Abstract
in this paper we present the improvement of our novel localization system, by introducing radio-frequency identification (RFID) which adds person identification capabilities and increases multi-person localization robustness. Our system aims at achieving multi-modal context-awareness in an assistive, ambient intelligence environment. The unintrusive devices used are RFID and 3-D audio-visual information from 2 Kinect sensors deployed at various locations of a simulated apartment to continuously track and identify its occupants, thus enabling activity monitoring. More specifically, we use skeletal tracking conducted on the depth images and sound source localization conducted on the audio signals captured by the Kinect sensors to accurately localize and track multiple people. RFID information is used mainly for identification purposes but also for rough location estimation, enabling mapping of the location information from the Kinect sensors to the identification events of the RFID. Our system was evaluated in a real world scenario and attained promising results exhibiting high accuracy, therefore showing the great prospect of using the RFID and Kinect sensors jointly to solve the simultaneous identification and localization problem.
Highlights
An assistive ambient intelligence environment is a smart space that aids the inhabitants with its embedded technology
In this paper we presented the introduction of radio-frequency identification (RFID) technology to our existing novel person localization system improving its location estimation robustness and adding identification capabilities
Accurate position estimation for each person was carried out using the depth sensor and microphone arrays of the Kinect devices as inputs, by means of skeletal tracking and sound source localization respectively
Summary
An assistive ambient intelligence environment is a smart space that aids the inhabitants with its embedded technology. The proliferation of ambient intelligent environments has triggered research related to applications, such as monitoring Assistive Daily Living (ADL), fall detection, risk prevention and surveillance [1, 2]. For achieving these goals, activity recognition performed in a natural and unintrusive way is of utmost importance. The most fundamental step towards activity monitoring and context-awareness is successful multi-person identification and localization. By utilizing the location of the person in a domestic setting, related activities can be derived. Our novel system uses information from multiple sensors in order to ensure reliable and unintrusive localization of the inhabitants
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