Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks

Zebin, Tahmina, Sperrin, Matthew, Peek, Niels and Casson, Alexander J (2018) Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018. pp. 1-4. ISSN 1557-170X

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Abstract

In recent years machine learning methods for human activity recognition have been found very effective. These classify discriminative features generated from raw input sequences acquired from body-worn inertial sensors. However, it involves an explicit feature extraction stage from the raw data, and although human movements are encoded in a sequence of successive samples in time most state-of-the-art machine learning methods do not exploit the temporal correlations between input data samples. In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network for the classification of six daily life activities from accelerometer and gyroscope data. Results show that our LSTM can processes featureless raw input signals, and achieves 92 % average accuracy in a multi-class-scenario. Further, we show that this accuracy can be achieved with almost four times fewer training epochs by using a batch normalization approach.

Item Type: Article
Uncontrolled Keywords: physical activity recognition,deep learning
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 28 May 2019 13:30
Last Modified: 22 Apr 2020 07:53
URI: https://ueaeprints.uea.ac.uk/id/eprint/71149
DOI: 10.1109/EMBC.2018.8513115

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