Abstract
We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture activity patterns. Changes in the activity patterns are detected as potential signs of changing health. A simple alert algorithm has been implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth in developing classifiers. Here, we present the methodology and results for two classification approaches using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The results show similar performance for the two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training.
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References
Boockvar, K.S., Lachs, M.S.: Predictive value of nonspecific symptoms for acute illness in nursing home residents. J. of the American Geriatrics Society 51(8), 1111–1115 (2003)
Ridley, S.: The recognition and early management of critical illness. Annals of the Royal College of Surgeons of England 87(5), 315–322 (2005)
Demiris, G., Hensel, B.K.: Technologies for an aging society: a systematic review of "smart home" applications. Year. Med. Inform., 33–40 (2008)
Ohta, S., Nakamoto, H., Shinagawa, Y., Tanikawa, T.: A health monitoring system for elderly people living alone. Journal of Telemedicine & Telecare 8(3), 151–156 (2008)
Chan, M., Campo, E., Esteve, D.: Assessment of activity of elderly people using a home monitoring system. Intl. Journal of Rehabilitation Research 28(1), 69–76 (2005)
Harvey, N., Zhou, Z., Keller, J.M., Rantz, M., He, Z.: Automated estimation of elder activity levels from anonymized video data. In: Proc., Intl. Conf. IEEE Eng. Med. Biol. Soc., pp. 7236–7239 (2009)
Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in Health Smart Homes: Sensors, algorithms, and first experimental results. IEEE Transaction on Information Technology in Biomedicine 14(2), 274–283 (2010)
Singla, G., Cook, D.J., Schmitter-Edgecombe, M.: Recognizing independent and joint activities among multiple residents in smart environments. J. Ambient Intell. Humaniz. Comput. 1(1), 57–63 (2010)
Hayes, T.L., Riley, T., Pavel, M., Kaye, J.A.: A method for estimating rest-activity patterns using simple pyroelectric motion sensors. In: Proc., Intl. Conf. IEEE Eng. Med. Biol. Soc. (2010)
Mack, D.C., Patrie, J.T., Suratt, P.M., Felder, R.A., Alwan, M.: Development and preliminary validation of heart rate and breathing rate detection using a passive, ballistocardiography-based sleep monitoring system. IEEE Transaction on Information Technology in Biomedicine 13, 111–120 (2009)
Heise, D., Rosales, L., Skubic, M., Devaney, M.J.: Refinement and Evaluation of a Hydraulic Bed Sensor. In: Proc., Intl. Conf. IEEE Eng. Med. Biol. Soc., pp. 4356–4360 (2011)
Beattie, Z.T., Hagen, C.C., Pavel, M., Hayes, T.L.: Classification of breathing events using load cells under the bed. In: Proc., Intl. Conf. IEEE Eng. Med. Biol. Soc., pp. 3921–3924 (2009)
Hagler, S., Austin, D., Hayes, T.L., Kaye, J., Pavel, M.: Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. IEEE Trans. Biomed. Eng. 57(4), 813–820 (2010)
Stone, E., Anderson, D., Skubic, M., Keller, J.M.: Extracting footfalls from voxel data. In: Proc. Intl. Conf. IEEE Eng. Med. Biol. Soc. (2010)
Yardibi, T., Cuddihy, P., Genc, S., Bufi, C., Skubic, M., Rantz, M., Liu, L., Phillips, C.: Gait characterization via pulse-Doppler radar. In: Proc., IEEE Intl. Conf. Pervasive Computing and Communications: SmartE Workshop, pp. 662–667 (2011)
Stone, E., Skubic, M.: Evaluation of an Inexpensive Depth Camera for In-Home Gait Assessment. Journal of Ambient Intelligence and Smart Environments 3(4), 349–361 (2011)
Cook, D., Schmitter-Edgecombe, M.: Assessing the quality of activities in a smart environment. Methods of Information in Medicine 48(5), 459–467 (2009)
Virone, G., Alwan, M., Dala, S., Kell, S., Stankovic, J.A., Felder, R.: Behavioral patterns of older adults in assisted living. IEEE Transaction on Information Technology in Biomedicine 12(3), 387–398 (2008)
Barger, T.S., Brown, D.E., Alwan, M.: Health-status monitoring through analysis of behavioral patterns. IEEE Trans. SMC-A 35(1), 22–27 (2005)
Brown, S., Majeed, B., Clarke, N., Lee, B.-S.: Developing a well-being monitoring system-Modeling and data analysis techniques. In: Mann, W., Helel, A. (eds.) Promoting Independence for Older Persons with Disabilities, Washington, DC (2006)
Wang, S., Skubic, M., Zhu, Y.: Activity density map dis-similarity comparison for eldercare monitoring. In: Proc., Intl. Conf. IEEE Eng. Med. Biol. Soc., pp. 7232–7235 (2009)
Rantz, M.J., Skubic, M., Koopman, R.J., Alexander, G., Phillips, L., Musterman, K.I., Back, J.R., Aud, M.A., Galambos, C., Guevara, R.D., Miller, S.J.: Automated technology to speed recognition of signs of illness in older adults. J. of Gerontological Nursing (in press)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2006)
Yager, R.R.: On a general class of fuzzy connectives. Fuzzy Sets and Systems 4(3), 235–242 (1980)
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Skubic, M., Guevara, R.D., Rantz, M. (2012). Testing Classifiers for Embedded Health Assessment. In: Donnelly, M., Paggetti, C., Nugent, C., Mokhtari, M. (eds) Impact Analysis of Solutions for Chronic Disease Prevention and Management. ICOST 2012. Lecture Notes in Computer Science, vol 7251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30779-9_25
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DOI: https://doi.org/10.1007/978-3-642-30779-9_25
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