# Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
## 4 Discussion
Currently, AI is implemented in several fields of medicine and assists in better identifying patients who need extra support and personalized protocols [14, 15, 16]. By leveraging these technologies, it may be possible to develop algorithms that can better differentiate between true alarms and false alarms, reducing the number of unnecessary alerts. This is executed by making the statistical significance value within the algorithm more stringent so that false positives and negatives are minimized [17]. AI algorithms could also learn from historical data, patient-specific information, and real-time contextual data to improve the accuracy of alert notifications, providing more reliable and actionable information to healthcare providers [18]. Additionally, the algorithm must also recognize the patterns related to minor movements or temporary changes so that a false alarm is not triggered. Therefore, a potential future project would be to incorporate AI to continuously receive input data from the sensors and recognize what body postures and movements serve as a baseline and which movement patterns outside of the regular movements call for triggering an alarm. Other studies have also utilized Procrustes analyses in conjunction with machine learning models [19]. Furthermore, evaluating diverse machine learning models on a consistent data set facilitates the identification of the most appropriate model for a particular application through the rigorous assessment of their respective performance, ultimately guiding model selection based on empirical evidence [20]. By creating this iterative routine of data acquisition and statistical significance analysis, we should be able to determine if there is a noticeable change in the patient's movement. If the algorithm can accurately recognize these patterns, its implementation through AI can serve as a promising tool to increase the safety of patients in the healthcare sector. Overall, a trigger alert system based on patient posture and movements in bed has the potential to significantly benefit patient care in hospitals. By facilitating fall prevention, pressure ulcer prevention, early detection of respiratory distress, enhanced monitoring, and personalized care, this system can contribute to improved patient outcomes and safety. However, careful consideration of challenges, such as false alarms, is necessary during implementation. With that said, incorporating such a system into healthcare settings can enhance patient care, promote proactive interventions, and optimize resource allocation. It is important to acknowledge that the current study is in its early stages and does not yet have sufficient significance for immediate practical application. This preliminary work involves a conceptual design, where basic principles were identified based on initial trunk posture data collected using mannequins as a temporary substitute. The next phase will involve testing with non-patient individuals followed by actual patients. These subsequent experiments will provide the necessary data to develop the desired classification algorithms. One of the main challenges in this area of research is that individuals, especially those with neurological impairments, may exhibit unique sequences of movements when adjusting their posture in bed or attempting to get out of bed. As a result, measuring the sequence of Euler angles for trunk movements (which can be performed relatively easily with modern sensors) is not as significant as developing a dependable classification algorithm that can work effectively across different patients. Nevertheless, in healthcare, it is not uncommon for certain practices and procedures to become deeply ingrained in our routines, even when newer technologies and advancements are available. This can be attributed to a combination of factors, including familiarity, resistance to change, and a reliance on established protocols.