Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors

Yueyi Ge; Mahsa Salehi; Mor Vered; Ingrid Zukerman

doi:10.1007/978-3-031-74640-6_26

Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors

Yueyi Ge, Mahsa Salehi, Mor Vered, Ingrid Zukerman

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

In this project, we use unintrusive sensors to collect data about toilet attendance of seniors as a proxy for micturition, in order to detect anomalous behaviour. Firstly, we identify and address challenges associated with building a robust dataset of normal toilet-attendance behaviour from sensor logs. Next, since our users are healthy, we leverage medical information to build personalized simulated models of abnormal toilet attendance on the basis of users’ normal behaviour. We then compare the performance of two anomaly-detection models in detecting abnormal increases in toilet visits.

Original language	English
Title of host publication	Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023 Turin, Italy, September 18–22, 2023 Revised Selected Papers, Part IV
Editors	Rosa Meo, Fabrizio Silvestri
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	336-344
Number of pages	9
ISBN (Electronic)	9783031746406
ISBN (Print)	9783031746390
DOIs	https://doi.org/10.1007/978-3-031-74640-6_26
Publication status	Published - 2025
Event	Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living 2023 - Turin, Italy Duration: 18 Sept 2023 → 18 Sept 2023 Conference number: 6th https://link.springer.com/book/10.1007/978-3-031-74640-6 (Proceedings) https://sites.google.com/view/arial2023/home (Website)

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	2136
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living 2023
Abbreviated title	ARIAL 2023
Country/Territory	Italy
City	Turin
Period	18/09/23 → 18/09/23
Internet address	https://link.springer.com/book/10.1007/978-3-031-74640-6 (Proceedings) https://sites.google.com/view/arial2023/home (Website)

Keywords

Anomaly detection
Modeling patients toilet attendance
Unintrusive sensors
Urinary anomalies

Access to Document

10.1007/978-3-031-74640-6_26

Cite this

Ge, Y., Salehi, M., Vered, M., & Zukerman, I. (2025). Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors. In R. Meo, & F. Silvestri (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023 Turin, Italy, September 18–22, 2023 Revised Selected Papers, Part IV (pp. 336-344). (Communications in Computer and Information Science; Vol. 2136). Springer. https://doi.org/10.1007/978-3-031-74640-6_26

Ge, Yueyi ; Salehi, Mahsa ; Vered, Mor et al. / Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors. Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023 Turin, Italy, September 18–22, 2023 Revised Selected Papers, Part IV. editor / Rosa Meo ; Fabrizio Silvestri. Cham Switzerland : Springer, 2025. pp. 336-344 (Communications in Computer and Information Science).

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abstract = "In this project, we use unintrusive sensors to collect data about toilet attendance of seniors as a proxy for micturition, in order to detect anomalous behaviour. Firstly, we identify and address challenges associated with building a robust dataset of normal toilet-attendance behaviour from sensor logs. Next, since our users are healthy, we leverage medical information to build personalized simulated models of abnormal toilet attendance on the basis of users{\textquoteright} normal behaviour. We then compare the performance of two anomaly-detection models in detecting abnormal increases in toilet visits.",

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author = "Yueyi Ge and Mahsa Salehi and Mor Vered and Ingrid Zukerman",

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Ge, Y, Salehi, M , Vered, M & Zukerman, I 2025, Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors. in R Meo & F Silvestri (eds), Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023 Turin, Italy, September 18–22, 2023 Revised Selected Papers, Part IV. Communications in Computer and Information Science, vol. 2136, Springer, Cham Switzerland, pp. 336-344, Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living 2023, Turin, Italy, 18/09/23. https://doi.org/10.1007/978-3-031-74640-6_26

Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors. / Ge, Yueyi; Salehi, Mahsa ; Vered, Mor et al.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023 Turin, Italy, September 18–22, 2023 Revised Selected Papers, Part IV. ed. / Rosa Meo; Fabrizio Silvestri. Cham Switzerland: Springer, 2025. p. 336-344 (Communications in Computer and Information Science; Vol. 2136).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - In this project, we use unintrusive sensors to collect data about toilet attendance of seniors as a proxy for micturition, in order to detect anomalous behaviour. Firstly, we identify and address challenges associated with building a robust dataset of normal toilet-attendance behaviour from sensor logs. Next, since our users are healthy, we leverage medical information to build personalized simulated models of abnormal toilet attendance on the basis of users’ normal behaviour. We then compare the performance of two anomaly-detection models in detecting abnormal increases in toilet visits.

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Ge Y, Salehi M , Vered M , Zukerman I. Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors. In Meo R, Silvestri F, editors, Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023 Turin, Italy, September 18–22, 2023 Revised Selected Papers, Part IV. Cham Switzerland: Springer. 2025. p. 336-344. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-74640-6_26

Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors

Abstract

Publication series

Conference

Keywords

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