Embedded Health Assessment Using Automated Health Alerts from In-Home Sensor Data
Abstract
We give an illustration of discreet, ongoing home monitoring that may be used to detect early health changes. Environmental sensors record patterns of behaviour and activity. Pattern alterations are noted as possible indicators of altering health. The findings of a pilot research looking at 22 characteristics gleaned from in-home sensor data are first presented. Then, a clinician in a senior living facility received health alerts created by a 1-D alert algorithm. Clinicians evaluate each warning and provide a score based on its clinical applicability. Then, in order to train and test classifiers, these ratings are utilised as the basis for comparison. Here, we outline the methodology for four categorization strategies that combine data from many sensors.
References
K. S. Boockvar and M. S. Lachs, "Predictive value of nonspecific symptoms for acute illness in nursing home residents", J. Amer. Geriatrics Soc., vol. 51, pp. 1111-1115, 2003.
N. Jiwani, K. Gupta and N. Afreen, "A Convolutional Neural Network Approach for Diabetic Retinopathy Classification," 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022, pp. 357-361, doi: 10.1109/CSNT54456.2022.9787577.
Gupta, K., & Jiwani, N. (2020). Effects of COVID-19 risk controls on the Global Supply Chain. Transactions on Latest Trends in Artificial Intelligence, 1(1).
G. Virone et al., "Behavioral patterns of older adults in assisted living", IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 3, pp. 387-398, May 2008.
M. Chan, E. Campo and D. Esteve, "Assessment of activity of elderly people using a home monitoring system", Int. J. Rehabil. Res., vol. 28, no. 1, pp. 69-76, 2005.
P. Cuddihy et al., "Successful aging", IEEE Pervasive Comput., vol. 3, no. 2, pp. 48-50, Apr. 2004.
T. van Kasteren, A. K. Noulas, G. Englebienne and B. Kröse, "Accurate activity recognition in a home setting", Proc. 10th Int. Conf. Ubiquitous Comput., pp. 1-9, 2008.
Jiwani, N., & Gupta, K. (2019). Comparison of Various Tools and Techniques used for Project Risk Management. International Journal of Machine Learning for Sustainable Development, 1(1), 51-58.
M. Ogawa, R. Suzuki, S. Otake, T. Izutsu, T. Iwaya and T. Togawa, "Long-term remote behavioral monitoring of the elderly using sensors installed in domestic houses", Proc. 24th Annu. Conf. Annu. Fall Meeting Biomed. Eng. Soc. 2nd Joint Eng. Med. Biol. (EMBS/BMES), vol. 3, pp. 23-26, Oct. 2002.
A. M. Adami, T. L. Hayes and M. Pavel, "Unobtrusive monitoring of sleep patterns", Proc. 25th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 1360-1363, Sep. 2003.
D. Hodgins, "The importance of measuring human gait", Med. Device Technol., vol. 19, no. 5:42, pp. 44-47, Sep. 2008.
K. Gupta, N. Jiwani, N. Afreen and D. D, "Liver Disease Prediction using Machine learning Classification Techniques," 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022, pp. 221-226, doi: 10.1109/CSNT54456.2022.9787574.
R. Camicioli, D. Howieson, B. Oken, G. Sexton and J. Kaye, "Motor slowing precedes cognitive impairment in the oldest old", Neurology, vol. 50, no. 5, pp. 1496-1498, 1998.
J. Kaye et al., "One walk a year to 1000 within a year: Continuous in-home unobtrusive gait assessment of older adults", Gait Posture, vol. 35, no. 2, pp. 197-202, 2012.