# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 1 Introduction  
The world population is aging rapidly. As time goes on, the proportion of the elderly relative to the total population has increased, and continues to increase, especially in developed countries [1]. Thus, helping seniors live a better life is crucial and has great societal benefits. Although the elders have the option of going to nursing homes or hospice care, most of them would prefer to stay in their own houses where they feel more familiar and comfortable. Limited funding for public healthcare services and the shortage of registered nurses are also driving factors for the adoption of a home-based assisted living paradigm. Therefore, healthy aging at home has become one of the most active research areas [1], especially the problem of abnormal activity detection [2; 3; 4; 5]. The elderly living alone in isolated areas have been in need of emergency attention, and in the worst cases, some were found dead in their homes [6]. Traditionally, abnormal activity detection approaches use cameras to obtain the data of full human body movements [7]. However, there are challenging issues in vision-based methods,such as computational complexity in image processing, data consistency under different illumination conditions, and privacy infringement of the human target [8]. These problems make the practical deployment of vision-based systems difficult. An alternative method is to collect sensing data from wearable motion sensors and detect abnormal activities based on the collected sensing data [2]. Although motion sensors worn on the human body or integrated into human clothing can collect motion data with much less volume of data compared to those from vision-based systems, such wearable devices may make the human subject feel obtrusive. In addition, the elders are prone to forget wearing the devices after they change clothes. In addition, having to recharge the wearable devices regularly, even after deliberate design of power management units, is inconvenient for the user [2]. In order to serve as a reliable and robust abnormal activity detection system for the elderly living alone, the following factors should be considered:  
1. robust to the change of environment, especially the light illumination;
2. protective to the residents' privacy;
3. convenient to use, especially for the elderly. Bearing those factors in mind, a Pyroelectric Infrared (PIR) sensing paradigm offers a promising alternative to the optical and wearable counterparts [3]. PIR sensors are non-intrusive sensors and only sensitive to the infrared radiation changes induced by human motion, which makes them robust to interference caused by clustered background and illumination variance. In addition, as PIR sensors are relatively cheap and can be embedded within home environments, such as ceiling-mounted deployment, they are suitable to be used for home-based assisted living. However, there are some challenges facing the PIR based sensing system for abnormal behavior detection. The first one is the design of sensor nodes, which are required to capture the spatio-temporal characteristic of the human motion. Furthermore, as the data generated continuously by ambient PIR sensors, there is an increasing demand to analyze those ever-growing sensing data automatically, with little human intervention. Most importantly, the problem of abnormal activity detection is computationally challenging [4]. Here, we define abnormal activities as events that they have not been expected in advance. Unlike normal activities, the abnormal samples are extremely scarce, or even non-existent. It is impossible to acquire or simulate all kinds of abnormal samples to train the system beforehand. In this paper, we propose a PIR-based sensing system for anomaly detection. We design a PIR sensor node that can capture the spatio-temporal feature of human motion effectively. The key to achieve this target is by leveraging the visibility modulation of each sensor to enhance spatial resolution.