Detection of daily living activities using a two-stage Markov model

A supervised statistical model for detecting the activities of daily living ADL from sensor data streams is proposed in this paper. This method works in two stages aiming at capturing temporal intra-and inter-activity relationships. In the first stage each activity is modeled separately by a Markov model where sensors correspond to states. By modeling each sensor as a state we capture the absolute and relational temporal features within the activities. A novel data segmentation approach is proposed for accurate inferencing at the first stage. To boost the accuracy, a second stage consisting of a Hidden Markov Model is added that serves two purposes. Firstly, it acts as a corrective stage, as it learns the probability of each activity being incorrectly inferred by the first stage, so that they can be corrected at the second stage. Secondly, it introduces inter-activity transition information to capture possible time-dependent relationships between two contiguous activities. We applied our method to three smart house datasets. Comparison of the results to other traditional approaches for ADL identification shows competitive or better performance. The paper also proposes a deployment of our methodology using an agent-based architecture.

[1]  Diane J. Cook,et al.  How smart are our environments? An updated look at the state of the art , 2007, Pervasive Mob. Comput..

[2]  Evangelos E. Milios,et al.  A Two-Stage Corrective Markov Model for Activities of Daily Living Detection , 2012, ISAmI.

[3]  Matjaz Gams,et al.  Home-based health monitoring of the elderly through gait recognition , 2012, J. Ambient Intell. Smart Environ..

[4]  G. Englebienne,et al.  Transferring Knowledge of Activity Recognition across Sensor Networks , 2010, Pervasive.

[5]  Monica Tentori,et al.  Hidden Markov Models for Activity Recognition in Ambient Intelligence Environments , 2007 .

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

[7]  Henry A. Kautz,et al.  Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.

[8]  Diane J. Cook,et al.  MavHome: an agent-based smart home , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[9]  Masamichi Shimosaka,et al.  Hierarchical recognition of daily human actions based on Continuous Hidden Markov Models , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[10]  Diane J. Cook,et al.  Multi-agent smart environments , 2009, J. Ambient Intell. Smart Environ..

[11]  Julie Doyle,et al.  Visualisation of movement of older adults within their homes based on PIR sensor data , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[12]  Hui Li,et al.  ZigBee wireless sensor network based Multi-Agent architecture in intelligent inhabited environments , 2008 .

[13]  L. Shrestha,et al.  The Changing Demographic Profile of the United States , 2009 .

[14]  Guang-Zhong Yang,et al.  The use of pervasive sensing for behaviour profiling - a survey , 2009, Pervasive Mob. Comput..

[15]  Martin Becker,et al.  Rule-based activity recognition framework: Challenges, technique and learning , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[16]  Shengrui Wang,et al.  Activity Recognition in Smart Environments: An Information Retrieval Problem , 2011, ICOST.

[17]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[18]  Gwenn Englebienne,et al.  UvA-DARE ( Digital Academic Repository ) Activity recognition using semi-Markov models on real world smart home datasets , 2010 .

[19]  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.

[20]  Sungyoung Lee,et al.  A Reconfigurable HMM for Activity Recognition , 2008, 2008 10th International Conference on Advanced Communication Technology.

[21]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[22]  Guo-Tan Liao,et al.  HMM machine learning and inference for Activities of Daily Living recognition , 2010, The Journal of Supercomputing.

[23]  Guimei Liu,et al.  Controlling False Positives in Association Rule Mining , 2011, Proc. VLDB Endow..

[24]  Sally I. McClean,et al.  Probabilistic Learning From Incomplete Data for Recognition of Activities of Daily Living in Smart Homes , 2012, IEEE Transactions on Information Technology in Biomedicine.

[25]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[26]  Yves Demazeau,et al.  User modeling for activity recognition and support in Ambient Assisted Living , 2011, 6th Iberian Conference on Information Systems and Technologies (CISTI 2011).

[27]  C. Dunnett.  A Multiple Comparison Procedure for Comparing Several Treatments with a Control , 1955 .

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

[29]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..

[30]  Sydney Katz.  Assessing Self‐maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living , 1983, Journal of the American Geriatrics Society.

[31]  Julie Brown,et al.  The "Acceptance" of Ambient Assisted Living: Developing an Alternate Methodology to This Limited Research Lens , 2011, ICOST.

[32]  Suh-Yin Lee,et al.  Human action recognition based on layered-HMM , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[33]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.