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Patient identification using a smart pill-bottle: poster abstract

Published: 10 November 2019 Publication History

Abstract

In this work, we investigate the identification of persons taking medication using a sensor-equipped pill-bottle. The bottle embeds inertial sensors in both the cap and body, making the added hardware un-obtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94 % accuracy. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91 %.

References

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Murtadha Aldeer, Mehdi Javanmard, and Richard Martin. 2018. A Review of Medication Adherence Monitoring Technologies. Applied System Innovation 1, 2 (2018), 14.
[2]
Murtadha Aldeer, Richard P. Martin, and Richard E. Howard. 2018. PillSense: Designing a Medication Adherence Monitoring System Using Pill Bottle-Mounted Wireless Sensors. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops). 1--6.
[3]
Murtadha M. N. Aldeer, Richard P. Martin, and Richard E. Howard. 2017. Tackling the Fidelity-energy Trade-off in Wireless Body Sensor Networks. In Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE '17). 7--12.
[4]
Guglielmo Cola, Marco Avvenuti, Fabio Musso, and Alessio Vecchio. 2016. Gait-based Authentication Using a Wrist-worn Device. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS 2016). 208--217.
[5]
Matthew Salzman, Lia Cruz, Sandra Nairn, Samuel Bechmann, Rupa Karmakar, and Brigitte M Baumann. 2019. The prevalence of modifiable parental behaviors associated with inadvertent pediatric medication ingestions. Western journal of emergency medicine 20, 2 (2019), 269--277.
[6]
Hoang Truong, Shuo Zhang, Ufuk Muncuk, Phuc Nguyen, Nam Bui, Anh Nguyen, Qin Lv, Kaushik Chowdhury, Thang Dinh, and Tam Vu. 2018. CapBand: Battery-free Successive Capacitance Sensing Wristband for Hand Gesture Recognition. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems (SenSys '18). 54--67.
[7]
U.S. Census Bureau. 2019. U.S. Census Bureau QuickFacts: UNITED STATES. https://www.census.gov/quickfacts [Online; accessed 28-April-2019].

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  • (2023)Poster Abstract: A Radar Based User Discrimination System for Medication Adherence MonitoringProceedings of the 22nd International Conference on Information Processing in Sensor Networks10.1145/3583120.3589839(338-339)Online publication date: 9-May-2023

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cover image ACM Conferences
SenSys '19: Proceedings of the 17th Conference on Embedded Networked Sensor Systems
November 2019
472 pages
ISBN:9781450369503
DOI:10.1145/3356250
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 November 2019

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Author Tags

  1. DTW
  2. SVM
  3. clustering
  4. smart pill bottle
  5. user discrimination

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  • (2023)Poster Abstract: A Radar Based User Discrimination System for Medication Adherence MonitoringProceedings of the 22nd International Conference on Information Processing in Sensor Networks10.1145/3583120.3589839(338-339)Online publication date: 9-May-2023

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