臺灣博碩士論文加值系統

本論文的目標是設計出一個加速度計型緊急系統。系統設計的要求有二：一是準確地判斷活動狀態，一是實現高能源效率。

我們使用多層次的感測資料融合機制(multi-level sensor data fusion)來準確地判斷活動狀態。為了實現高能源效率，我們利用融合機制的結果減少無線傳輸的次數，來降低無線電傳輸的耗電量。採用慣用手傾聽的機制(habitual hand listening)使我們可以進一步地降低由於閒置傾聽(idle listening)所造成的無線電能耗。

我們的實驗結果顯示，當人們有意識時，用以判斷活動狀態的不活動期間(inactivity period)所需的時間長度為30秒至4分鐘；當人們熟睡時,不活動期間則需要30分鐘到40分鐘。關於能源效率的模擬結果顯示，與單純地每一週期皆傳送加速度計資料相比，利用融合機制 的結果減少無線傳輸的次數可以使系統壽命延長39倍。採用慣用手傾聽的機制，系統壽命可以進一步再延長兩倍。

從實驗結果來看，我們發現使誤判率(false positive rate)最低所需的不活動期間除了因人而異，亦視所從事的活動而定；也就是沒有一個放諸四海皆準的不活動期間。本論文最大的貢獻在於，藉由顯著地延長系統壽命，我們建立了一個高能源效率緊急系統的典範。

In this work, we aim to design an accelerometer-based emergency alarm system able to judge activity state accurately in the meantime achieve energy efficiency.

We use the multi-level sensor data fusion scheme to judge activity state accurately. To achieve energy efficiency, we reduce radio power consumption by cutting down the number of times of transmission utilizing the fusion results. Moreover, we reduce radio power consumption due to idle listening by adopting habitual hand listening.

Our results of human experiments show that for people being conscious, the inactivity period varies from 30 seconds to 4 minutes; for people being asleep, the inactivity period varies from 30 minutes to 40 minutes. The simulation results show that the lifetime of the system is prolonged 39 times utilizing the fusion results compared to naively sending accelerometer data periodically. Utilizing habitual hand listening, the lifetime of the system is prolonged twice compared to the scheme merely utilizing the fusion results.

From the experimental results, we find that the inactivity period to minimize the false positive rate varies from person to person also from activity to activity, i.e., no concrete selection of inactivity period can be drawn. The most contribution of the work is that we set an energy efficient example of emergency alarm system by significantly prolonging the lifetime of system.

1 Introduction 1
2 Related Work 4
2.1 Emergency Alarm Systems 4
2.2 Sensor Data Fusion 5
3 Multi-Level Sensor Data Fusion 6
4 Inter-Device Communication 9
4.1 Transmitting According to the First Two Levels of Fusion Results 9
4.2 Habitual Hand Listening 10
5 Experimental Results 12
5.1 Experimental Design 12
5.2 Threshold Determination 14
5.3 False Positive Rate of Human Subjects 14
5.4 False Negative Rate 19
5.5 Energy Efficiency 19
6 Conclusion and Outlook 23
Bibliography 24

[1] R. A. Powers, "Batteries for low power electronics," Proceedings of the IEEE, vol. 83,pp. 687~693, Apr. 1995.
[2] N. M. Barnes, N. H. Edwards, D. A. D. Rose, and P. Garner, "Lifestyle monitoring: Technology for supported independence," IEE Computing and Control Engineering,
vol. 9, pp. 169~174, Aug. 1998.
[3] S. Bonner, "Assisted interactive dwelling house: Edinvar housing association smart technology demonstrator and evaluation site," in Improving the Quality of Life for
the European Citizen (TIDE), Helsinki, Finland, June 1998, pp. 396~400.
[4] P. Cuddihy, J. Weisenberg, C. Graichen, and M. Ganesh, "Algorithm to automatically detect abnormally long periods of inactivity in a home," in Proc. 1st ACM SIGMOBILE International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments (HealthNet''07), San Juan, Puerto Rico, June 2007, pp. 89~94.
[5] K. Doughty, "Fall prevention and management strategies based on intelligent detection, monitoring and assessment," in New Technologies in Medicine for the Elderly, Charing Cross Hospital, London, Nov. 2000.
[6] The button, American Senior Safety Agency, 2002.
[7] A. R. Golding and N. Lesh, "Indoor navigation using a diverse set of cheap, wearable sensors," in Proc. Third International Symposium on Wearable Computers(ISWC''99), San Francisco, CA, Oct. 1999, pp. 29~36.
[8] M. Chan, H. Bocquet, E. Campo, and J. Pous, "Remote monitoring system to measure indoors mobility and transfer of the elderly," in Improving the Quality of Life for the European Citizen (TIDE), Helsinki, Finland, June 1998, pp. 379~383.
[9] S.-W. Lee and K. Mase, "Activity and location recognition using wearable sensors," IEEE Pervasive Computing, vol. 1, pp. 24~32, July 2002.
[10] N. Kern, B. Schiele, H. Junker, P. Lukowicz, and G. Troster, "Wearable sensing to annotate meeting recordings," in Proc. Sixth International Symposium on Wearable
Computers (ISWC''02), Sardinia, Italy, Oct. 2002, pp. 186~193.
[11] E. M. Tapia, "Activity recognition in the home setting using simple and ubiquitous sensors," Master''s thesis, Massachusetts Institute of Technology, Cambridge, 2003.
[12] Y. Song, X. Feng, and P. Perona, "Towards detection of human motion," in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR''00), Hilton Head Island, SC, June 2000, pp. 810~817.
[13] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, "Pfinder: Real-time tracking of the human body," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, pp. 780~785, July 1997.
[14] K. V. Laerhoven and H.-W. Gellersen, "Spine versus porcupine: a study in distributed wearable activity recognition," in Proc. Eighth International Symposium
on Wearable Computers (ISWC''04), Arlington, VA, Oct./Nov. 2004, pp. 142~149.
[15] N. Kern, B. Schiele, and A. Schmidt, "Multi-sensor activity context detection for wearable computing," in Proc. European Symposium on Ambient Intelligence (EU-
SAI''03), Eindhoven, The Netherlands, Nov. 2003, pp. 220~232.
[16] J. A. Ward, P. Lukowicz, G. Troester, and T. E. Starner, "Activity recognition of assembly tasks using body-worn microphones and accelerometers," IEEE Trans.
Pattern Analysis and Machine Intelligence, vol. 28, pp. 1553~1567, Oct. 2006.
[17] D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, "Implementation of a real-time human movement classifier using a triaxial accelerometer
for ambulatory monitoring," IEEE Trans. Information Technology in Biomedicine, vol. 10, pp. 156~167, Jan. 2006.
[18] T. R. Burchfield and S. Venkatesan, "Accelerometer-based human abnormal movement detection in wireless sensor networks," in Proc. 1st ACM SIGMOBILE International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments (HealthNet''07), San Juan, Puerto Rico, June 2007, pp. 67~69.
[19] G. Williams, K. Doughty, K. Cameron, and D. A. Bradley, "A smart fall and activity monitor for telecare applications," in Proc. 20th IEEE Annual International
Conference on Engineering in Medicine and Biology Society, Hong Kong, China, Oct./Nov. 1998, pp. 1151~1154.
[20] H. Nait-Charif and S. J. McKenna, "Activity summarisation and fall detection in a
supportive home environment," in Proc. 17th International Conference on Pattern Recognition (ICPR''04), Cambridge, UK, Aug. 2004, pp. 323~326.
[21] M.-C. Teng, J.-P. Chen, T.-H. Lin, P. Huang, C.-L. Huang, C.-H. Chen, and H.-S. Chen, "Emergency alarm system: Prototype and experience," in Proc. 7th Inter-national Workshop on Enterprise Networking and Computing in Healthcare Industry(HealthCom''05), Busan, Korea, June 2005, pp. 73~76.
[22] H. Mitchell, Multi-Sensor Data Fusion: An Introduction. Springer, Aug. 2007.
[23] S. R. Jeffery, G. Alonso, M. J. Franklin, W. Hong, and J. Widom, "Declarative support for sensor data cleaning," in Proceedings of the 4th International Conference
on Pervasive Computing, (PERVASIVE ''06), Dublin, Ireland, May 2006, pp. 83~100.
[24] T. He, P. Vicaire, T. Yan, Q. Cao, G. Zhou, L. Gu, L. Luo, R. Stoleru, J. A. Stankovic, and T. Abdelzaher, "Achieving long-term surveillance in vigilnet," in Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM''06), Barcelona, Spain, Apr. 2006.
[25] D. Smith and S. Singh, "Approaches to multisensor data fusion in target tracking: A survey," IEEE Trans. Knowledge and Data Engineering, vol. 18, pp. 1696~1710,
2006.
[26] A. Gad and M. Farooq, "Data fusion architecture for maritime surveillance," Proceedings of the Fifth International Conference on Information Fusion, vol. 1, pp.
448~455, 2002.
[27] G. C. Nandi and D. Mitra, "Development of a sensor fusion strategy for robotic application based on geometric optimization," Journal of Intelligent and Robotic
Systems, vol. 35, pp. 171~191, Nov. 2004.
[28] B. Bracio, W. Horn, and D. Moller, "Sensor fusion in biomedical systems," Proceedings of the 19th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society, vol. 3, pp. 1387~1390, Oct. 1997.
[29] Texas instruments incorporated, cc2420 data sheet. [Online]. Available:
http://www.chipcon.com/‾les/CC2420 Data Sheet 1 3.pdf
[30] J. Polastre, R. Szewczyk, and D. Culler, Telos: Enabling ultra-low power wireless research," in Proc. Fourth IEEE International Conference on Information Processing in Sensor Networks: Special Track on Platform Tools and Design Methods for Network Embedded Sensors (IPSN''05), Los Angeles, CA, Apr. 2005, pp. 364~369.
[31] Analog devices incorporated, adxl330 data sheet. [Online]. Available:
http://www.analog.com/UploadedFiles/Data Sheets/ADXL330.pdf
[32] Texas instruments incorporated, msp430x1xx family user''s guide (rev. f). [Online].
Available: http://focus.ti.com/lit/ug/slau049f/slau049f.pdf
[33] V. Shnayder, M. Hempstead, B. rong Chen, G. Werner-Allen, and M. Welsh, "Simulating the power consumption of large-scale sensor network applications," in Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys''04), Baltimore, MD, Nov. 2004.