Abstract
Given the impact of the pandemic era, it is important the effects of physical activity on human beings, physically and mentally. The significant advance in the technology industry of biomedical sensors and mobile devices allowed the arrival of new health monitoring prototypes to improve people’s lives. This work implements a data capture system, using an electrocardiogram (ECG) and accelerometer (ACC) type sensor to collect a large volume of data for further analysis to obtain metrics to assess the activity level during this pandemic phase. Using a BITalino device that allows us to collect a large amount of information from various sensors, we, therefore, chose to use it as a platform to capture data from the sensors mentioned above. In the first phase, we will capture the largest possible amount of data from the subject in the test phase. Then, the collected data will be sent to a web server, where it will be processed. Finally, in a third phase, the data will be presented in a more summarized and graphical way. In this way, we will analyze the impact of movement/inactivity on the test subjects’ daily life with the referred sensors’ biometric data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, N., Zhu, Y.: Early detection of atrial fibrillation based on ECG signals. Bioengineering 7(1) (2020)
Alexandre, C.M.A.: Recolha e análise de bio-sinais em ambientes abertos. Master’s thesis, Master’s dissertation - Universidade da Beira Interior (2018)
Biosignals, P.W.: Bitalino, December 2020. https://bitalino.com/
da Silva, H.P., Fred, A., Martins, R.: Biosignals for everyone. IEEE Pervasive Comput. 13(4), 64–71 (2014)
de Miranda Tavares, P.M.: Development of a smart sensor node based on BITalino. Master’s thesis, Master’s dissertation - University of Oporto (2016)
Denning, T., Borning, A., Friedman, B., Gill, B.T., Kohno, T., Maisel, W.H.: Patients, pacemakers, and implantable defibrillators: human values and security for wireless implantable medical devices. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2010, pp. 917–926. Association for Computing Machinery, New York (2010)
Dombrowski, Q., Gniady, T., Kloster, D.: Introduction to Jupyter Notebooks. The Programming Historian (2019)
Eng, A., Wahsheh, L.A.: Look into my eyes: a survey of biometric security. In: 2013 10th International Conference on Information Technology: New Generations, pp. 422–427 (2013)
Guerreiro, J., Martins, R., Plácido da Silva, H., Lourenco, A., Fred, A.: BITalino: a multimodal platform for physiological computing. In: Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2013, vol. 1, July 2013
Hamidi, H.: An approach to develop the smart health using internet of things and authentication based on biometric technology. Futur. Gener. Comput. Syst. 91, 434–449 (2019)
Kumar, S., Singh, S.K.: Monitoring of pet animal in smart cities using animal biometrics. Futur. Gener. Comput. Syst. 83, 553–563 (2018)
Matias, I., et al.: Prediction of atrial fibrillation using artificial intelligence on electrocardiograms: a systematic review. Comput. Sci. Rev. 39, 100334 (2021)
Pires, I., Marques, G., Garcia, N., Flórez-Revuelta, F., Ponciano, V., Oniani, S.: A research on the classification and applicability of the mobile health applications. J. Personalized Med. 10, 11 (2020)
Plácido da Silva, H., Guerreiro, J., Lourenco, A., Fred, A., Martins, R.: BITalino: a novel hardware framework for physiological computing. In: Proceedings of the International Conference on Physiological Computing Systems, PhyCS 2014, January 2014
Ponciano, V., Pires, I.M., Ribeiro, F.R., Garcia, N.M.: Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors’ data. Data Brief 32, 106306 (2020)
Acknowledgements
This work is funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020. (Este trabalho é financiado pela FCT/MEC através de fundos nacionais e cofinanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 no âmbito do projeto UIDB/50008/2020). This work is also funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the projects UIDB/00742/2020 and UIDB/05583/2020. This article is based upon work from COST Action IC1303-AAPELE-Architectures, Algorithms, and Protocols for Enhanced Living Environments and COST Action CA16226-SHELD-ON-Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. It boosts their research, career, and innovation. More information in www.cost.eu. Furthermore, we would like to thank the Research Center in Digital Services (CISeD) and the Polytechnic of Viseu for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Marques, L. et al. (2021). Biometric Data Capture as a Way to Identify Lack of Physical Activity in Daily Life. In: Pires, I.M., Spinsante, S., Zdravevski, E., Lameski, P. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-030-91421-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-91421-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91420-2
Online ISBN: 978-3-030-91421-9
eBook Packages: Computer ScienceComputer Science (R0)