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User Identification using Smart Pill Bottles: Systems and Machine Learning Models: PhD Forum Abstract

Published: 18 May 2021 Publication History

Abstract

Medication adherence is one of the leading factors that can make the difference between life and death, especially for patients managing chronic conditions. These issues have driven a recent wave of research, including the development of smart pill bottles that monitor when a pill is extracted. The goal of my PhD research is to develop systems that can identify who has taken the pill and when. To do so, we have designed different generations of smart pill bottles and associated algorithms for enabling several applications. We use 3D-printed pill bottles equipped with a magnetic switch sensor and an accelerometer. The bottles are carefully designed to minimize power consumption and we devise new machine learning-based techniques that use the accelerometer data generated during bottle interaction (pill extraction) to capture the user gesture that is extracting the pill. Our work can be classified into 3 core thrust areas: 1) User identification using smart pill bottle systems. 2) Adaptive learning techniques for user identification across multiple smart pill bottles. 3) Latent conditions monitoring using smart pill bottles.

References

[1]
Murtadha Aldeer et. al. 2021. Poster Abstract: User Identification Across Multiple Smart Pill Bottle Systems. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021) (IPSN'21). 2.
[2]
Murtadha Aldeer et. al. 2021. Unobtrusive patient identification using smart pill-bottle systems. Internet of Things 14 (2021), 100389.
[3]
Hanbin Zhang et. al. 2021. mHealth Technologies Towards Parkinson's Disease Detection and Monitoring in Daily Life: A Comprehensive Review. IEEE Reviews in Biomedical Engineering 14 (2021), 71--81.
[4]
Bedartha Goswami. 2019. A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots. Vibration 2, 4 (2019), 332--368.

Cited By

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  • (2022)MedBuds: In-Ear Inertial Medication Taking Detection Using Smart Wireless Earbuds2022 2nd International Workshop on Cyber-Physical-Human System Design and Implementation (CPHS)10.1109/CPHS56133.2022.9804515(19-23)Online publication date: May-2022
  • (2022)Radar-based monitoring system for medication tampering using data augmentation and multivariate time series classificationSmart Health10.1016/j.smhl.2021.10024523(100245)Online publication date: Mar-2022

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  1. User Identification using Smart Pill Bottles: Systems and Machine Learning Models: PhD Forum Abstract

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      cover image ACM Conferences
      IPSN '21: Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021)
      May 2021
      423 pages
      ISBN:9781450380980
      DOI:10.1145/3412382
      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 18 May 2021

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      Author Tags

      1. Inertial sensors
      2. Smart pill bottles
      3. User identification

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      View all
      • (2022)MedBuds: In-Ear Inertial Medication Taking Detection Using Smart Wireless Earbuds2022 2nd International Workshop on Cyber-Physical-Human System Design and Implementation (CPHS)10.1109/CPHS56133.2022.9804515(19-23)Online publication date: May-2022
      • (2022)Radar-based monitoring system for medication tampering using data augmentation and multivariate time series classificationSmart Health10.1016/j.smhl.2021.10024523(100245)Online publication date: Mar-2022

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