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Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults

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Abstract

The number of elderly people requiring different levels of care in their home has increased in recent times, with further increases expected. User studies show that the main concern of elderly people and their families is “fall detection and safe movement in the house”, while eschewing intrusive monitoring devices. We view abnormally long periods of inactivity as indicators of unsafe situations, and present three models of the distribution of inactivity periods obtained from unintrusive sensor observations. The performance of these models was evaluated on two real-life datasets, and compared with that of a state-of-the-art system, with our models outperforming this system.

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Notes

  1. In subsequent experiments, we observed that beam breakers are more reliable than the other two types of sensors for detecting a person crossing the inside/outside thresholds.

  2. E is set to \(\mu /2\) because it yields a confidence interval of size \(\mu \)—with fewer data points our estimation will have a confidence interval that is larger than the parameter that we are trying to estimate, i.e., \(\mu \).

  3. The adaptive behaviour of the baseline is based on a sliding window, rather than a forgetting factor. Hence, it is not possible to compare it directly with our methods for this parameter. Additionally, the baseline does not have an equivalent parameter to the MT threshold.

  4. In our two datasets there are no situations which require an alert, hence all alerts are false.

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Acknowledgments

This research was supported in part by Grant LP100200405 from the Australian Research Council, and endowments from VicHealth, the Helen McPherson Smith Trust, and Meticube, Portugal. The authors thank M. Larizza and G. Rees for their insights in the initial stages of this study, and D. Albrecht for initial discussions regarding the development of the algorithms. The authors also thank the three anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton, VIC, 3800, Australia

    Masud Moshtaghi & Ingrid Zukerman

  2. Faculty of Engineering, Monash University, Clayton, VIC, 3800, Australia

    R. Andrew Russell

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  1. Masud Moshtaghi
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Correspondence to Masud Moshtaghi.

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Moshtaghi, M., Zukerman, I. & Russell, R.A. Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults. User Model User-Adap Inter 25, 231–265 (2015). https://doi.org/10.1007/s11257-015-9162-6

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