Abstract
Most mobile devices include motion, magnetic, acoustic, and location sensors. These sensors can be used in the development of a framework for activities of daily living (ADL) and environment recognition. This framework is composed of the acquisition, processing, fusion, and data classification features. This study compares different implementations of artificial neural networks. The obtained results were 85.89% and 100% for the recognition of standard ADL and standing activities with Deep Neural Networks, respectively. Furthermore, the results present 86.50% for identification of the environments using Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the incorporation of data fusion methods using mobile devices.
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Acknowledgements
This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020).
This article/publication is based on 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). More information in www.cost.eu.
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Pires, I.M. et al. (2021). Recognition of Activities of Daily Living Based on a Mobile Data Source Framework. In: Bhoi, A., Mallick, P., Liu, CM., Balas, V. (eds) Bio-inspired Neurocomputing. Studies in Computational Intelligence, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-15-5495-7_18
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DOI: https://doi.org/10.1007/978-981-15-5495-7_18
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