A phonological expression for physical movement monitoring in body sensor networks

Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for physical movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.

[1]  Thomas B. Moeslund,et al.  Finding Motion Primitives in Human Body Gestures , 2005, Gesture Workshop.

[2]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

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

[4]  A. Savvides,et al.  A sensory grammar for inferring behaviors in sensor networks , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[5]  Detlef Sieling.  Minimization of decision trees is hard to approximate , 2008, J. Comput. Syst. Sci..

[6]  Takao Kobayashi,et al.  Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM , 2005, IEICE Trans. Inf. Syst..

[7]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[8]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

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

[10]  Sid Lamrous,et al.  Divisive Hierarchical K-Means , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

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

[12]  Paul J. M. Havinga,et al.  Movement-Based Group Awareness with Wireless Sensor Networks , 2007, Pervasive.

[13]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[14]  Antti Vehkaoja,et al.  A resource optimized physical movement monitoring scheme for environmental and on-body sensor networks , 2007, HealthNet '07.

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

[16]  Y. Aloimonos,et al.  Discovering a Language for Human Activity 1 , 2005 .

[17]  Michael J. Brusco,et al.  Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques , 2007, J. Classif..

[18]  Roozbeh Jafari,et al.  Locomotion Monitoring Using Body Sensor Networks , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[19]  Maja J. Mataric,et al.  Deriving action and behavior primitives from human motion data , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Feng Liu,et al.  3D motion retrieval with motion index tree , 2003, Comput. Vis. Image Underst..

[21]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Jake K. Aggarwal,et al.  Human motion: modeling and recognition of actions and interactions , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[23]  Eugenio Culurciello,et al.  A Lightweight Camera Sensor Network Operating on Symbolic Information , 2006 .

[24]  László Lovász,et al.  Approximating Min Sum Set Cover , 2004, Algorithmica.

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

[26]  S. Shankar Sastry,et al.  Physical Activity Monitoring for Assisted Living at Home , 2007, BSN.

[27]  Stephen B. Wicker,et al.  CareNet: an integrated wireless sensor networking environment for remote healthcare , 2008, BODYNETS.

[28]  Yiannis Aloimonos,et al.  View-Invariant Modeling and Recognition of Human Actions Using Grammars , 2006, WDV.

[29]  Yiannis Aloimonos,et al.  A Language for Human Action , 2007, Computer.

[30]  Meinard Müller,et al.  Efficient content-based retrieval of motion capture data , 2005, SIGGRAPH '05.

[31]  Hongli Zhu,et al.  Motion Retrieval Based on Temporal-Spatial Features by Decision Tree , 2007, HCI.

[32]  C. S. Wetherell,et al.  Probabilistic Languages: A Review and Some Open Questions , 1980, CSUR.