Development of the Structure of an Information System for Monitoring Human Physical Activity Data
https://doi.org/10.21869/2223-1560-2021-25-4-122-133
Abstract
Purpose of research is to design an information system taking into account the dveloped requirements and criteria, based on the use of new methods and modules for processing data from smartphone sensors, designed to monitor physical activity and risk factors for human health.
Methods. The work of the system for monitoring data on human physical activity is based on methods for recognizing simple and complex types of human physical activity using sensors of wearable devices, personalized analysis of risk factors and their impact on health, characterized by constant monitoring of the types of physical activity performed and the space surrounding human, on the use of technology that allows combination of the activity performed with elements of the game, as well as smart reminders to a person about the need to pay attention to their health, which are formed taking into account the physical condition of a person and the surrounding risk factors for his health.
Results. The structure of the proposed system is provided and the functionality of the software is described.
Conclusion. The designed system will help to develop an information system for monitoring data on human physical activity.
About the Authors
E. S. Abramova
Vladimir State University named after Alexader Grigoryevich and Nickolay Grigoryevich Stoletovs
Russian Federation
Russian Federation
Elena S. Abramova, Post-Graduate Student, Information Systems and Software Engineering Department
87 Gorkogo str., Vladimir 600000
A. A. Orlov
Murom Institute (branch) Vladimir State University named after Alexader Grigoryevich and Nickolay Grigoryevich Stoletovs
Russian Federation
Russian Federation
Alexey A. Orlov, Dr. of Sci. (Engineering), Head of Physics and Applied Mathematics Department
23 Orlovkaya str., Murom 602264
K. V. Makarov
Murom Institute (branch) Vladimir State University named after Alexader Grigoryevich and Nickolay Grigoryevich Stoletovs
Russian Federation
Russian Federation
Kirill V. Makarov, Cand. of Sci. (Engineering), Associate Professor of Physics and Applied Mathematics Department
23 Orlovkaya str., Murom 602264
References
1. Zanyatiya fizicheskoj kul'turoj i sportom i zabolevaemost' (v %) [Physical culture and sports and morbidity (in %)]. Federal'naya sluzhba gosudarstvennoj statistiki [Federal State Statistics Service]. Available at: www.rosstat.gov.ru (accessed: 01.09.2021).
2. Godwin Ogbuabor and Robert La. 2018. Human Activity Recognition for Healthcare using Smartphones. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing (ICMLC 2018). ACM, New York, NY, USA, 41-46.
3. Jordao, Artur & Antonio C. Nazare, Jr &Sena de Souza, Jessica & Schwartz, William. Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art. 2018.
4. Hasan S.S. et al. Human Activity Recognition using Smartphone Sensors with Context Filtering. ACHI 2016: The Ninth International Conference on Advances in ComputerHuman Interactions, 2016, vol. 571–572, pp. 1019–1029.
5. Attila Reiss. Personalized mobile physical activity monitoring for everyday life (Doctoral Thesis, Technical University of Kaiserslautern). 2014.
6. Jorge Luis Reyes Ortiz. Smartphone-based human activity recognition (Doctoral Thesis, Universitat Politècnica de Catalunya). 2015.
7. He Y., Li Y. Physical activity recognition utilizing the built-in Kinematic sensors of a smartphone. International Journal of Distributed Sensor Networks. SAGE Publications Sage UK: London, England, 2013, vol. 2013, no. 4, pp. 481-580.
8. Miao F. et al. Identifying typical physical activity on smartphone with varying positions and orientations. Biomed. Eng. Online. BioMed Central, 2015, vol. 14, no. 1, pp. 32-46.
9. Fitbit SmartTrack. Fitbit (accessed: 01.09.2021).
10. Mi Fit. Google Play Market (accessed: 01.09.2021).
11. Urban fit. Google Play Market (accessed: 01.09.2021).
12. Liangying Peng, Ling Chen, Zhenan Ye, and Yi Zhang. 2018. AROMA: A Deep Multi-Task Learning based Simple and Complex Human Activity Recognition Method Using Wearable Sensors. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 2, 2, Article 74 (June 2018), 16 pages.
13. Li Liu, Yuxin Peng, Ming Liu, Zigang Huang, Sensor-based human activity recognition system with a multilayered model using time series shapelets. Knowledge-Based Systems, 2015, vol. 90. pp. 138-152.
14. Sakr N.A., Abu-Elkheir M., Atwan A., Soliman H.H. Current trends in complex human activity recognition. Journal of Theoretical and Applied Information Technology, 2018, 96, pp. 4564-4583.
15. Higgins J.P. Smartphone applications for patients’ health and fitness. Am J Med. 2016, 129(1):11–9.
16. Buller D.B., Borland R., Bettinghaus E.P. et al. Randomized trial of a smartphone mobile application compared to text messaging to support smoking cessation. Telemed J E Health. 2014; 20(3): 206–14.
17. Vohralik S.L., Bowen A.R., Burns J. et al. Reliability and validity of a smartphone app to measure joint range. Am J Phys Med Rehabil. 2015; 94(4): 325–30.
18. Usanov D.A., Protopopov A.A., Skripal' A.V. i dr. Pokazateli serdechno-sosudistoj sistemy pri fizicheskih nagruzkah u sportsmenov [Indicators of the cardiovascular system during physical exertion in athlete]. Saratovskij nauchno-medicinskij zhurnal = Saratov Scientific and Medical Journal. 2013;9(1):64–8.
19. Rosney D.M., Horvath P.J. Exergaming Intervention in Sedentary Middle-Aged Adults Improves Cardiovascular Endurance, Balance and Lower Extremity Functional Fitness. Heal. Sci J, 2018, vol. 12, № 6. P. 601.
20. Ymohan T., Binti AIahad N. A Model for Mobile Exergame Development to Promote Anti-Sedentary Behaviour. J. Inf. Syst. Res. Innov, 2017, vol. 11, no. 3, pp. 1–6.
Review
For citations:
Abramova E.S., Orlov A.A., Makarov K.V. Development of the Structure of an Information System for Monitoring Human Physical Activity Data. Proceedings of the Southwest State University. 2021;25(4):122-133. (In Russ.) https://doi.org/10.21869/2223-1560-2021-25-4-122-133
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