# An Intelligent Healthcare System for Residential Aged Care during the COVID-19 Pandemic
## 2 Literature Review
For example, the elderly person may walk to a park for exercise but may only be sitting on a chair without any risk. As a result, CF is also insufficient for a healthcare system's needs. In sum, it is not suitable to use CF in times of the COVID-19 epidemic. Wearable devices monitor the physiological characteristics of users [11; 20; 21] (e.g., weight and blood pressure) to know whether or not they are in danger. Some of the factors it monitors could trigger chronic diseases, such as an unhealthy diet, a lack of physical exercise, and irregular sleeping [20]. These unhealthy living habits could also cause death or disability in the elderly. To address the challenges of monitoring chronic diseases in real time, many researchers have designed these wearable devices to monitor characteristics, such as blood pressure, and provide basic healthcare services with the internet of things (IoTs) cloud architecture [11; 12; 21]. Some researchers use the WD method to replace mobile sensors, such as accelerometers, in order to obtain fixed body placement for ShEP [22]. However, this method is usually not accepted widespread because these wearable devices could reveal the weaknesses of ShEP and they may also forget to wear such external devices. WDs still have many limitations. First, although a WD can measure the physical health state of the elderly dynamically, it is not sufficient for identifying fall risks in real time. Second, a WD cannot measure many human behaviors that may be relevant to fall risks, only physiological characteristics. For example, a WD cannot tell whether elderly people are sitting or running in a park or in a room. However, those activities could be risk factorsfor a fall. Third, many elderly people do not use a WD, so it could be less useful for ShEP who undertake many activities. Although existing studies reveal that PIM, CF, and WD could be relevant to fall detection, there are still many limitations. The strengths and weaknesses of each approach are summarized in Table 1. One of the common limitations across these systems is a lack of sufficient context information to improve user performance and the awareness of fall risks. In addition, the majority of studies on healthcare systems do not consider the serious problem of human variability. Even some researchers use mobile sensors, but they mainly focus on using the accelerometer as a sensor, which has insufficient context information to lead to the low performance of fall detection [23; 24]. This paper will try to address the existing problems of user performance and human variability by using context information and AI technologies. The proposed methods are described in the following sections. Most existing studies on the identification of risk context factors affecting the elderly are designed and optimized for personal information management (PIM) only. This system includes information, such as physical symptoms, impairment records, and demographics [7; 25]. However, PIM is a highly personal and burdensome task because the information is stored in multiple hierarchies [25]. Another existing method is called collaborative filtering (CF). Users can share their current context information (e.g., interest or location) with family members or caregivers to let them know whether they are safe or in danger. CF is usually used through social network domains, such as Facebook. CF has been proven to be beneficial in improving healthcare performance by alleviating the problems of context information loss, disconnection, and redundant contacts [15; 16; 18; 26], but it is not enough for detecting sudden risks immediately and effectively. In summary, most studies focus on developing a system of fall detection through static risk factors, and there are few empirical studies that systematically examine the impact of those factors on elderly care [27].