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

Yueyi Ge; Ingrid Zukerman; Mahsa Salehi; Mor Vered

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

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

Yueyi Ge, Ingrid Zukerman, Mahsa Salehi, Mor Vered

Monash University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › 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, Revised Selected Papers
Editors	Rosa Meo, Fabrizio Silvestri
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	336-344
Number of pages	9
ISBN (Print)	9783031746390
DOIs	https://doi.org/10.1007/978-3-031-74640-6_26
State	Published - 2025
Externally published	Yes
Event	Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy Duration: 18 Sep 2023 → 22 Sep 2023

Publication series

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

Conference

Conference	Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
Country/Territory	Italy
City	Turin
Period	18/09/23 → 22/09/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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., Zukerman, I., Salehi, M., & Vered, M. (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, Revised Selected Papers (pp. 336-344). (Communications in Computer and Information Science; Vol. 2136 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-74640-6_26

Ge, Yueyi ; Zukerman, Ingrid ; Salehi, Mahsa 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, Revised Selected Papers. editor / Rosa Meo ; Fabrizio Silvestri. Springer Science and Business Media Deutschland GmbH, 2025. pp. 336-344 (Communications in Computer and Information Science).

@inproceedings{e59cd4f3d7984290bb39e6b728ba4bd7,

title = "Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors",

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.",

keywords = "Anomaly detection, Modeling patients toilet attendance, Unintrusive sensors, Urinary anomalies",

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

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",

year = "2025",

doi = "10.1007/978-3-031-74640-6\_26",

language = "אנגלית",

isbn = "9783031746390",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "336--344",

editor = "Rosa Meo and Fabrizio Silvestri",

booktitle = "Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers",

address = "גרמניה",

}

Ge, Y, Zukerman, I, Salehi, M & Vered, M 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, Revised Selected Papers. Communications in Computer and Information Science, vol. 2136 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 336-344, Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 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; Zukerman, Ingrid; Salehi, Mahsa et al.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers. ed. / Rosa Meo; Fabrizio Silvestri. Springer Science and Business Media Deutschland GmbH, 2025. p. 336-344 (Communications in Computer and Information Science; Vol. 2136 CCIS).

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

TY - GEN

T1 - Modeling and Detecting Urinary Anomalies in Seniors from Data Obtained by Unintrusive Sensors

AU - Ge, Yueyi

AU - Zukerman, Ingrid

AU - Salehi, Mahsa

AU - Vered, Mor

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - 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.

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.

KW - Anomaly detection

KW - Modeling patients toilet attendance

KW - Unintrusive sensors

KW - Urinary anomalies

UR - https://www.scopus.com/pages/publications/85215930721

U2 - 10.1007/978-3-031-74640-6_26

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

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85215930721

SN - 9783031746390

T3 - Communications in Computer and Information Science

SP - 336

EP - 344

BT - Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers

A2 - Meo, Rosa

A2 - Silvestri, Fabrizio

PB - Springer Science and Business Media Deutschland GmbH

T2 - Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023

Y2 - 18 September 2023 through 22 September 2023

ER -

Ge Y, Zukerman I, Salehi M, Vered M. 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, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 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

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this