# An Intelligent Healthcare System for Residential Aged Care during the COVID-19 Pandemic
## 2 Literature Review  
Some studies [4; 7] state that medical records can help predict problems with falls, and using additional data is critical to detecting dynamic status changes within a user's daily activities. In this section, some existing methods can be summarized as three categories: personal information management (PIM), collaborative filtering (CF), and wearable devices (WD). PIM in the ShEP area means the current physical conditions of the elderly. It includes chronic diseases, nursing interventions, counseling experience, and fall accidents [8; 9]. In order to find out what context factors of a healthcare system could be relevant to fall detection, it is necessary to conduct a literature review in PIM. CF in the ShEP area represents the sharing of real-time information between an elderly person, family member, and caregivers and can be scaled up for a healthcare system or used through social media application systems (e.g., Facebook) to let caregivers know whether or not the elderly person is at risk [10]. WD is a good method for identifying patterns in the elderly individual's behavior and using big data analysis to help with fall detection [11; 12]. PIM is an important research area in information systems. PIM can be applied to a healthcare system to allow for personalized features [8]. For example, patients can have an inspection scheduled in their appointment based on the current state of their illness. PIM is an important clue as to the current physical condition of an elderly person. Among its varied advantages, PIM can provide relevant factors for falls and allow sub-healthy patients and their caregivers to interactively and constructively participate in their own care. PIM also provides information about altered behaviors that can impact fall detection, including smoking, exercise, diet, and alcohol consumption [9]. Despite these advantages, however, PIM has some limitations. For example, a user may need to guess whether or not a current activity corresponds to their PIM, but in a real situation there is the possibility that risk activities may suddenly occur beyond their PIM. Therefore, only using PIM is insufficient to protect against all variations and exceptions to ShEP activities. In addition, PIM does not record every symptom experienced by a user (e.g., a mild sprain), and many caregivers do not know the details of patients' chronic diseases due to privacy issues. This possibility of misrepresentation limits its effectiveness in fall detection and makes it insufficient as a sole source of information for healthcare systems. CF is widely used in e-commerce applications, mobile applications, and recommender systems [13; 14; 15; 16; 17; 18], particularly in healthcare through a \"Neighborhood-based\" approach [13; 14; 16], which provides a possible option for predicting the behavioral patterns of the elderly based on their preferences [17]. In addition, CF can incorporate an elderly person's nearest neighbors or relevant members into clustering algorithms [14] for predicting the probability of a fall. Therefore, the clinical applications of fall detection can appropriately include CF (e.g., through social media or a decision support system) in the hopes of identifying potential risks to the elderly. Existing methods using CF techniques share the context information of an elderly person with others to avoid possible risks. For example, the elderly can share their location to their friends or family members when they are exercising in a park [19]. Therefore, that information can be useful to caregivers when deciding whether or not there is a risk. If such a system were applied accurately and effectively, a healthcare system based on CF techniques could be more scalable than a system based on PIM, because CF can respond to real-time situations dynamically. However, CF requires the elderly person to send their current context information to the system constantly and caregivers need to check the system frequently, which is tedious. In addition, the context information may be misleading.