Abstract
In this paper, we propose a two-layered classification approach to effectively recognize the physical activities while the smartphone is placed at any four common positions on the body. Then we implement a Life Record app on smartphone that automatically classifies physical activities and records them as the personal life logs. For assisting users in comprehending their daily activities, the system also provides the visualization interface that shows the brief descriptions of their life logs.
We demonstrate that the system possesses less limitation to monitor daily activities that the users are not restricted to carry their smartphones in specific positions. Another major benefit of our system is to provide a complete overview of personal activities, which enhances the self-awareness of physical activity in our daily life through an intuitive visualization interface. Furthermore, analysis of life logs can also be applied in specific services or recommendation applications in the future.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
W. H. Organization, Preventing chronic diseases: a vital investment: WHO global report. World Health Organization, Geneva (2005)
Fitbit One. http://www.fitbit.com/one
Fitbit Flex. http://www.fitbit.com/flex
Bodymedia. http://www.bodymedia.com/
Jawbone UP. https://jawbone.com/up
Bicocchi, N., Mamei, M., Zambonelli, F.: Detecting activities from body-worn accelerometers via instance-based algorithms. Pervasive and Mobile Computing 6(4), 482–495 (2010)
Luo, Z.C.: The development of activity recognition system using the smartphone with accelerometer. Institute of Medical Informatics, National Cheng Kung University, Master (2010)
Wu, P., Peng, H.-K., Zhu, J., Zhang, Y.: SensCare: Semi-automatic Activity Summarization System for Elderly Care. In: Zhang, J.Y., Wilkiewicz, J., Nahapetian, A. (eds.) MobiCASE 2011. LNICST, vol. 95, pp. 1–19. Springer, Heidelberg (2012)
Zhao, Z., et al.: Cross-people mobile-phone based activity recognition. In: IJCAI, vol. 11 (2011)
Lee, Y.-S., Cho, S.-B.: Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part I. LNCS, vol. 6678, pp. 460–467. Springer, Heidelberg (2011)
Longstaff, B., Reddy, S., Estrin, D.: Improving activity classification for health applications on mobile devices using active and semi-supervised learning. In: 2010 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth). IEEE (2010)
Martin, E., et al.: Enhancing context awareness with activity recognition and radio fingerprinting. In: 2011 Fifth IEEE International Conference on Semantic Computing (ICSC). IEEE (2011)
Nickel, C., et al.: Using hidden markov models for accelerometer-based biometric gait recognition. In: 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (CSPA). IEEE (2011)
Mathie, M.J., et al.: Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement 25(2), R1 (2004)
Okada, S., et al.: Analysis of the correlation between the regularity of work behavior and stress indices based on longitudinal behavioral data. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction. ACM (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, PC., Su, SC., Wu, IL., Chiang, JH. (2015). Life Record: A Smartphone-Based Daily Activity Monitoring System. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-20472-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20471-0
Online ISBN: 978-3-319-20472-7
eBook Packages: Computer ScienceComputer Science (R0)