Abstract
This article aims to develop a minimally intrusive system of care and monitoring. Furthermore, the goal is to get a cheap, comfortable and, especially, efficient system which controls the physical activity carried out by the user. All this, is based on the data of accelerometry analysis which are obtained through a mobile phone.
Besides this, we will develop a comprehensive system for consulting the activity obtained in order to provide families and care staff an interface through which to observe the condition of the individual subject to monitoring.
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References
Paiyarom, S., et al.: Activity Monitoring System using Dynamic Time Warping for the Elderly and Disabled people. In: 2nd International Conference on Computer, Control and Communication (February 2009), ISBN: 978-1-60558-792-9
Ravi, N., et al.: Activity Recognition from Accelerometer Data. In: American Association for Artificial Intelligence (2005), ISBN: 1-57735-236-x
Hong, Y.-J., et al.: Activity Recognition using Wearable Sensors for Elder Care. In: IEEE Future Generation Communication and Networking (2008), ISBN: 978-0-7695-3431-2
Yang, J.-Y., Wang, J.-S., Chen, Y.-P.: Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers. In: Pattern Recognition Letters (August 2008), ISSN: 0167-8655
Brezmes, T., Gorricho, J.-L., Cotrina, J.: Activity Recognition from Accelerometer Data on a Mobile Phone, June 2009. LNCS. Springer, Heidelberg (2009), ISBN: 978-3-642-02480-1
Hyun, J., et al.: Estimation of Activity Energy Expenditure: Accelerometer Approach. In: IEEE: Engineering in Medicine and Biology 27th Annual Conference (September 2005)
Cho, Y., et al.: SmartBuckle: Human Activity Recognition using a 3-axis Accelerometer and a Wearable Camera. In: HealthNet 2008: Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, pp. 1–3 (2008), ISBN: 978-1-60558-199-6
Cho, I.-Y., et al.: Development ot a Single 3-Axis Accelerometer Sensor Based Wearable Gesture Recognition Band. LNCS. Springer, Berlin (ISBN: 978-3-540-73548-9)
Olsen, G., Brilliant, S., Primeaux, D., Najarian, K.: Signal processing and machine learning for real-time classification of ergonomic posture with unobtrusive on-body sensors. In: ICME International Conference on Complex Medical Engineering (CME 2009), April 9-11, pp. 1–11 (2009)
Joo Hyun Hong, N.J.K., Cha, E.J., Lee, T.S.: Classification Technique of Human Motion Context based on Wireless Sensor Network. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2005)
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Soria-Morillo, L.M., Álvarez-García, J.A., Ortega, J.A., González-Abril, L. (2010). Tracking System Based on Accelerometry for Users with Restricted Physical Activity. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_49
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DOI: https://doi.org/10.1007/978-3-642-13025-0_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13024-3
Online ISBN: 978-3-642-13025-0
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