Wearable Device for Observation of Physical Activity with the Purpose of Patient Monitoring Due to COVID-19
Open accessAbstract
In late 2019, a new genre of coronavirus (COVID-19) was first identified in humans in Wuhan, China. In addition to this, COVID-19 spreads through droplets, so quarantine is necessary to halt the spread and to recover physically. This modern urgency creates a critical challenge for the latest technologies to detect and monitor potential patients of this new disease. In this vein, the Internet of Things (IoT) contributes to solving such problems. This paper proposed a wearable device that utilizes real-time monitoring to detect body temperature and ambient conditions. Moreover, the system automatically alerts the concerned person using this device. The alert is transmitted when the body exceeds the allowed temperature threshold. To achieve this, we developed an algorithm that detects physical exercise named “Continuous Displacement Algorithm” based on an accelerometer to see whether a potential temperature rise can be attributed to physical activity. The people responsible for the person in quarantine can then connect via nRF Connect or a similar central application to acquire an accurate picture of the person’s condition. This experiment included an Arduino Nano BLE 33 Sense which contains several other sensors like a 9-axis IMU, several types of temperature, and ambient and other sensors equipped. This device successfully managed to measure wrist temperature at all states, ranging from 32 °C initially to 39 °C, providing better battery autonomy than other similar devices, lasting over 12 h, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption. Furthermore, a 1D Convolutional Neural Network (CNN) was employed to classify whether the user is feverish while considering the physical activity status. The results obtained from the 1D CNN illustrated the manner in which it can be leveraged to acquire insight regarding the health of the users in the setting of the COVID-19 pandemic.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Continuous measurement of core body temperature can determine the stage of an ongoing disease.
“Another use of continuous measurement of the patient’s core body temperature is to determine the stage of the ongoing disease [155,156].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [155]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Wearable devices for the detection of COVID-19
Ates, H. C., Yetisen, A. K., Güder, F., & Dincer, C. (2021) · Nature Electronics · doi:10.1038/s41928-020-00533-1 · Cited by 253 · see citing works
IoT Healthcare Analytics: The Importance of Anomaly Detection
Ukil, A., Bandyoapdhyay, S., Puri, C., & Pal, A. (2016) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2016.158 · Cited by 245 · see citing works
Vedaei, S. S., Fotovvat, A., Mohebbian, M. R., Rahman, G. M. E., Wahid, K. A., Babyn, P., Marateb, H. R., Mansourian, M., & Sami, R. (2020) · IEEE Access · doi:10.1109/access.2020.3030194 · Cited by 227 · see citing works
The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review
Channa, A., Popescu, N., Skibinska, J., & Burget, R. (2021) · Sensors · doi:10.3390/s21175787 · Cited by 209 · see citing works
IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19
Bassam, N. A., Hussain, S. A., Al-Qaraghuli, A., Khan, J., Sumesh, E. P., & Lavanya, V. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100588 · Cited by 203 · see citing works
Temperature Regulation During Exercise
Gleeson, M. (1998) · International Journal of Sports Medicine · doi:10.1055/s-2007-971967 · Cited by 183 · see citing works
Physical activity during COVID-19 induced lockdown: recommendations
Füzéki, E., Groneberg, D. A., & Banzer, W. E. (2020) · Journal of Occupational Medicine and Toxicology · doi:10.1186/s12995-020-00278-9 · Cited by 183 · see citing works
Feasibility of continuous fever monitoring using wearable devices
Smarr, B. L., Aschbacher, K., Fisher, S. M., Chowdhary, A., Dilchert, S., Puldon, K., Rao, A., Hecht, F. M., & Mason, A. E. (2020) · Scientific Reports · doi:10.1038/s41598-020-78355-6 · Cited by 133 · see citing works
Human activity recognition using machine learning methods in a smart healthcare environment
Subaşı, A., Khateeb, K., Brahimi, T., & Sarirete, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-819043-2.00005-8 · Cited by 132 · see citing works
Wearable Technology to Assist the Patients Infected with Novel Coronavirus (COVID-19)
Islam, M. M., Mahmud, S., Muhammad, L. J., Islam, M. R., Nooruddin, S., & Ayon, S. I. (2020) · SN Computer Science · doi:10.1007/s42979-020-00335-4 · Cited by 125 · see citing works
A Headset Like Wearable Device to Track COVID-19 Symptoms
Stojanovic, R. D., Škraba, A., & Lutovac, B. M. (2020) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco49872.2020.9134211 · Cited by 88 · see citing works
Hoang, M. L., Carratù, M., Paciello, V., & Pietrosanto, A. (2021) · Sensors · doi:10.3390/s21072313 · Cited by 61 · see citing works
Theodoropoulos, P., Spandonidis, C. C., Giannopoulos, F., & Fassois, S. D. (2021) · Sensors · doi:10.3390/s21165658 · Cited by 47 · see citing works
Self-citationChen, G., Xie, J., Dai, G., Zheng, P., Hu, X., Lu, H., Xu, L., Chen, X., & Chen, X. (2020) · Iranian Journal of Public Health · doi:10.18502/ijph.v49is1.3670 · Cited by 40 · see citing works
Wearable technology: Covid-19 and the rise of remote clinical monitoring
Best, J. (2021) · BMJ · doi:10.1136/bmj.n413 · Cited by 30 · see citing works
Quraishi, S. A., Berra, L. M., & Nozari, A. (2020) · Occupational and Environmental Medicine · doi:10.1136/oemed-2020-106653 · Cited by 25 · see citing works
IoT based Smart Healthcare System to Detect and Alert Covid Symptom
Anjali, K., Anand, R., Prabhu, S. D., & Geethu, R. S. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9488952 · Cited by 25 · see citing works
Cloud-based COVID-19 Patient Monitoring using Arduino
Hanoon, I. K., & Aal-Nouman, M. I. (2021) · East Indonesia Conference on Computer and Information Technology (EIConCIT) · doi:10.1109/eiconcit50028.2021.9431881 · Cited by 15 · see citing works
An IoT and artificial intelligence-based patient care system focused on COVID-19 pandemic
Goar, V. K., Yadav, N. S., Chowdhary, C. L., Kumaresan, P., & Mittal, M. (2021) · International Journal of Networking and Virtual Organisations · doi:10.1504/ijnvo.2021.120169 · Cited by 13 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Subasi, A.; Khateeb, K.; Brahimi, T.; Sarirete, A. Chapter 5-Human activity recognition using machine learning methods in a smart healthcare environment. In Innovation in Health Informatics;
Cited by
Sorted by citations
Jouini, O., Sethom, K., Namoun, A., Aljohani, N. F., Alanazi, M. H., & Alanazi, M. N. (2024) · Technologies · doi:10.3390/technologies12060081 · Cited by 122 · see citing works
The Current State of Optical Sensors in Medical Wearables
Vavrinsky, E., Esfahani, N. E., Hausner, M., Kuzma, A., Rezo, V., Donoval, M., & Kosnacova, H. (2022) · Biosensors · doi:10.3390/bios12040217 · Cited by 103 · see citing works
Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
Mitro, N., Krommyda, M., & Amditis, A. (2022) · Applied Sciences · doi:10.3390/app12052315 · Cited by 53 · see citing works
Chen, K., Shin, J., Hasan, M. A. M., Liaw, J., Okuyama, Y., & Tomioka, Y. (2022) · Sensors · doi:10.3390/s22155700 · Cited by 32 · see citing works
Adaimi, R., & Thomaz, E. (2022) · Sensors · doi:10.3390/s22186881 · Cited by 28 · see citing works
Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data
Luu, L., Pillai, A., Lea, H., Buendía, R., Khan, F. M., & Dennis, G. (2022) · Sensors · doi:10.3390/s22113989 · Cited by 25 · see citing works
Dkhar, D. S., Kumari, R., Mahapatra, S., divya, D. D., & Chandra, P. (2022) · Electroanalysis · doi:10.1002/elan.202200154 · Cited by 18 · see citing works
Viciano-Tudela, S., Sendra, S., Lloret, J., Tomas, J., & Belda, J. (2022) · Electronics · doi:10.3390/electronics11193061 · Cited by 13 · see citing works
Skyfall: Signal Fusion from a Smartphone Falling from the Stratosphere
Garcés, M., Bowman, D., Zeiler, C., Christe, A. J., Yoshiyama, T., Williams, B., Colet, M., Takazawa, S. K., & Popenhagen, S. K. (2022) · Signals · doi:10.3390/signals3020014 · Cited by 12 · see citing works
Watch-Type Dual-Mode Wearable Health Device
Park, H., Nguyen, L. H. T., Lakshminarayana, S., Sun, Y., & Jung, S. (2023) · ECS Sensors Plus · doi:10.1149/2754-2726/acc74d · Cited by 10 · see citing works
Wearable Devices for Patient Monitoring System using IoT
Arunkumar, J. R., Raman, R., Sivakumar, S., & Pavithra, R. (2023) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces57224.2023.10192741 · Cited by 10 · see citing works
X-RCRNet: An explainable deep-learning network for COVID-19 detection using ECG beat signals
Nkengue, M. J., Zeng, X., Koehl, L., & Tao, X. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105424 · Cited by 9 · see citing works
Measuring of the Energy Expenditure during Balance Training Using Wearable Electronics
Veselý, T., Janatová, M., Smrčka, P., Vítězník, M., Kliment, R., & Hána, K. (2022) · Electronics · doi:10.3390/electronics11071096 · Cited by 7 · see citing works
Gallo, A., Fregola, S., Menon, M., Talarico, F., Fragkiadaki, S., Kontaxopolou, D., Vukojević, K., Matijaca, D., Miljkovic, M., Kozetinac, S., et al. (2023) · COVID · doi:10.3390/covid3020007 · Cited by 6 · see citing works
Free-Space Optical Space Division Multiplexing for Smart Healthcare
Amphawan, A., Anwar, A., Ong, S., Sutanto, J., Neo, T., & Anwar, K. (2022) · International Conference on Network and Information Systems for Computers (ICNISC) · doi:10.1109/icnisc57059.2022.00110 · Cited by 3 · see citing works
An intelligent garment for long COVID-19 real-time monitoring
Nkengue, M. J., Zeng, X., Koehl, L., Tao, X., Dassonville, F., Dumont, N., Ye, S., Akwa, Y., & Ye, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109067 · Cited by 3 · see citing works
Internet of Things and wearable device-basedtracking of an elderly person's physical activity
Zearah, S. A., Abed, A. M., Hussian, A., Ettyem, S. A., Hussam, R., & Matti, I. (2023) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434171 · Cited by 2 · see citing works
Development of a mist computing device for a smart insole aiming on human gait analysis
Dimitrios, S., Giannopoulos, F., & Spandonidis, C. C. (2022) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast54814.2022.9837593 · Cited by 1 · see citing works
Self-citationNajim, A., & Nasri, N. (2023) · Bilad Alrafidain Journal for Engineering Science and Technology · doi:10.56990/bajest/2023.020105 · Cited by 1 · see citing works
Smart Health Care Monitoring and Indoor Occupancy Control System using Arduino
Jassim Mohamed, S., Rahul, R., Gengaraj, M., Suseel Raja, P., Jegan Raj, G., & Kalaivani, L. (2022) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca55336.2022.10009584
The SI4CARE Project: Using Wearable Devices for Assisting People with Dementia in CalabriaPreprint
Gallo, A., Fregola, S., Menon, M., Talarico, F., Fragkiadaki, S., Kontaxopoulou, D., Vukojević, K., Matijaca, D., Milikovic, M., Kozetinac, S., et al. (2023) · Preprints.org · doi:10.20944/preprints202301.0427.v1
The SI4CARE Project: Using Wearable Devices for Assisting People with Dementia in CalabriaPreprint
Gallo, A., Fregola, S., Menon, M., Talarico, F., Fragkiadaki, S., Kontaxopoulou, D., Stanitsa, E., Vukojević, K., Matijaca, D., Milikovic, M., et al. (2023) · Preprints.org · doi:10.20944/preprints202301.0427.v2
Telecare Device System Based on Patient Body Temperature
Hadi, M. S., Arrohman, M. L., Sugiono, B. S. R., Zaeni, I. A. E., & Irvan, M. (2023) · doi:10.1109/iceeie59078.2023.10334897
Internet of Things Based Remote Body Temperature Monitoring in Public Transportation
Atioğlu, A. B., & Cihanbeğendi, Ö. (2024) · The European Journal of Research and Development · doi:10.56038/ejrnd.v4i4.580
Monitoring vital signs with an IoT devices
Wojszczyk, R., & Sawala, P. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.11.910
- Authors
- Daskalos, A.-C., Theodoropoulos, P., Spandonidis, C., & Vordos, N.
- Affiliations
- International Hellenic University (GR)
- Prisma Electronics (Greece) (GR)
- Corresponding author
- Daskalos, Angelos-Christos
- Theodoropoulos, Panayiotis
- Venue
- Signals
- Publisher
- MDPI AG
- Published
- 2022-01-06
- Volume
- 3
- Issue
- 1
- Pages
- 11-28
- ISSN
- 2624-6120 (ISSN-L)
- OpenAlex
- W4205863006
- Cited by
- 26
- References
- 18
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 9 · 2022: 12
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Signals (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Arduino and IoT Applications
- Keywords
- wearable devices, COVID-19 patient monitoring, quarantine, body temperature monitoring, physical activity detection, accelerometer, Bluetooth Low Energy, 1D convolutional neural network, fever detection, Internet of Things, ambient temperature measurement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
