# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 2 Related Work  
Ambient Assisted Living systems can be differentiated along four different dimensions [2], namely the sensors package, the data collected, the algorithms applied, and the objectives of the system. In this paper, we focus on the use of passive binary environment sensor networks, due to its low-cost, and minimal-intrusive nature. As a result, event-based behavioral data is acquired, which is analyzed by unsupervised algorithms, to eliminate the labeling overhead. The objective is to provide medically relevant notifications to caregivers, so they can intervene at the right time and mitigate medical worsening. Identifying different activity types is often the fundamental step in sensor-based assisted living systems. In [18], patient daily activity was extracted using presence sensors deployed in a hospital setting. In [19], 14 binary sensors were installed at a three-room apartment to recognize 7 pre-defined activity categories. In [20], activity recognition was achieved by converting annotated binary sensor data into binary activity images in combination with a four-layer (two convolution layers and two fully connected layers) neural network. Recurrent network models were also used to recognize and predict daily activities in densely deployed binary sensor networks [21; 22]. Most such systems were tested in single-resident environments, and required carefully labeled training data. In [23], densely distributed binary sensors were deployed at two multi-person households to recognize 27 different activity categories. Sixty to a hundred activity labels were collected as ground-truth per day to train the system. High-quality sleep is considered a cornerstone of a healthy lifestyle [24] and is recognized to be an active and dynamic process of physical and mental recovery. Traditionally, sleep quality is quantified using the Pittsburgh Sleep Quality Index (PSQI) [25], which is a self-report questionnaire consisting of 19 items of 7 aspects. In the medical field, Polysomnography [26] is conducted as a diagnostic tool to monitor different sleep stages based on neuronal and eye activities during sleeping. Heart rate variability-based sleep analysis is presented in [24; 27]. Meanwhile, wearable devices that incorporate accelerometers for sleep analysis have been used on a massive scale [28]. Nocturnal sound-based sleep staging analysis has also been explored in [29]. In [30], sleep patterns were inferred by using actigraphy data generated by binary motion sensors scattered around participants' apartment. In addition to the typical indoor activities, outings and visits are strong cues of social activity. Outings are often considered to be long inactive periods. In [31], multiple motion sensors and a door sensor were installed in a single room office. A Naive Bayes classifier and hidden Markov models were then applied to detect visits based on the raw binary sensor signals. Room transitions at each time stamp were used to detect visiting events in [32]. In [33], the dwell time, number of sensor firings, and number of transitions between major living spaces are extracted as features from 15-minutes windows, based on which a Support Vector Machine (SVM) classifier was applied. These systems rely on carefully labeled visiting and non-visiting events. However, in real life it is challenging and intrusive to label every visit in the life of elders at their private homes. In [34], the authors proposed an unsupervised method for visiting detection using room transitions. In [35], visits were identified when simultaneous activities were detected in bedroom and bathroom. These systems rely on a sensor connection topology, which is carefully crafted, based on the sensor installation floor-map. In [11], sensor signals between adjacent door activities were represented in a feature space and semi-supervised learning algorithms were trained with partially labeled visiting events.