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

Tahmina Zebin, Ertan Balaban, Krikor Ozanyan, Alex Casson, Niels Peek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publicationIEEE-EMBS BHI 2019
DOIs
Publication statusPublished - 12 Sept 2019
EventIEEE-EMBS International Conference on Biomedical and Health Informatics - Chicago, United States
Duration: 19 May 201922 May 2019

Conference

ConferenceIEEE-EMBS International Conference on Biomedical and Health Informatics
Abbreviated titleIEEE-EMBS BHI 2019
Country/TerritoryUnited States
CityChicago
Period19/05/1922/05/19

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