## 4 Discussion  
The final outcome of the prototype is visually and successfully computes and displays data related to the following parameters:  
* Physical activity;
* Energy expenditure;
* Unintentional weight loss;
* Exhaustion or fatigue;
* Daily sedentary time and steps history;
* Heart rate daily values variation;
* High body mass index. Otherwise, while the chosen parameters were those that were the most mentioned in the reviewed literature, other parameters like cognitive, psychological, and social aspects cannot be assessed in a non-intrusive manner and they might require specialized assistance. Integrating these additional parameters could enhance the accuracy of the system, and more research is necessary to develop accurate and easy-to-use methods of assessing these parameters, exploring new ways of capturing the metrics of these parameters by using additional sensor data. Taking into account that physical activity, energy expenditure, and daily sedentary times are based on models trained with data from one person, the system may have a better accuracy if more diverse data are used for training, from both non-frail and pre-frail individuals, young and elderly. The dashboard serves as a tool for specialized personnel to detect and assess insights related to frailty. Based on the information provided, the frailty syndrome can be attributed to a person linked with other specific clinical tests. Even though the initial purpose of the dashboard is frailty detection, it can also be used as a tool for monitoring a person's overall health. In the next phase, the entire system is planned for an extended clinical study  
Figure 16: Screen capture of the BMI index with classification categories. aimed at collecting data from elderly individuals in a coordinated, ethical, and scientifically approved manner. Integration with wearable health devices, such as smartwatches and fitness trackers, offers real-time data to enhance the system's accuracy. Furthermore, telehealth applications for the remote monitoring of older adults, especially those in remote or underserved areas, have significant potential. The development of personalized interventions, predictive analytics to forecast frailty risk, and customized user interfaces for older individuals are all options worth exploring. Additionally, ethical considerations, validation, and certification, user education, population-specific models, and cost-effectiveness studies are components of future work, ensuring the system's effectiveness, ethical compliance, and accessibility while advancing the well-being of older adults. Another limitation is that most IoT devices require an intermediary to connect to the Internet. In this case, a mobile phone is needed, which increases the cost of such a system. Addressing a limited number of detected activity types narrows the system's accuracy in confidently predicting the exact level of sedentariness and energy consumption. At the same time, considering that the system is designed to analyze data in a distributed context over extended periods, the accuracy is offset by the nature of the process. The subjective nature of some parameters can introduce data interpretation errors. Improving such a system is achievable by integrating smart scales, as well as algorithms that can determine gait based on the number of steps taken over a period. Another technology that can be used for a more accurate determination is indoor localization, which can also provide information about the exact distance covered and the precise step size of a person. The development of such systems, where relevant health data about vulnerable patients are used, must take into account the ethical dimension of data collection. The security and confidentiality of these data are essential to prevent unauthorized use for purposes other than those for which they are intended. In this regard, future work involves the development of proprietary hardware solutions that transmit data in a single direction, towards an isolated on-premise system.