Distributed Continuous Action Recognition Using a Hidden Markov Model in Body Sensor Networks

One important application of Body Sensor Networks is action recognition. Action recognition often implicitly requires partitioning the sensor data into intervals, then labeling the partitions according to the actions each represents or as a non-action. The temporal partitioning stage is called segmentation and the labeling is called classification. While many effective methods exist for classification, segmentation remains problematic. We present a technique inspired by continuous speech recognition that combines segmentation and classification using Hidden Markov Models. This technique is distributed and only involves limited data sharing between sensor nodes. We show the results of this technique and the bandwidth savings over full data transmission.

[1]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[2]  Kamiar Aminian,et al.  Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.

[3]  Axel Pinz,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[4]  Virginia Teller.  Review of Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition by Daniel Jurafsky and James H. Martin. Prentice Hall 2000. , 2000 .

[5]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sethuraman Panchanathan,et al.  Analysis of low resolution accelerometer data for continuous human activity recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Blake Hannaford,et al.  A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.

[8]  Jeffrey M. Hausdorff,et al.  Gait variability and basal ganglia disorders: Stride‐to‐stride variations of gait cycle timing in parkinson's disease and Huntington's disease , 1998, Movement disorders : official journal of the Movement Disorder Society.

[9]  J. Y. Lee,et al.  An online signature verification system using hidden Markov model in polar space , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[10]  Juan Carlos Augusto,et al.  Editorial: Inaugural issue , 2009, J. Ambient Intell. Smart Environ..

[11]  Kristof Van Laerhoven,et al.  Spine versus porcupine: a study in distributed wearable activity recognition , 2004, Eighth International Symposium on Wearable Computers.

[12]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[13]  Hassan Ghasemzadeh,et al.  Action coverage formulation for power optimization in body sensor networks , 2008, 2008 Asia and South Pacific Design Automation Conference.

[14]  Ramakant Nevatia,et al.  Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost , 2006, ECCV.

[15]  Martin Vetterli,et al.  Proceedings of the 4th international symposium on Information processing in sensor networks , 2005 .

[16]  Ling Bao,et al.  Physical activity recognition from acceleration data under semi-naturalistic conditions , 2003 .

[17]  Alain Biem,et al.  A model selection criterion for classification: application to HMM topology optimization , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[18]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[19]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[20]  Allen Y. Yang,et al.  Distributed recognition of human actions using wearable motion sensor networks , 2009, J. Ambient Intell. Smart Environ..

[21]  Y. Selen,et al.  Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.

[22]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[23]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[24]  Franco Zambonelli,et al.  A Simple Model and Infrastructure for Context-Aware Browsing of the World , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07).

[25]  Paul Lukowicz,et al.  Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[27]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[28]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[29]  Stephen J. McKenna,et al.  Activity summarisation and fall detection in a supportive home environment , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[30]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[31]  Jin-Hyung Kim,et al.  An HMM-Based Threshold Model Approach for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..