Summary: As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried’s phenotype that is capable of detecting frailty. To determine user requirements, the system’s architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty.

Summary: Early detection of frailty is one of the main challenges in the world we live in. Being aware of physiologic and behavior changes can predict and prevent the onset of mental complications for older people. By using modern technologies, one can get insights, which may help detection of different pathologies. In this paper, preliminary results for a novel system to detect early signs of frailty are presented. A prototype was developed and tested in laboratory conditions after requirements and functional capabilities were defined. The main advantages of the proposed architecture are the usage of commercial off-the-shelf (COTS) components and custom mechanisms of security to assure a high level of confidentiality and integrity of user-specific data. Other original elements are its easy-to-use and non-intrusive characteristics.

Purpose: Smart apps and wearables devices are an increasingly used way in healthcare to monitor a range of functions associated with certain health conditions. Even if in the present there are some devices and applications developed, there is no sufficient evidence of the use of such wearables devices in the detection of some disorders such as depression. Thus, through this paper, we want to address this need and present a quantitative research to determine the user requirements for developing a smart device that can detect depression. Material and Methods: To determine the user requirements for developing a system to detect depression we developed a questionnaire which was applied to 205 participants. Results and conclusions: Such a system addressed to detect depression is of interest among the respondents. The most essential parameters to be monitored refer to sleep quality, level of stress, circadian rhythm, and heart rate. Also, the developed system should prioritize reliability, privacy, security, and ease of use.

Frailty is a clinical syndrome prevalent among the older population, characterized by unintentional weight loss, exhaustion being associated with a decline in cognitive and physiological functions. Ambient Assisted Living can be used for coping through solutions based on smart devices that help older people to cope with frailty. In this paper, we present a quantitative research to determine the user requirements for developing a system to assist people with frailty.