Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

As the number of elderly people has increased worldwide, there has been a surge of research into assistive technologies to provide them with better care by recognizing their normal and abnormal activities. However, existing abnormal activity recognition (AAR) algorithms rarely consider sub-activity relations when recognizing abnormal activities. This paper presents an application of the Hidden State Conditional Random Field (HCRF) method to detect and assess abnormal activities that often occur in elderly persons’ homes. Based on HCRF, this paper designs two AAR algorithms, and validates them by comparing them with a feature vector distance based algorithm in two experiments. The results demonstrate that the proposed algorithms favorably outperform the competitor, especially when abnormal activities have same sensor type and sensor number as normal activities.

[1]  Vikramaditya R. Jakkula,et al.  Anomaly Detection Using Temporal Data Mining in a Smart Home Environment , 2008, Methods of Information in Medicine.

[2]  Rong Chen,et al.  Latent-Dynamic Conditional Random Fields for recognizing activities in smart homes , 2014, J. Ambient Intell. Smart Environ..

[3]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[4]  Diane J. Cook,et al.  Temporal pattern discovery for anomaly detection in a smart home , 2007 .

[5]  Diane J Cook,et al.  Assessing the Quality of Activities in a Smart Environment , 2009, Methods of Information in Medicine.

[6]  Ahmad Lotfi,et al.  Behavioural pattern identification and prediction in intelligent environments , 2013, Appl. Soft Comput..

[7]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[8]  Chris D. Nugent,et al.  A Knowledge-Driven Approach to Activity Recognition in Smart Homes , 2012, IEEE Transactions on Knowledge and Data Engineering.

[9]  Roman Klinger,et al.  Classical Probabilistic Models and Conditional Random Fields , 2007 .

[10]  Trevor Darrell,et al.  Conditional Random Fields for Object Recognition , 2004, NIPS.

[11]  Lawrence B. Holder,et al.  Conditional random fields for activity recognition in smart environments , 2010, IHI.

[12]  Manuel P. Cuéllar,et al.  Similarity measure for anomaly detection and comparing human behaviors , 2012, Int. J. Intell. Syst..

[13]  Xingshe Zhou,et al.  Detecting Abnormal Patterns of Daily Activities for the Elderly Living Alone , 2014, HIS.

[14]  Chien-Chen Chen,et al.  RFID-based human behavior modeling and anomaly detection for elderly care , 2010 .

[15]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[16]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[17]  Ahmad Lotfi,et al.  Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour , 2012, J. Ambient Intell. Humaniz. Comput..

[18]  Juan Carlos Augusto,et al.  Ambient Intelligence: Concepts and applications , 2007, Comput. Sci. Inf. Syst..

[19]  Qiang Yang,et al.  Sensor-Based Abnormal Human-Activity Detection , 2008, IEEE Transactions on Knowledge and Data Engineering.

[20]  Masashi Sugiyama,et al.  A least-squares approach to anomaly detection in static and sequential data , 2014, Pattern Recognit. Lett..

[21]  Yang Gao,et al.  Detecting Abnormal Events via Hierarchical Dirichlet Processes , 2009, PAKDD.

[22]  Shaogang Gong,et al.  Video behaviour profiling and abnormality detection without manual labelling , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[23]  Trevor Darrell,et al.  Hidden Conditional Random Fields for Gesture Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[24]  Makoto Takizawa,et al.  Abnormal behavior detection with fuzzy clustering for elderly care , 2010, 2010 International Computer Symposium (ICS2010).

[25]  Zongben Xu,et al.  The learning performance of support vector machine classification based on Markov sampling , 2013, Science China Information Sciences.

[26]  Diane J. Cook,et al.  Detecting Anomalous Sensor Events in Smart Home Data for Enhancing the Living Experience , 2011, Artificial Intelligence and Smarter Living.

[27]  Kostas Delibasis,et al.  Activity Recognition in Assistive Environments: The STHENOS Approach , 2014, HCI.

[28]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[29]  Manuela M. Veloso,et al.  Conditional random fields for activity recognition , 2007, AAMAS '07.

[30]  Yi-Ting Chiang,et al.  Strategies for Inference Mechanism of Conditional Random Fields for Multiple-Resident Activity Recognition in a Smart Home , 2010, IEA/AIE.

[31]  Trevor Darrell,et al.  Hidden Conditional Random Fields , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Wen-Zhan Song,et al.  Distributed Abnormal Activity Detection in Smart Environments , 2014, Int. J. Distributed Sens. Networks.

[33]  Ahmad Lotfi,et al.  User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques , 2016, Comput. Intell..