Markus Scholz
Michael Beigl
ABSTRACT
Background: We investigate direct use of 802.15.4 radio signal strength indication (RSSI) for human activity recognition when 1) a user carries a wireless node (device-bound) and when 2) a user moves in the wireless sensor net (WSN) without a WSN node (device-free). We investigate recognition feasibility in respect to network topology, subject and room geometry (door open, half, closed).
Methods: In a 2 person office room 8 wireless nodes are installed in a 3D topology. Two subjects are outfitted with a sensor node on the hip. Acceleration and RSSI are recorded while subject performs 6 different activities or room is empty. We apply machine learning for analysis and compare our results to acceleration data.
Results: 10-fold cross-validation with all nodes gives accuracies of 0.896 (device-bound), 0.894 (device-free) and 0.88 (accelerometer). Topology investigation reveals that similar accuracies may be reached with only 5 (device-bound) or 4 (device-free) selected nodes. Applying trained data from one subject to the other and vice-versa shows higher recognition difference on RSSI than on acceleration. Changing of door state has smaller effect on both systems than subject change; with least impact when door is closed.
Conclusion: 802.15.4 RSSI suited for activity recognition. 3D topology is helpful in respect to type of activities. Discrimination of subjects seems possible. Practical systems must adapt no only to long-term environmental dispersion but consider typical geometric changes. Adaptable, robust recognition models must be developed.
- L. Bao and S. Intille. Activity recognition from user-annotated acceleration data. In A. Ferscha and F. Mattern, editors, Pervasive Computing, volume 3001 of Lecture Notes in Computer Science, pages 1--17. Springer Berlin/Heidelberg, 2004.Google Scholar
- C. Bouten, K. Koekkoek, M. Verduin, R. Kodde, and J. Janssen. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering, 44(3):136--147, Mar. 1997.Google ScholarCross Ref
- G. Cohn, D. Morris, S. Patel, and D. Tan. Humantenna: using the body as an antenna for real-time whole-body interaction. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, CHI '12, pages 1901--1910, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- P. Misra, N. Ahmed, D. Ostry, and S. Jha. Characterization of asymmetry in low-power wireless links: an empirical study. In Proceedings of the 12th ICDCN'11, Berlin, Heidelberg, 2011. Springer-Verlag. Google ScholarDigital Library
- N. Patwari and J. Wilson. Spatial models for human motion-induced signal strength variance on static links. Information Forensics and Security, IEEE Transactions on, 6(3):791--802, sept. 2011. Google ScholarCross Ref
- M. Reschke, J. Starosta, S. Schwarzl, and S. Sigg. Situation awareness based on channel measurements. In Proceedings of the fourth Conference on Context Awareness for Proactive Systems (CAPS), 2011.Google ScholarCross Ref
- M. Scholz, S. Sigg, G. Bagschik, T. Guenther, G. von Zengen, D. Shishkova, Y. Ji, and M. Beigl. Sensewaves: Radiowaves for context recognition, video submission. In Pervasive, 2011.Google Scholar
- M. Scholz, S. Sigg, H. R. Schmidtke, and M. Beigl. Challenges for device-free radio-based activity recognition. In Workshop on Context Systems, Design, Evaluation and Optimisation, 2011.Google Scholar
- S. Shi, S. Sigg, and Y. Ji. Activity recognition from radio frequency data: Multi-stage recognition and features. In VTC2012 Fall, 2012.Google ScholarCross Ref
- S. Shi, S. Sigg, and Y. Ji. Passive detection of situations from ambient fm-radio signals. In SegaWare Workshop in Conjunction UbiComp '12., 2012. Google ScholarDigital Library
- T. Sohn, A. Varshavsky, A. Lamarca, M. Y. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. G. Griswold, and E. D. Lara. Mobility detection using everyday gsm traces. In Ubicomp 2006, pages 212--224. Springer, 2006. Google ScholarDigital Library
- J. Wilson and N. Patwari. A fade-level skew-laplace signal strength model for device-free localization with wireless networks. Mobile Computing, IEEE Transactions on, 11(6):947--958, june 2012. Google ScholarDigital Library
- K. Woyach, D. Puccinelli, and M. Haenggi. Sensorless sensing in wireless networks: implementation and measurements. In 2nd International Workshop on Wireless Network Measurement (WiNMee), 2006.Google ScholarCross Ref
- M. Youssef, M. Mah, and A. Agrawala. Challenges: device-free passive localization for wireless environments. In Proceedings of the 13th MobiCom, pages 222--229, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- D. Zhang and L. Ni. Dynamic clustering for tracking multiple transceiver-free objects. In PerCom, march 2009. Google ScholarDigital Library
Index Terms
- Device-free and device-bound activity recognition using radio signal strength
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