DIY Wrist-Worn Device for Physiological Monitoring: Metrological Evaluation at Different Band Tightening Levels | SpringerLink
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DIY Wrist-Worn Device for Physiological Monitoring: Metrological Evaluation at Different Band Tightening Levels

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IoT Technologies for Health Care (HealthyIoT 2021)

Abstract

Wearable devices are currently employed in several application fields, especially in the healthcare context, thanks to the advent of IoT technology in the global market. However, there are few studies focused on the reliability of collected data depending on the best wearing conditions, e.g. the band tightness in the case of wrist-worn devices, necessary to optimise the quality of the measured data. The aim of this study is to evaluate the variability of heart rate (HR) and tightening force data measured with a Do-It-Yourself (DIY) wrist-worn device, considering three different band tightening levels: loose, medium and tight. Results show that the increasing tightening levels produce an increasing tightening force, as expected; interestingly, the coefficient of variation is minimum (i.e., 0.16%) when the band tightening level is medium.

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References

  1. Telemetry viewer. http://www.farrellf.com/TelemetryViewer/

  2. Alsulami, M.H., Almuayqil, S.N., Atkins, A.S.: A comparison between heart-rate monitoring smart devices for ambient assisted living. J. Ambient Intell. Hum. Comput. 1–12 (2021). https://doi.org/10.1007/s12652-021-03025-y

  3. Belmonte-Fernández, Ó., Puertas-Cabedo, A., Torres-Sospedra, J., Montoliu-Colás, R., Trilles-Oliver, S.: An indoor positioning system based on wearables for ambient-assisted living. Sensors 17(12), 36 (2016)

    Article  Google Scholar 

  4. Bhagat, Y.A., Das, K., Bui, T.: Show me the SO2: real-time led oximetry display on multimodal wearable devices. In: Cullum, B.M., Kiehl, D., McLamore, E.S. (eds.) Smart Biomedical and Physiological Sensor Technology XVIII. vol. 11757, pp. 15–20. International Society for Optics and Photonics, SPIE (2021), https://doi.org/10.1117/12.2588173

  5. Can, Y.S., Ersoy, C.: Privacy-preserving federated deep learning for wearable IoT-based biomedical monitoring. ACM Trans. Internet Technol. 21(1), 1–7 (2021)

    Article  Google Scholar 

  6. Casaccia, S., Revel, G., Cosoli, G., Scalise, L.: Assessment of domestic well-being: from perception to measurement. IEEE Int. Instr. Measure Mag. 24(6), 58–67 (2021)

    Article  Google Scholar 

  7. Casaccia, S., et al.: Measurement of users’ well-being through domotic sensors and machine learning algorithms. IEEE Sens. J. 20(14), 8029–8038 (2020)

    Article  Google Scholar 

  8. Casaccia, S., Revel, G.M., Scalise, L., Cucchieri, G., Rossi, L.: Smartwatches selection: market analysis and metrological characterization on the measurement of number of steps. In: 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–5 (2021). https://doi.org/10.1109/MeMeA52024.2021.9478770

  9. Cosoli, G., Iadarola, G., Poli, A., Spinsante, S.: Learning classifiers for analysis of blood volume pulse signals in IoT-enabled systems. In: IEEE MetroInd4.0 & IoT, Virtual Conference (2021). https://www.metroind40iot.org/

  10. Cosoli, G., Scalise, L., Poli, A., Spinsante, S.: Wearable devices as a valid support for diagnostic excellence: lessons from a pandemic going forward. Health Technol. 11(3), 673–675 (2021)

    Article  Google Scholar 

  11. Cosoli, G., Spinsante, S., Scalise, L.: Wrist-worn and chest-strap wearable devices: systematic review on accuracy and metrological characteristics. Measurement p. 107789 (2020), https://linkinghub.elsevier.com/retrieve/pii/S0263224120303274

  12. Cosoli, G., Spinsante, S., Scardulla, F., D’Acquisto, L., Scalise, L.: Wireless ECG and cardiac monitoring systems: State of the art, available commercial devices and useful electronic components. Measure. J. Int. Measure. Confed. 177, 109243 (2021)

    Google Scholar 

  13. Culić, A., Nižetić, S., Šolić, P., Perković, T., Čongradac, V.: Smart monitoring technologies for personal thermal comfort: a review. J. Cleaner Prod. 312, 127685 (2021)

    Google Scholar 

  14. Drummond, G.B., Fischer, D., Lees, M., Bates, A., Mann, J., Arvind, D.: Classifying signals from a wearable accelerometer device to measure respiratory rate. ERJ Open Res. 7(2) (2021). https://doi.org/10.1183/23120541.00681-2020

  15. Düking, P., Giessing, L., Frenkel, M.O., Koehler, K., Holmberg, H.C., Sperlich, B.: Wrist-worn wearables for monitoring heart rate and energy expenditure while sitting or performing light-to-vigorous physical activity: Validation study. JMIR Mhealth Uhealth 8(5), e16716 (2020)

    Article  Google Scholar 

  16. Haghi, M., Danyali, S., Ayasseh, S., Wang, J., Aazami, R., Deserno, T.M.: Wearable devices in health monitoring from the environmental towards multiple domains: A survey. Sensors 21(6) (2021). https://doi.org/10.3390/s21062130. Article Number 2130

  17. Hao, Y., Ma, X.K., Zhu, Z., Cao, Z.B.: Validity of wrist-wearable activity devices for estimating physical activity in adolescents: comparative study. JMIR Mhealth Uhealth 9(1), e18320 (2021)

    Article  Google Scholar 

  18. Hayashi, M., Yoshikawa, H., Uchiyama, A., Higashino, T.: Preliminary investigation on band tightness estimation of wrist-worn devices using inertial sensors. In: O’Hare, G.M.P., O’Grady, M.J., O’Donoghue, J., Henn, P. (eds.) MobiHealth 2019. LNICST, vol. 320, pp. 256–266. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49289-2_20

  19. Hinde, K., White, G., Armstrong, N.: Wearable devices suitable for monitoring twenty four hour heart rate variability in military populations. Sensors 21(4), 1061 (2021)

    Google Scholar 

  20. Iqbal, S.M., Mahgoub, I., Du, E., Leavitt, M.A., Asghar, W.: Advances in healthcare wearable devices. NPJ Flexible Electronics 5(1), 1–14 (2021)

    Google Scholar 

  21. Jin, N., Zhang, X., Hou, Z., Sanz-Prieto, I., Mohammed, B.S.: Iot based psychological and physical stress evaluation in sportsmen using heart rate variability. Aggression and Violent Behavior 101587 (2021)

    Google Scholar 

  22. Kwon, S., Kim, H., Yeo, W.H.: Recent advances in wearable sensors and portable electronics for sleep monitoring. iScience 24(5), 102461 (2021)

    Google Scholar 

  23. Leonidis, A., et al.: Improving stress management and sleep hygiene in intelligent homes. Sensors 21(7), 2398 (2021)

    Google Scholar 

  24. Mahloko, L., Adebesin, F.: A systematic literature review of the factors that influence the accuracy of consumer wearable health device data. In: Hattingh, M., Matthee, M., Smuts, H., Pappas, I., Dwivedi, Y.K., Mäntymäki, M. (eds.) I3E 2020. LNCS, vol. 12067, pp. 96–107. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45002-1_9

  25. Moraes, J.L., et al.: Advances in photopletysmography signal analysis for biomedical applications. Sensors 18(6), 1894 (2018)

    Google Scholar 

  26. Morresi, N., Casaccia, S., Sorcinelli, M., Arnesano, M., Uriarte, A., Torrens-Galdiz, J.I., Revel, G.M.: Sensing physiological and environmental quantities to measure human thermal comfort through machine learning techniques. IEEE Sens. J. 21(10), 12322–12337 (2021)

    Article  Google Scholar 

  27. Mühlen, J.M., et al.: Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE network. British J. Sports Med. 55(14), 767–779 (2021)

    Google Scholar 

  28. Poli, A., Cosoli, G., Scalise, L., Spinsante, S.: Impact of wearable measurement properties and data quality on ADLs classification accuracy. IEEE Sens J. 21(13), 14221–14231 (2021)

    Article  Google Scholar 

  29. Přibil, J., Přibilová, A., Frollo, I.: Comparative measurement of the ppg signal on different human body positions by sensors working in reflection and transmission modes. In: Engineering Proceedings vol. 2, no. 1, p. 69 (2020)

    Google Scholar 

  30. Regalia, G., Onorati, F., Lai, M., Caborni, C., Picard, R.W.: Multimodal wrist-worn devices for seizure detection and advancing research: focus on the empatica wristbands. Epilepsy Res. 153, 79–82 (2019)

    Google Scholar 

  31. Scalise, L., Cosoli, G.: Wearables for health and fitness: Measurement characteristics and accuracy. In: I2MTC 2018–2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings, pp. 1–6. Institute of Electrical and Electronics Engineers Inc. (2018).https://doi.org/10.1109/I2MTC.2018.8409635

  32. Scardulla, F., D’acquisto, L., Colombarini, R., Hu, S., Pasta, S., Bellavia, D.: A study on the effect of contact pressure during physical activity on photoplethysmographic heart rate measurements. Sensors (Switzerland) 20(18), 1–15 (2020)

    Article  Google Scholar 

  33. Stojanović, R., Škraba, A., Lutovac, B.: A headset like wearable device to track covid-19 symptoms. In: 2020 9th Mediterranean Conference on Embedded Computing (MECO), pp. 1–4 (2020). https://doi.org/10.1109/MECO49872.2020.9134211

  34. Tamura, T., Maeda, Y., Sekine, M., Yoshida, M.: Wearable photoplethysmographic sensors-past and present. Electronics 3(2), 282–302 (2014)

    Article  Google Scholar 

  35. Teixeira, E., et al.: Wearable devices for physical activity and healthcare monitoring in elderly people: a critical review. Geriatrics 6(2), 38 (2021)

    Google Scholar 

  36. Zhang, Y., et al.: Relationship between major depression symptom severity and sleep collected using a wristband wearable device: multicenter longitudinal observational study. JMIR mHealth and uHealth 9, e24604 (2021)

    Google Scholar 

  37. Zhao, J., Li, G.: Study on real-time wearable sport health device based on body sensor networks. Comput. Commun. 154, 40–47 (2020)

    Google Scholar 

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Correspondence to Gloria Cosoli .

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Poli, A. et al. (2022). DIY Wrist-Worn Device for Physiological Monitoring: Metrological Evaluation at Different Band Tightening Levels. In: Spinsante, S., Silva, B., Goleva, R. (eds) IoT Technologies for Health Care. HealthyIoT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 432. Springer, Cham. https://doi.org/10.1007/978-3-030-99197-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-99197-5_17

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