Medication intake adherence with real time activity recognition on IoT

Serdaroğlu, Kemal Çağrı; Uslu, Gamze; Baydere, Şebnem

doi:10.1109/wimob.2015.7347966

Cited by 24 publications

(8 citation statements)

References 11 publications

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“…The potential for supporting people with respiratory conditions, such as asthma and COPD, have also been studied including; wireless body-worn sensors, measuring factors such as a heart rate, blood pressure, and oxygen saturation levels [13,57]; and environmental sensors measuring air humidity, and temperature [13] and air quality [44]. Finally, connected pill boxes have the potential to support medication adherence for a range of conditions, by providing relevant reminders that go beyond traditional preset alarms [65,83].…”

Section: Monitoring Chronic Health Conditions Through the Iotmentioning

confidence: 99%

Future Opportunities for IoT to Support People with Parkinson's

McNaney

Tsekleves

Synnott

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Recent years have seen an explosion of internet of things (IoT) technologies being released to the market. There has also been an emerging interest in the potentials of IoT devices to support people with chronic health conditions. In this paper, we describe the results of engagements to scope the future potentials of IoT for supporting people with Parkinson's. We ran a 2-day multidisciplinary event with professionals with expertise in Parkinson's and IoT, to explore the opportunities, challenges and benefits. We then ran 4 workshops, engaging 13 people with Parkinson's and caregivers, to scope out the needs, values and desires that the community has for utilizing IoT to monitor their symptoms. This work contributes a set of considerations for future IoT solutions that might support people with Parkinson's in better understanding their condition, through the provision of objective measurements that correspond to their, currently unmeasured, subjective experiences.

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Section: Monitoring Chronic Health Conditions Through the Iotmentioning

confidence: 99%

Future Opportunities for IoT to Support People with Parkinson's

McNaney

Tsekleves

Synnott

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…Classification accuracies were 95% and 97.5% for cap twisting and hand-to-mouth actions, respectively. Another application of accelerometers embedded in smartwatches is presented in [71]. One smartwatch placed on the right hand of the user was used to collect the acceleration data for the actions associated with medication intake.…”

Section: Wearable Sensorsmentioning

confidence: 99%

A Review of Medication Adherence Monitoring Technologies

Aldeer

Javanmard

Martin

2018

ASI

112

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Medication non-adherence is a prevalent, complex problem. Failure to follow medication schedules may lead to major health complications, including death. Proper medication adherence is thus required in order to gain the greatest possible drug benefit during a patient's treatment. Interventions have been proven to improve medication adherence if deviations are detected. This review focuses on recent advances in the field of technology-based medication adherence approaches and pays particular attention to their technical monitoring aspects. The taxonomy space of this review spans multiple techniques including sensor systems, proximity sensing, vision systems, and combinations of these. As each technique has unique advantages and limitations, this work describes their trade-offs in accuracy, energy consumption, acceptability and user's comfort, and user authentication.

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“…Mo et al [ 84 ] have used a smartphone to collect data from body worn sensors with energy harvesting capabilities, and to send it over to a distant server for processing and activity recognition. Serdaroglu et al [ 85 ] have used a wrist worn watch with a built-in accelerometer to collect data in order to monitor patients’ daily medication intake. The data is sent wirelessly from the watch to a gateway, connected to a computer via USB, before being sent over to a web server.…”

Section: Real-time Centralized Activity Recognitionmentioning

confidence: 99%

From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review

Baghezza

Bouchard

Bouzouane

et al. 2021

Sensors

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This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.

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Medication intake adherence with real time activity recognition on IoT

Cited by 24 publications

References 11 publications

Future Opportunities for IoT to Support People with Parkinson's

Future Opportunities for IoT to Support People with Parkinson's

A Review of Medication Adherence Monitoring Technologies

From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review

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