Poster abstract: a radar based user discrimination system for medication adherence monitoring

Murtadha Aldeer; David Waterworth; Parth Jain; Xiang Meng; Richard P. Martin; Jorge Ortiz

doi:10.1145/3583120.3589839

Poster abstract: a radar based user discrimination system for medication adherence monitoring

Murtadha Aldeer, David Waterworth, Parth Jain, Xiang Meng, Richard P. Martin, Jorge Ortiz

School of Computing

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

Abstract

Medication non-adherence is a major healthcare challenge globally, with over half of patients with chronic conditions in developed countries failing to follow their prescribed medication regimen. This can lead to poor disease outcomes, increased hospital visits, and a significant financial burden on healthcare systems [1]. These issues have driven a recent wave of research, including the development of smart adherence products [6] that can be incorporated into a patient's daily life to monitor medication adherence. In this work, we present a radar-based system for user identification while taking medication, which extends our recent work [5]. we conducted preliminary experiments examining semi-medication-taking activities executed by 6 subjects. Our system achieved 80% accuracy in identifying who has taken the medication in a group of 3 subjects.

Original language	English
Title of host publication	IPSN '23
Subtitle of host publication	proceedings of the 2023 the 22nd International Conference on Information Processing in Sensor Networks
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	338-339
Number of pages	2
ISBN (Electronic)	9798400701184
DOIs	https://doi.org/10.1145/3583120.3589839
Publication status	Published - 2023
Event	22nd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2023 - San Antonio, United States Duration: 9 May 2023 → 12 May 2023

Conference

Conference	22nd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2023
Country/Territory	United States
City	San Antonio
Period	9/05/23 → 12/05/23

Keywords

Medication adherence
Radar sensing
User identification

Access to Document

10.1145/3583120.3589839

Cite this

Aldeer, M., Waterworth, D., Jain, P., Meng, X., Martin, R. P., & Ortiz, J. (2023). Poster abstract: a radar based user discrimination system for medication adherence monitoring. In IPSN '23: proceedings of the 2023 the 22nd International Conference on Information Processing in Sensor Networks (pp. 338-339). Association for Computing Machinery. https://doi.org/10.1145/3583120.3589839

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title = "Poster abstract: a radar based user discrimination system for medication adherence monitoring",

abstract = "Medication non-adherence is a major healthcare challenge globally, with over half of patients with chronic conditions in developed countries failing to follow their prescribed medication regimen. This can lead to poor disease outcomes, increased hospital visits, and a significant financial burden on healthcare systems [1]. These issues have driven a recent wave of research, including the development of smart adherence products [6] that can be incorporated into a patient's daily life to monitor medication adherence. In this work, we present a radar-based system for user identification while taking medication, which extends our recent work [5]. we conducted preliminary experiments examining semi-medication-taking activities executed by 6 subjects. Our system achieved 80\% accuracy in identifying who has taken the medication in a group of 3 subjects.",

keywords = "Medication adherence, Radar sensing, User identification",

author = "Murtadha Aldeer and David Waterworth and Parth Jain and Xiang Meng and Martin, \{Richard P.\} and Jorge Ortiz",

year = "2023",

doi = "10.1145/3583120.3589839",

language = "English",

pages = "338--339",

booktitle = "IPSN '23",

publisher = "Association for Computing Machinery",

address = "United States",

note = "22nd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2023 ; Conference date: 09-05-2023 Through 12-05-2023",

}

Aldeer, M, Waterworth, D, Jain, P, Meng, X, Martin, RP & Ortiz, J 2023, Poster abstract: a radar based user discrimination system for medication adherence monitoring. in IPSN '23: proceedings of the 2023 the 22nd International Conference on Information Processing in Sensor Networks. Association for Computing Machinery, New York, pp. 338-339, 22nd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2023, San Antonio, United States, 9/05/23. https://doi.org/10.1145/3583120.3589839

Poster abstract: a radar based user discrimination system for medication adherence monitoring. / Aldeer, Murtadha; Waterworth, David; Jain, Parth et al.
IPSN '23: proceedings of the 2023 the 22nd International Conference on Information Processing in Sensor Networks. New York: Association for Computing Machinery, 2023. p. 338-339.

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

TY - CHAP

T1 - Poster abstract

T2 - 22nd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2023

AU - Aldeer, Murtadha

AU - Waterworth, David

AU - Jain, Parth

AU - Meng, Xiang

AU - Martin, Richard P.

AU - Ortiz, Jorge

PY - 2023

Y1 - 2023

N2 - Medication non-adherence is a major healthcare challenge globally, with over half of patients with chronic conditions in developed countries failing to follow their prescribed medication regimen. This can lead to poor disease outcomes, increased hospital visits, and a significant financial burden on healthcare systems [1]. These issues have driven a recent wave of research, including the development of smart adherence products [6] that can be incorporated into a patient's daily life to monitor medication adherence. In this work, we present a radar-based system for user identification while taking medication, which extends our recent work [5]. we conducted preliminary experiments examining semi-medication-taking activities executed by 6 subjects. Our system achieved 80% accuracy in identifying who has taken the medication in a group of 3 subjects.

AB - Medication non-adherence is a major healthcare challenge globally, with over half of patients with chronic conditions in developed countries failing to follow their prescribed medication regimen. This can lead to poor disease outcomes, increased hospital visits, and a significant financial burden on healthcare systems [1]. These issues have driven a recent wave of research, including the development of smart adherence products [6] that can be incorporated into a patient's daily life to monitor medication adherence. In this work, we present a radar-based system for user identification while taking medication, which extends our recent work [5]. we conducted preliminary experiments examining semi-medication-taking activities executed by 6 subjects. Our system achieved 80% accuracy in identifying who has taken the medication in a group of 3 subjects.

KW - Medication adherence

KW - Radar sensing

KW - User identification

UR - https://www.scopus.com/pages/publications/85160076517

U2 - 10.1145/3583120.3589839

DO - 10.1145/3583120.3589839

M3 - Conference abstract

AN - SCOPUS:85160076517

SP - 338

EP - 339

BT - IPSN '23

PB - Association for Computing Machinery

CY - New York

Y2 - 9 May 2023 through 12 May 2023

ER -

Poster abstract: a radar based user discrimination system for medication adherence monitoring

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