Abstract
The increased prevalence of chronic disease in elderly people is placing requirements for new approaches to support efficient health status monitoring and reporting. Advances in sensor technologies have provided an opportunity to perform continuous point-of-care physiological and activity-related measurement and data capture. Context-aware physiological pattern analysis with regard to activity performance has great potential for health monitoring in addition to the detection of abnormal lifestyle patterns. In this paper, the successful capture of the relationships between physiological and activity profile information is presented. Experiments have been designed to collect ECG data during the completion of five predefined everyday activities using wearable wireless sensors. The impact of these activities on heart rate has been captured through the analysis of changes in heart rate patterns. This has been achieved using CUSUM with change points corresponding to the transition between activities. From this initial analysis a future mechanism for context aware health status monitoring based on sensors is proposed.
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References
United Nations, Department of Economic and Social Affairs, Population Division: World Population Prospects The 2008 Revision: Volume I: Comprehensive Tables. Technical Report ST/ESA/SER.A/287 (2009)
Zhang, S., McClean, S., Scotney, B., Hong, X., Nugent, C., Mulvenna, M.: An intervention mechanism for assistive living in smart homes. J. Ambient Intell. Smart Environ. 2(3), 233–252 (2010)
Bogan, D., Spence, J., Donnelly, P.: Connected Health in Ireland: An All-Island Review. Technical Report 1109CH-REP (2010)
Hong, X., Nugent, C., Mulvenna, M., McClean, S., Scotney, B., Devlin, S.: Evidential fusion of sensor data for activity recognition in smart homes. Pervasive Mob. Comput. 5, 236–252 (2009)
Noury, N., Hadidi, T., Laila, M., Fleury, A., Villemazet, C., Rialle, V., Franco, A.: Level of Activity, Night and Day Alternation, and well being measured in a Smart Hospital Suite. In: IEEE Engineering in Medicine and Biology Society, pp. 3328–3331. IEEE Press, Los Alamitos (2008)
Celler, B.G., Earnshaw, W., Ilsar, E.D., Betbeder-Matibet, L., Harris, M.F., Clark, R., Hesketh, T., Lovell, N.H.: Remote monitoring of health status of the elderly at home. A multidisciplinary project on aging at the University of New South Walses. Int. J. Bio-Med. Comput. 40(2), 147–155 (1995)
Yuchi, M., Jo, J.: Heart Rate Prediction Based on Physical Activity using Feedforward Neural Network. In: International Conference on Convergence and Hybrid Information Technology, pp. 344–350. IEEE Computer Society, Los Alamitos (2008)
Jakkula, V.: Predictive Data Mining to Learn Health Vitals of a Resident in a Smart Home. In: Proceedings of the 7th IEEE International Conference on Data Mining Workshops, pp. 163–168. IEEE Computer Society, Los Alamitos (2007)
Jakkula, V., Youngblood, G., Cook, D.: Identification of Lifestyle Behavior Patterns with Prediction of the Happiness of an Inhabitant in a Smart Home. In: AAAI Workshop on Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness, pp. 23–29. AAAI Press, Menlo Park (2006)
Pawar, T., Chaudhuri, S., Duttagupta, S.: Body Movement Activity Recognition for Ambulatory Cardiac Monitoring. IEEE Trans. Biomed. Eng. 54(5), 874–882 (2007)
Jakkula, V., Cook, D., Jain, G.: Prediction Models for a Smart Home Based Health Care System. In: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops, pp. 761–765. IEEE Computer Society, Los Alamitos (2007)
Ogawa, M., Tamura, T., Togawa, T.: Fully Automated Biosignal Acquisition in Daily Routine Through 1 Month. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1947–1950. IEEE Press, Los Alamitos (1998)
Shimmer Research: Shimmer Biophysical Expansion User Guide I: ECG, EMG, GSR. User Guide, Shimmer Research (2010)
Trost, S.G., McIver, K.L., Pate, R.R.: Conducting Accelerometer-Based Activity Assessments in Field-Based Research. Med. Sci. Sports Exerc. 37(11), 531–543 (2005)
Shimmer Research: SHIMMER: Sensing Health with Intelligence, Modularty, Mobility and Experimental Reusability User Manual, Revision 2.0b, Shimmer Research (2008)
Philipose, M., Fishkin, K., Patterson, M., Fox, D., Kautz, H., Hahnel, D.: Inferring activities from interactions with objects. IEEE Pervasive Comput. 3(4), 50–58 (2004)
Hinkley, D.: Inference about the change-point from cumulative sum tests. Biometrika 58, 509–522 (1971)
Pettitt, A.: A simple cumulative sum type statistic for the change-point problem with zero-one observations. Biometrika 67, 79–83 (1980)
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© 2011 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Zhang, S., Galway, L., McClean, S., Scotney, B., Finlay, D., Nugent, C.D. (2011). Deriving Relationships between Physiological Change and Activities of Daily Living Using Wearable Sensors. In: Par, G., Morrow, P. (eds) Sensor Systems and Software. S-CUBE 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23583-2_17
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DOI: https://doi.org/10.1007/978-3-642-23583-2_17
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