## 4 Discussion  
This proof-of-concept study found that a motion detector, attached under the ICU bed and not on the subject's body, could help identify an emergency situation even if the classical monitoring modalities are disconnected. Motions were detected with high sensitivity (96%) and reasonable specificity (75%), with an error rate of 13.3% for incorrect assignments. Associations were similar if solely the emergency scene (\"motionlessness with no or shallow breathing\") was tested (correct assignments 85.3%; error rate 14.7%). False alarms of various origins are a constant challenge in the ICU. Alarm fatigue is a consequence of inappropriate alarms, and many concepts have been proposed to overcome false alarms and reduce alarm fatigue [6; 15; 16; 17; 18]. If the monitoring modalities are disconnected, as often seen in delirious patients, no false alarms but alarm notifications are \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Univariable Analysis** & **Multivariable Analysis** \\  & **OR (95\% CI), \(p\)** & **OR (95\% CI), \(p\)** \\ \hline Height, per centimeter & 1.01 (0.97; 1.06); \(p\) = 0.53 & \\ \hline Weight, per kilogram & 1.00 (0.99; 1.02); \(p\) = 0.75 & \\ \hline Male sex, yes vs. no & 3.43 (1.76; 6.7); \(p\) \(<\) 0.001 & 4.4 (2.06; 9.4); \(p\) \(<\) 0.001 \\ \hline Mobile phone scene, yes vs. no & 11.1 (4.9; 25.1); \(p\) \(<\) 0.001 & 14.2 (5.8; 34.9); \(p\) \(<\) 0.001 \\ \hline \hline \end{tabular} Potential confounders of a false test result were tested in univariate logistic regression. Significant factors were included in the multivariable logistic regression analysis. Abbreviations: CI—confidence interval; OR—odds ratio.  
\end{table} 
Table 6: Determinants of false test results in logistic regression analysis. given. This reminds the medical staff that the monitoring modalities must be reattached. However, if such alarms are triggered too often, alarm fatigue develops, and the reaction times increase [19; 20; 21]. During the reaction time, patients are left unmonitored. If motion detectors become part of the monitoring system, detection of patient motions could be used as an indirect confirmation of patients' vitality until the conventional alarm system has been reattached. In particular, the reliable detection of motionlessness (\"zero or near-zero lines\") may indicate the patient's unconsciousness and trigger immediate high-level alarms, thereby bridging the observational gap created by the detachment of the conventional monitoring system. While the idea of using motion detectors to monitor ICU patients is not new [8; 9; 10; 22; 23; 24; 25], their application to monitor potential emergencies has not been investigated so far. Importantly, motion detectors have not been approved medically for this indication and are, therefore, unsuitable as a solitary monitoring solution. One of the most frequent indications of testing motion detectors at the adult ICU was to measure activity levels, especially in patients with mobility constraints [8; 9; 10]. As many ICU patients may develop severe critical illness neuropathy due to sedation and immobility, monitoring of the activity level is of prognostic importance [26]. The most common techniques utilized for activity monitoring at the ICU have been wearable accelerometers [10] and imaging techniques [8; 9; 27]. As wearable devices would probably not be tolerated in patients, who intentionally remove their standard monitoring system, imaging salutation appears more attractive. [PERSON] et al. tested a noninvasive mobility sensor using an RBG-D (red-blue-green-depth) camera in patients at the ICU [8; 9]. Different activities such as lying in bed, sitting and exercising in bed, out-of-bed activities, and walking in an ICU patient room were tested. Automated prediction analysis showed high agreement between the motions detected by the sensor and the expert assessment [8]. Inaccurate estimations occurred mainly in the \"lying in bed\" and \"sitting or exercising in bed\" categories.