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- Deng, Weipeng, et al.
(author)
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Energy-Efficient Monitoring of Potential Side Effects from COVID-19 Vaccines
- 2022
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In: Proceedings. - : IEEE. - 9781665454179 - 9781665454186 ; , s. 222-227
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Conference paper (peer-reviewed)abstract
- COVID-19 has affected the world for almost two years causing lots of damages and losses of lives. With the development of sensing technology and digital health, research studies suggest to use wearable devices for monitoring COVID-19 symptoms or analyzing people’s behaviour change. As COVID-19 vaccines are getting widely available, their side effects have raised public concerns, though have not yet been thoroughly studied due to the short deployment time. As far as we know, this work is the first study to use wearable devices and mobile app to collect physiological data to explore potential side effects to human bodies from COVID-19 vaccinations. We designed and developed a mobile sensing system, which can monitor changes of physiological indicators through wearable devices, collect self-reported data from the users and proposed a green data transmission mechanism which can reduce the communication overheads. Pilot study has been conducted to evaluate the feasibility of our system. Preliminary results show that increased resting heart rate (RHR) and changes on average heart rate (HR) are observed in some participants after COVID-19 vaccinations. This study opens up the opportunity to collect larger amount of data and further investigate potential side effects from COVID-19 vaccinations.
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| 2. |
- Jiang, Zhihan, et al.
(author)
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Leveraging Machine Learning for Disease Diagnoses based on Wearable Devices : A Survey
- 2023
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:24, s. 21959-21981
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Journal article (peer-reviewed)abstract
- Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson’s disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and machine learning. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, machine learning techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multi-modality, multi-sensor, multi-devices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.
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