# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 1 Introduction  
With the population of the world increasing, the number of older people is growing inevitably. For personal comfort and due to limited medical resources, most of them live alone within their own house instead of nursing houses [1]. However, in their private space, emergency situations may not be noticed by others in time. For example, lying on the floor for a long time after a fall is one of the most dangerous situations. This will cause complications, and even death for the elderly [2]. Hence, how to assist them to live conveniently and safely has become an important social issue. To achieve the automatic recognition of human daily activities for healthy aging, the methods proposed by scientists can be roughly divided into three categories [3]. The first category is based on vision sensors. Vision-based systems can monitor the entire scenario and capture the detailed movement of the human target [4, 5]. However, because of the data association problem, it is challenging to handle the huge volume of vision data effectively [6]. Environmental factors such as occlusions and poor illumination conditions will deteriorate this problem. Besides, many people are uncomfortable living with cameras, which make them feel infringement on their privacy [7, 8]. The second category is based on wearable sensors. Compared to vision sensors, the acceptance of wearable sensors is more preferable, and the volume of data to process is much less; there is also no data association problem [9, 10]. However, the attachment of wearable sensors on the human body--even only one sensor--will feel obtrusive and uncomfortable to the resident [11]. What is more, people usually change their clothes daily and forget to attach the wearable sensors again or sometimes are not sufficiently clothedto wear sensors when the indoor temperature is high [12]. Even after the careful design of the power management unit, the batteries inside the wearable sensors need to be recharged or changed regularly, which feels inconvenient for the users [13]. The third category is dense sensing-based [3]. Dense sensing refers to the deployment of numerous low-cost low-power sensors in the ambient intelligent environment. These sensors include microphones, vibration sensors, switch sensors, pressure mat sensors, etc. The interaction between the human and the object with sensors attached often provides powerful clues about the activity being undertaken. However, compared with wearable sensors, each of these dense sensors needs \"fine tuning\" after deployment, which means that they are hardly used ubiquitously [2]. To sum up, there is a great demand to \"fill in the blanks\" when these three categories of sensors are unsuitable for use in daily life. Pyroelectric infrared (PIR) sensors are an excellent candidate for pervasive sensing. They are well accepted, because they appear in numerous places as part of security systems, including homes, banks, libraries, etc. [14]. They are inexpensive and can be attached to any indoor environment, which makes them \"invisible\" to the occupants. They also do not need to be worn or carried, which avoids the problems of forgetting to carry sensors and recharge batteries. They are a kind of passive infrared sensor; their performances are not affected changes of illumination [15]. However, for a single PIR sensor, its output is a raw sine-like signal and can only be used to detect whether or not human motion occurs. We have to carefully design the sensing paradigm and classification algorithm to develop their full potential. In this paper, we propose an approach to extract and fuse the location information and motion information from the PIR sensor data stream simultaneously. To monitor indoor environments, we built a wireless sensor network (WSN). In the WSN, sensor nodes consist of pyroelectric infrared (PIR) sensors.