# An Intelligent Healthcare System for Residential Aged Care during the COVID-19 Pandemic
## 3 The Proposed Method  
According to International Data Corporation (IDC) reports, the worldwide mobile device market could reach 1.76 billion units shipped in 2020 (see Worldwide Smartphone \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Description** & **Strengths** & **Weaknesses** \\ \hline PIM & PIM helps with identifying an elderly person’s current physical condition. It provides information on chronic diseases, nursing interventions, counselling experience, fall accidents, etc. [8; 9]. & PIM could provide relevant factors for fall detection because knowing an elderly person’s chronic disease treatment regimen could allow caregivers to interactively and constructively participate in the elderly person’s person’s care. & \\ CF & CF provides a possible option for predicting behavioral patterns of the elderly preferences [17] & CF can incorporate the elderly person’s nearest neighbors or relevant members into clustering algorithms [14] for predicting the probability of a fall. & \\ WD & WDs monitor the physiological characteristics of users [11; 20; 21] & WDs are a good method for understanding and analyzing the behavioral patterns of an elderly person who needs fall detection [11; 12]. & \\ WD & (e.g., weight and blood pressure to know whether they are in danger. & [11; 12]. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of existing methods. Forecast Update, [[https://www.idc.com/](https://www.idc.com/)]([https://www.idc.com/](https://www.idc.com/)), accessed on 1 November 2022). Many studies show that ShEP widely uses smartphones in their everyday lives [30; 31; 32]. Yet, their mobile technology needs with regard to healthcare application systems have received too little attention from academic researchers or practitioners so far. One of the purposes of this study is to find out the risk factors of fall detection among ShEP who still participate in numerous daily activities and regularly use mobile phones for their social communications. This study proposes a novel method using smartphones with mobile sensors to detect and monitor the dynamic context information of ShEP. The proposed method is suitable for people who did not need body contact during the COVID-19 epidemic. The first proposed method is an indoor and outdoor (IO) approach. Smartphones can be used in fall detection to alert caregivers immediately after an accident, which can help lower the possibility of further consequences, such as permanent brain damage or sudden death. However, existing studies only focus on technologies, such as three-axis accelerometers or gyroscopes [5; 33], which only detect some phenomena. Most ShEP participate in activities in different locations with varying fall risks [4]. For example, forest trails and bedrooms present different risk probabilities for falls; thus, it is difficult to accurately assess risk factors only using PIM or CF. For these reasons, the first proposed method is an indoor and outdoor (IO) approach to fall detection. Light intensity could be one of environment contexts for detecting whether a ShEP is indoors or not. Mobile light sensors can help dynamically capture light intensity. For example, if the value of illumination is greater than 5000 lux, which indicates in a sunny environment, it could suggest a lower probability of falling. Therefore, to decrease the probability of falls, illumination should be one of the more significant factors, such as changing our visualization to a bright level. Mobile light sensors could detect indoor or outdoor environments because we can use the illumination value as an emergency number in a dark room. If the value breaks through a certain threshold, we can conclude that a fall is occurring. The IO algorithm is presented below (Algorithm 1).