# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
###### Abstract
Finally, from a privacy point of view, patients claim that it is not always easy to discuss private health concerns in person, and there should be better options, which could be carried out without interfering with their daily life [4]. In particular, during onset of an epidemic, such as the COVID-19 global pandemic, home or hospital visits became unavailable, or strictly restricted/limited in many regions. Such challenges require a new healthcare model to be widely applied. Technologies that offer low-cost, scalable solutions through objective and continued remote monitoring, without interfering with user privacy and daily life, become crucial. Ambient Assisted Living (AAL) systems [2] provide remote monitoring at the home environment through a connected network of ambient sensors. Different sensor systems were tested, such as the multimedia-based monitoring systems [5; 6], which rely on video signals captured through surveillance cameras or ambient sounds recorded using microphones installed at the monitoring environment, thus privacy issue is of great concern for such systems to be widely accepted. Depth camera [7], millimeter-wave sensor [8] and LiDAR signal [9]-based systems offer alternative monitoring options with improved privacy experience. However, such systems often come with a non-neglectable cost. Additionally, high-end commercial devices such as ballistocardiographic-based bed sensors [10] provide detailed sleep analysis through passive monitoring, though the cost-factor could again be a concern for many elderly users. On the other hand, wearable devices in combination with ambient sensor systems enable personalized monitoring, although such systems show reduced adherence for elderly patients [11], due to the high-maintenance requirements by most wearable devices (e.g., battery charging, and data synchronization with a smart device). Research efforts developed integrated platforms [12] to minimize the required interaction efforts to improve user experience and adherence. Binary passive environmental sensors are not just cost effective, they are also efficient with respect to data transfer, storage, and computational needs. Furthermore, once installed they do not require any user interaction, and are the least intrusive from a privacy standpoint. Binary passive infra-red sensor, as well as contact, and pressure sensors, are often deployed in a connected network. Daily activity and health-status information are extracted from raw sensor signals, based on which personalized healthcare services can be provided. Due to the ambiguity of binary sensor signals, such systems are often restricted to be used in single-resident households, to avoid the problem of having to distinguish between different residents [2]. Additional personalized devices or high-level sensors (e.g., camera and microphone) could be integrated, at the expense of user privacy and usage complexity, to reduce the ambiguity of such systems, to make them applicable in multiple-resident indoor environments. On the other hand, researchers have also exploredthe feasibility of identifying different individuals by deploying large amounts of binary sensors [13], at the cost of a higher system complexity. In this paper, we consider five types of binary sensors [14], i.e., passive infra-red motion sensors (PIR), contact-based door sensors, pressure-based bed and chair sensors, as well as proximity-based toilet sensors. Up to five sensors (one per type) are deployed at multiple-resident households of elderly people to test the functionality of the system, in terms of whether this simple sensor network could be deployed as a generic AAL system setting, regardless of the number of residents and the structure of the households. Once deployed, our system adaptively learns user behavior patterns in an unsupervised manner, which does not rely on pre-defined knowledge, and does not require any data labeling. Based on internationally validated geriatric references, aging is often associated with physical, cognitive, psychological, and social health-status changes.