Dempster–Shafer theory-based human activity recognition in smart home environments

• Published in: Annales des télécommunications Vol. 69; no. 3-4; pp. 171 - 184
• Main Authors: Sebbak, Faouzi, Benhammadi, Farid, Chibani, Abdelghani, Amirat, Yacine, Mokhtari, Aicha
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01.04.2014; Springer
• Subjects: Applied sciences; Circuits; Communications Engineering; Computer Communication Networks; Detection, estimation, filtering, equalization, prediction; Engineering; Exact sciences and technology; Information and Communication; Information Systems and Communication Service; Information theory; Information, signal and communications theory; Networks; Pattern recognition; R & D/Technology Policy; Services and terminals of telecommunications; Signal and communications theory; Signal processing; Signal, noise; Signal,Image and Speech Processing; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control; Activity recognition; Dempster–Shafer theory; Context reasoning; Evidential mapping; Smart home; Performance evaluation; Automatic classification; Home automation; Information integration; Conflict resolution; Wireless telecommunication; Belief function; Sensor fusion; Mapping; Activity levels; Robotics; Remote sensing; Accuracy; Dempster Shafer theory; Signal detection; Decision support system; Measurement sensor; Gesture recognition; Dempster-Shafer theory; Coverage; Signal classification; Data fusion; Wireless network; Sensor array
• ISSN: 0003-4347; 1958-9395
• DOI: 10.1007/s12243-013-0407-2

Context awareness and activity recognition are becoming a hot research topic in ambient intelligence (AmI) and ubiquitous robotics, due to the latest advances in wireless sensor network research which provides a richer set of context data and allows a wide coverage of AmI environments. However, using raw sensor data for activity recognition is subject to different constraints and makes activity recognition inaccurate and uncertain. The Dempster–Shafer evidence theory, known as belief functions, gives a convenient mathematical framework to handle uncertainty issues in sensor information fusion and facilitates decision making for the activity recognition process. Dempster–Shafer theory is more and more applied to represent and manipulate contextual information under uncertainty in a wide range of activity-aware systems. However, using this theory needs to solve the mapping issue of sensor data into high-level activity knowledge. The present paper contributes new ways to apply the Dempster–Shafer theory using binary discrete sensor information for activity recognition under uncertainty. We propose an efficient mapping technique that allows converting and aggregating the raw data captured, using a wireless senor network, into high-level activity knowledge. In addition, we propose a conflict resolution technique to optimize decision making in the presence of conflicting activities. For the validation of our approach, we have used a real dataset captured using sensors deployed in a smart home. Our results demonstrate that the improvement of activity recognition provided by our approaches is up to of 79 %. These results demonstrate also that the accuracy of activity recognition using the Dempster–Shafer theory with the proposed mappings outperforms both naïve Bayes classifier and J48 decision tree.