# Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
## 5 Conclusions  
In the healthcare industry, medical professionals are often busy and have limited time to monitor patients. Currently, devices for monitoring physiological signs serve as a helpful tool in measuring vital indicators and alerting staff to significant changes. Call systems are also provided next to patients so they can request assistance when needed. However, some patients may be unable to use these systems due to their distress. Furthermore, patient posture and movement patterns are frequently overlooked despite their potential role in identifying possible complications. Using a sensor device to monitor patient movements shows promise as an effective solution. Our research indicates that data on different body postures and movements can be collected and distinguished from one another, making it possible to develop a trigger alert system based on such values. By collecting information on posture and movement patterns, medical staff can be notified of urgent situations caused by abnormal changes in movement. Implementing such a notification system would improve response times and ultimately enhance patient outcomes.  
Using simple, affordable sensors to monitor bed-bound patients and their movements in hospitals can offer several advantages and disadvantages. Such sensors can be a valuable addition to patient care in hospitals, offering continuous monitoring and improved safety. However, their effectiveness depends on the careful selection, implementation, and integration of the sensors, along with ongoing training for healthcare providers to use the data effectively while addressing the potential disadvantages and privacy concerns.  
One of the main challenges is that individuals, especially those with neurological impairments, may exhibit unique sequences of movements when adjusting their posture in bed. Hence, developing a reliable classification algorithm that can work effectively across different patients poses a challenge. Notably, false alarms could become a significant issue with such systems. The algorithm must recognize minor or temporary movements to ensure false alarms are not triggered. Another challenge pertains to the operational implementation of such systems.  
Conceptualization, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; methodology, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; software, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; validation, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; formal analysis, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; investigation, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; resources, M.T.; data curation, M.T.; writing--original draft preparation, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; writing--review and editing, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; visualization, J.M., R.J., G.K., P.S., C.C., T.D. and M.T.; supervision, M.T.; project administration, M.T.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ICU & Intensive Care Unit \\ ENU & East-North-Up \\
3D & Three-Dimensional \\ AI & Artificial Intelligence \\ \end{tabular}