Human Presence Recognition in a Closed Space by using Cost-effective CO2 Sensor and the Information Gain Processing Method

Abstract

The recent rapid progress in ICT technologies such as smart/intelligent sensor devices, broadband/ubiquitous networks, and Internet of everything (IoT) has advanced the penetration of sensor networks and their applications. The requirements of human daily life, security, energy efficiency, safety, comfort, and ecological, can be achieved with the help of these networks and applications. Traditionally, if we want some information on, for example, environment status, a variety of dedicated sensors is needed. This will increase the number of sensors installed and thus system cost, sensor data traffic loads, and installation difficulty. Therefore, we need to find redundancies in the captured information or interpret the semantics captured by non-dedicated sensors to reduce sensor network overheads. This paper clarifies the feasibility of recognizing human presence in a space by processing information captured by other than dedicated sensors. It proposes a method and implements it as a cost-effective prototype sensor network for a university library. This method processes CO2 concentration, originally designed to check environment status. In the experiment, training data is captured with none, one, or two subjects. The information gain (IG) method is applied to the resulting data, to set thresholds and thus judge the number of people. Human presence (none, one or two people) is accurately recognized from the CO2 concentration data. The experiments clarify that a CO2 sensor in set in a small room to check environment status can recognize the number of humans in the room with more than 70 % accuracy. This eliminates the need for an extra sensor, which reduces sensor network cost.