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- Deng, Weipeng, et al.
(författare)
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Energy-Efficient Monitoring of Potential Side Effects from COVID-19 Vaccines
- 2022
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Ingår i: Proceedings. - : IEEE. - 9781665454179 - 9781665454186 ; , s. 222-227
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Konferensbidrag (refereegranskat)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. |
- Li, Liying, et al.
(författare)
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Data availability optimization for cyber-physical systems
- 2022
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665454179 ; , s. 349-356
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Konferensbidrag (refereegranskat)abstract
- As the backbone of Industry 4.0, Cyber-Physical Systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. The appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. The proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%.
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