires further exploration. Future research directions are set to include testing the model on human subjects and in real-life environments to verify its applicability in a variety of healthcare settings. The computational efficiency of the proposed model is characterized by its ability to be trained, validated, and tested within a short time frame on a single PC unit. This suggests that the model is computationally efficient and could potentially be implemented in a real-time hospital setting. However, the specific hardware and software requirements for such an application have not been evaluated in this study. Future research could focus on determining these requirements to facilitate the implementation of the model in a real-time hospital setting. It is important to note that the primary focus of this study was the development and testing of the model, and as such, the specific computational requirements for real-time application were not evaluated. Regarding cost-effectiveness, while it was not directly evaluated in this study, the sensors used are inexpensive and reusable, suggesting that the model could potentially be more cost-effective than traditional video monitoring systems. Moreover, while the sensors utilized are effective in capturing patient movements, they may not be able to capture all types of patient behavior or conditions. For instance, they might not be able to detect subtle changes in a patient's condition that could be picked up by a human observer.