Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition

Zebin, Tahmina ORCID: https://orcid.org/0000-0003-0437-0570, Balaban, Ertan, Ozanyan, Krikor B., Casson, Alexander J. and Peek, Niels (2019) Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition. In: 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings . The Institute of Electrical and Electronics Engineers (IEEE), USA. ISBN 9781728108483

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Abstract

In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network (RNN) model for the classification of human daily life activities by using the accelerometer and gyroscope data of a smartphone. The proposed model was trained by using the open-source TensorFlow library, optimised and deployed on an Android smartphone. Hardware resource requirements for the implementation are empirically investigated and the effect of data quantization on the accuracy of the implementation is discussed. In addition, we profile the power budget for running the proposed model on smartphone. Results of this work will be of use for deep learning implemented on edge computing devices, which leverages the user privacy as the raw data never leaves the person.

Item Type:	Book Section
Additional Information:	Funding Information: This work was supported by the UK Engineering and Physical Sciences Research Council grant number EP/P010148/1 and EP/P02713X/1. Publisher Copyright: © 2019 IEEE.
Uncontrolled Keywords:	artificial intelligence,signal processing,information systems and management,biomedical engineering,health informatics,radiology nuclear medicine and imaging ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School:	Faculty of Science > School of Computing Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	03 Nov 2022 12:31
Last Modified:	06 May 2024 00:06
URI:	https://ueaeprints.uea.ac.uk/id/eprint/89565
DOI:	10.1109/BHI.2019.8834480

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