## 5 Discussion  
Remote monitoring systems based on sensor networks in smart homes for the elderly have the potential to revolutionize the way care is provided to this population. By using a network of wireless sensors to gather data on the individual's activity and environment, these systems can provide valuable insights into the individual's health and well-being. The use of wearable sensors to monitor vital signs such as heart rate and oxygen saturation, in particular, can provide early warning signs of potential health issues, allowing for prompt intervention and medical attention. This can be especially important for older adults who may be at risk of falls or other accidents, as well as those with chronic conditions such as heart disease or diabetes. Furthermore, the use of machine learning and artificial intelligence techniques to analyze the data collected by the sensors can also provide additional benefits. These technologies can be used to detect patterns and anomalies in the individual's behavior or vital signs, which can provide early warning signs of potential issues. This can include identifying changes in sleep patterns that may indicate the onset of a sleep disorder, or detecting a decline in mobility that may increase fall risks. Additionally, these systems can also be used to monitor medication adherence, which can be especially important for older adults who may have difficulty remembering to take their medication. In addition, the data collected by the systems can also be used to provide more personalized care, based on the individual's specific needs and preferences. By monitoring the individual's activities and movements, for instance, the systems can identify patterns of behavior that can be used to adjust the individual's care plan. This can include adjusting the lighting or temperature in the home, or providing reminders to take medication or perform other important tasks. Overall, remote monitoring systems with sensor networks in smart homes for the elderly can provide a wide range of benefits, including improved health outcomes, increased safety and security, and more personalized care. By tracking vital signs and activity, \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Predicted Class** & **Metric** & **Coefficient** & \(p\)**-Value** & **Odd Radios** \\ \hline \multirow{6}{*}{High impact} & Intersection & \(-133.579\) & 0.040 & \multirow{2}{*}{1.190} \\  & HR & 0.174 & \(<\)0.001 & 1.190 \\  & BF & 0.607 & \(<\)0.001 & 1.835 \\  & T & 4.877 & 0.007 & 131.209 \\  & REMS & \(-\)0.380 & 0.073 & 0.684 \\  & OS & \(-\)0.652 & \(<\)0.001 & 0.521 \\  & TST & \(-\)1.180 & 0.028 & 0.307 \\  & S & 0.357 & 0.005 & 1.429 \\ \hline \multirow{6}{*}{Middle impact} & Intersection & \(-\)106.781 & 0.062 & \multirow{2}{*}{1.102} \\  & HR & 0.097 & 0.003 & 1.102 \\ \cline{1-1}  & BF & 0.330 & 0.007 & 1.391 \\ \cline{1-1}  & T & 3.249 & 0.039 & 25.762 \\ \cline{1-1}  & REMS & \(-\)0.475 & 0.008 & 0.622 \\ \cline{1-1}  & OS & \(-\)0.162 & 0.186 & 0.850 \\ \cline{1-1}  & TST & \(-\)0.430 & 0.279 & 0.650 \\ \cline{1-1}  & S & 0.053 & 0.579 & 1.054 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Significant estimation parameters result from the multinomial regression with the reference class low impact. these systems can provide early warning signs of potential issues, allowing for prompt intervention and medical attention. Additionally, the use of machine learning and artificial intelligence techniques can provide additional benefits, such as identifying patterns and anomalies in the individual's behavior or vital signs, and providing more personalized care based on the individual's specific needs and preferences. A remote sensor network monitoring system can have a significant impact on alarm feedback and recommendations for users to improve their sleeping habits. The system allows for continuous monitoring of sleep patterns and provides real-time data on sleep efficiency, latency, wake after sleep onset, and total sleep time.