# Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
## 4 Discussion  
A notification signal system that identifies patient positions and movements in their bed has the capacity to substantially enhance comprehensive patient care within a hospital environment, as outlined below, with various critical factors that could result in enhanced healthcare outcomes if put into practice. Improved Fall Prevention: Falls are a significant concern in healthcare facilities, especially among elderly or physically compromised patients. Not only do falls lead to possible physical injuries, they can also lead to psychological sequelae such as an increased fear of falling which will ultimately delay patient recovery [10]. Pressure Ulcer Prevention: Prolonged immobility or incorrect positioning can lead to the development of pressure ulcers, which are a major concern for bed-ridden or immobilized patients. Developing a pressure ulcer can cause unnecessary pain, increase the length of stay at the hospital, and can even contribute to the rapid mortality of certain patients [11]. Early Detection of Respiratory Distress: Changes in patient posture and movements can provide valuable insights into their respiratory status. For example, sudden changes in respiratory patterns or increased restlessness may indicate respiratory distress. Additionally, for patients who suffer from acute respiratory distress syndrome, a change in movement, specifically being placed in the prone position, could help in lowering their respiratory rate and improve oxygenation [12]. The aforementioned categories share a common theme of an abnormal change in body position. By utilizing a trigger alert system that monitors patient postures and movements in bed, healthcare providers can receive real-time notifications when a patient is in distress, which will ultimately allow for timely interventions. This also serves as an add on to enhance patient monitoring and patient-specific care. Due to the uniqueness of every patient, the additional monitoring of patient postures and movements can provide a more comprehensive understanding of the patient's condition and the recognition of specific patient trends in movement so that a tailored care plan can be implemented to enhance patient satisfaction, comfort, and overall care quality. Euler angles, which represent an object's orientation in three-dimensional space, have a significant importance in this study. They are utilized to assess and monitor patients' body postures in bed over a specific time frame. The goal is to identify distinct patterns in these angle variations that are indicative of various movements. Euler angles are deemed suitable for analyzing body movements because they offer a straightforward and concise method for representing the orientation of the human body in three-dimensional space. With the help of sensors, changes in Euler angles that occur during common movements performed by patients can be examined. With proper consideration given to the differences between experimental and computational methods [13], analyzing data using Euler angles can be a dependable approach for algorithms to effectively evaluate body position and movement. This enables accurate monitoring of patient conditions and allows for necessary adjustments to improve their comfort and overall well-being. Moreover, the study involves a comparison of dissimilarity values between various body movements by analyzing Euler angles, which could contribute to the development of a trigger alert system based on these values. While the use of sensors to measure body posture can provide improved patient safety and response times, there are still several challenges that follow. One area that needs to be addressed is the occurrence of false alarms. False alarms can lead to alert fatigue among healthcare providers, where they become desensitized to the constant influx of alarms and may miss critical alerts. This can potentially jeopardize patient safety. To address this issue, advancements in artificial intelligence (AI) and machine learning could be explored.