A Novel Fuzzy Based Home Occupant Monitoring System Using Kinect Cameras

In this paper, an approach is presented for the detection of abnormal behaviours in the activities of people living alone in their homes. The proposed approach takes input from Kinect cameras and extracts a number of visual attributes that represent the occupant's location and orientation. A set of fuzzy logic parameters is first learnt from the training data. Next the proposed approach learns epochs of activities in each location and then generates models of normal behaviour patterns. Unusual behaviour is then detected in subsequent data by looking for patterns which differ from the learnt normal behaviours based on their time of occurrence, visual attributes, or duration. Experiments conducted showed the effectiveness of the proposed system.

[1]  Shane A Lowe,et al.  Monitoring human health behaviour in one's living environment: a technological review. , 2014, Medical engineering & physics.

[2]  Riza C. Berkan,et al.  Fuzzy System Design Principles , 1997 .

[3]  Xi Chen,et al.  Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Trevor P. Martin,et al.  Fuzzy Ambient Intelligence for Next Generation Telecare , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[5]  Bogdan Kwolek,et al.  Improving fall detection by the use of depth sensor and accelerometer , 2015, Neurocomputing.

[6]  Hirokazu Seki.  Fuzzy inference based non-daily behavior pattern detection for elderly people monitoring system , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Chiou Peng Lam,et al.  Activities of daily living classification using depth features , 2013, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013).

[8]  Dragan Kukolj,et al.  Design of adaptive Takagi-Sugeno-Kang fuzzy models , 2002, Appl. Soft Comput..

[9]  Yingli Tian,et al.  Privacy Preserving Automatic Fall Detection for Elderly Using RGBD Cameras , 2012, ICCHP.

[10]  Pierre Barralon,et al.  Fusion of Multiple Sensors Sources in a Smart Home to Detect Scenarios of Activities in Ambient Assisted Living , 2012, Int. J. E Health Medical Commun..

[11]  Jian Lu,et al.  epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[12]  W. Pedrycz.  Why triangular membership functions , 1994 .

[13]  Maarit Kangas,et al.  Comparison of low-complexity fall detection algorithms for body attached accelerometers. , 2008, Gait & posture.

[14]  Nathalie Labonnote,et al.  Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study , 2017 .

[15]  Max Mignotte,et al.  Fall Detection from Depth Map Video Sequences , 2011, ICOST.

[16]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[17]  Boreom Lee,et al.  Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description , 2011, IEEE Transactions on Information Technology in Biomedicine.

[18]  Wolfram Luther,et al.  A Novel Human Autonomy Assessment System , 2012, Sensors.

[19]  Man Hon Wong,et al.  Mining fuzzy association rules in databases , 1998, SGMD.

[20]  Ignas G. Niemegeers,et al.  On the Stability of Ad Hoc Group Mobility Models , 2009, 2009 IEEE International Conference on Communications.

[21]  Andrea Fossati,et al.  Consumer Depth Cameras for Computer Vision , 2013, Advances in Computer Vision and Pattern Recognition.

[22]  Bingbing Ni,et al.  RGBD-HuDaAct: A color-depth video database for human daily activity recognition , 2011, ICCV Workshops.