Abstract
Advances in sensor technologies have provided the opportunity to perform continuous and unobtrusive capturing of physiological signals. One particular application that has benefitted from this technology is the remote monitoring and management of cardiovascular conditions. In this paper, details of an investigation considering the impact of everyday activities on heart rate are presented. ECG and accelerometer signals collected from wearable wireless sensors have been utilized to investigate the underlying relationships between physiological and activity-related profile information. The impact of activities on heart rate has been captured through analysis of the patterns of heart rate using the CUSUM algorithm. Subsequently, results have shown that a change in the pattern of heart rate is detected shortly after an activity commences. Further extensions of the research are also proposed, including integration of a range of ECG features and intelligent data analysis techniques, thereby facilitating the future development of context aware health monitoring mechanisms.
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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. Int. J. Bio-Med. Comput. 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 Press, New York (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 Press, New York (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. 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 Press, New York (2007)
Shimmer Research: SHIMMER Sensing Health with Intelligence, Modularity, Mobility and Experimental Reusability User Manual. Shimmer Research, Dublin (2008)
Shimmer Research: Shimmer Biophysical Expansion User Guide I: ECG, EMG, GSR. Shimmer Research, Dublin (2010)
Pettitt, A.: A simple cumulative sum type statistic for the change-point problem with zero-one observations. Biometrika 67, 79–83 (1980)
Zhang, S., Galway, L., McClean, S., Scotney, B., Finlay, D., Nugent, C.: Deriving Relationships between Physiological Change and Activities of Daily Living using Wearable Sensors. In: Procceding of the 2nd International ICST Conference on Sensor Systems and Software (2010) (in press)
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Galway, L., Zhang, S., Nugent, C., McClean, S., Finlay, D., Scotney, B. (2011). Utilizing Wearable Sensors to Investigate the Impact of Everyday Activities on Heart Rate. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds) Toward Useful Services for Elderly and People with Disabilities. ICOST 2011. Lecture Notes in Computer Science, vol 6719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21535-3_24
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DOI: https://doi.org/10.1007/978-3-642-21535-3_24
Publisher Name: Springer, Berlin, Heidelberg
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