An automatic segmentation technique in body sensor networks based on signal energy

ABSTRACT

Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The long-term lifestyle monitoring can greatly improve healthcare by gathering information about quality of life; aiding the diagnosis and tracking of certain diseases such as Parkinson's. The deployment of an automatic and computationally-efficient algorithm reduces the complexities involved in the detection and recognition of human activities in a distributed system. This paper presents a new algorithm for automatic segmentation of routine human activities. The proposed algorithm can distinguish between discrete periods of activity and rest without specifically knowing the activity. A finite subset of nodes can detect all human activities, but each node by itself can only detect a particular set of activities. For local segmentation we choose the parameters for each node that result in the least segmentation error. We demonstrate the effectiveness of our algorithm on data collected from body sensor networks for a scenario simulating a set of daily activities.

References

1. H. Ghasemzadeh, J. Barnes, E. Guenterberg, and R. Jafari, "A Phonological Expression for Physical Movement Monitoring in Body Sensor Networks," in The Fifth IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS) , Atlanta, GA, 2008.
2. M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Fox, H. Kautz, and D. Hahnel, "Inferring activities from interactions with objects," Pervasive Computing, IEEE , vol. 3, pp. 50--57, 2004.
3. A. Vehkaoja, M. Zakrzewski, J. Lekkala, S. Iyengar, R. Bajcsy, S. Glaser, S. Sastry, and R. Jafari, "A resource optimized physical movement monitoring scheme for environmental and on-body sensor networks," in Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments San Juan, Puerto Rico, pp. 64--66, 2007.
4. R. Jafari, F. Dabiri, P. Brisk, and M. Sarrafzadeh, "Adaptive and fault tolerant medical vest for life-critical medical monitoring," in Proceedings of the 2005 ACM symposium on Applied computing Santa Fe, New Mexico: ACM, 2005.
5. L. Rabiner, "A tutorial on Hidden Markov Models and selected applications in speech recognition," in Proceedings of the IEEE , pp. 77--86, 1989.
6. N. Kern, B. Schiele, and A. Schmidt, "Multi-sensor Activity Context Detection for Wearable Computing," Proc. 1 ^{st European Symp. Ambient Intelligence} (Eusai 03), LNCS 2875, Springer, pp. 220--232, 2003.
7. L. Bao and S. Intille, "Activity Recognition from User Annotated Acceleration Data," Proc. 2nd Int'l Conf. Pervasive Computing (Pervasive 04), LNCS 3001, Springer, pp. 1--17, 2004.
8. R. Aylward and J. Paradiso. A compact, high-speed, wearable sensor network for biomotion capture and interactive media. In IPSN, 2007.
9. Ward, J. A. Lukowicz, P. Troster, G. Starner, T. E., "Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers," in IEEE Transactions onPattern Analysis and Machine Intelligence , pp. 1553--1567, 2006.
10. R. Jafari, W. Li, R. Bajcsy, S. Glaser, S. Sastry, Physical Activity Monitoring for Assisted Living at Home, In Proceedings of International Workshop on Wearable and Implantable Body Sensor Networks (BSN) , March 2007, Aachen, Germany.
11. D. M. Sherrill, M. L. Moy, J. J. Reilly, P. Bonato, Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data, In Journal of NeuroEngineering and Rehabilitation , pp. 1--14, June 2005.
12. P Renevey, R Vetter, P Celka, J Krauss, Activity Classification Using HMM For Improvement of Wrist Located Pulse Detection, In Proceedings of the Biosignal , 2002.
13. G. S. Chambers, S. Venkatesh, G. A. W. West, H. H. Bui, Segmentation of Intentional Human Gestures for Sports Video Annotation, In Proceedings of the 10th IEEE International Multimedia Modeling Conference (MMMi04) , 2004.
14. G. S. Chambers, S. Venkatesh, G. A. W. West, Automatic Labeling of Sports Video Using Umpire Gesture Recognition, Lecture Notes in Computer Science, Structural, Syntactic, and Statistical Pattern Recognition , pp. 859--867, 2004.
15. A. Y. Benbasat, J. A. Paradiso. An inertial measurement framework for gesture recognition and applications. In Gesture and Sign Language in Human Computer Interaction , volume 2298 of LNCS, pp. 9--20. SpringerVerlag, 2002.
16. M. J. Mathie, B. G. Celler, N. H. Lovell, A. C. F. Coster, Classification of basic daily movements using a triaxial accelerometer, In Journal of Medical and Biological Engineering and Computing , pp. 679--687, Springer Berlin/Heidelberg, September, 2004.
17. C. Li, S. Q. Zheng, B. Prabhakaran, Segmentation and Recognition of Motion Streams by Similarity Search, ACM Tansactions on Multimedia Computing, Communications and Applications , Vol. 03, No. 03, pp. 1--26, 2007.

Index Terms

• Applied computing

  • Life and medical sciences

    • Health care information systems

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning algorithms

      • Feature selection

    • Machine learning approaches

      • Classification and regression trees

  • Modeling and simulation

    • Model development and analysis

      • Model verification and validation

      • Modeling methodologies

    • Simulation evaluation

• Hardware

  • Communication hardware, interfaces and storage

    • Signal processing systems