# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
## 4 Discussion  
The main utility of utilizing sensor-based surveillance is its effectiveness in continuously monitoring patients while avoiding privacy concerns of other technologies, such as AI-based video surveillance. In one study, the authors utilized a security framework that had successfully maintained both privacy and security in the healthcare setting [13]. The recording system was activated only as a response to health or security concerns. Additionally, only medical professionals (nurses, doctors) may access the encrypted video data. The ability to balance privacy and security in video-based patient monitoring systems. The regression model in the current study did not rely on video surveillance which ensures absolute privacy by utilizing the Movella DOT sensors [22]. In another study which used Internet-of-Things sensors and AI to monitor patient activities and alert healthcare professionals to recent changes [23]. It was found that with the use of the CNN-UUGRU deep learning model, which outperforms existing models in terms of accuracy (97.7%) and precision (96.8%) in identifying human activities based on sensor data [23]. Additionally, this sensor-based system can predict possible falls based on behavioral and physiological data, as discussed in fall prevention research by [PERSON] et al. [24]. These systems not only detect immediate threats but also help predict falls before they occur, utilizing patient history and sensor fusion technology. Incorporating EHR data not only enhances the predictive power of these models but also allows for the creation of personalized care plans based on a combination of sensor-based movement data and detailed patient histories. For example, patients with a history of mobility issues or chronic diseases that impact their movement (such as arthritis or Parkinson's disease) could have higher-risk scores calculated by the model, which can trigger early intervention measures. In line with this, [PERSON] et al. 's systematic review (2020) emphasizes that integrating predictive models into EHR systems significantly improves clinical outcomes, particularly in critical care settings like sepsis management and thrombotic disorder prevention [25]. [PERSON] et al. conducted a systematic review of fall detection and prevention using ML, emphasizing the growing use of wearable sensors, such as IMUs, to capture patient movements. Their review highlights the effectiveness of ML models like SVM, RF, and ANN, all achieving high accuracy rates (up to 99%) in fall detection. By including EMR data, such as mobility restrictions, history of falls, and cognitive impairments, alongside sensor data, the predictive accuracy of the regression models could be optimized. This integration would allow the system to generate alerts that are personalized to the patient's specific condition and treatment regimen, creating a more dynamic and proactive fall prevention system [16]. The selection of the six specific movement categories in this study was based on their relevance to the safety and well-being of bedridden patients. These movements include breathing, seizures, rolling to the right side, rolling to the left side, rolling off the bed from the left, and rolling off the bed from the right. These categories were chosen to provide a comprehensive overview of potential scenarios that these patients might encounter. While there are other movements that patients might perform in bed, such as shifting positions or reaching for objects, these were not included in the current study. Future research could consider these additional movements to further enhance the model's practical applicability. The data collection process was designed to minimize the potential for noise and outliers. A single mannequin was employed to perform six distinct types of movements, and the Euler angles were measured and collected during these movements. This controlled setup significantly reduced the likelihood of extraneous variables introducing noise or causing outliers.