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

Zebin, T., Sperrin, M., Peek, N. and Casson, A.J. 2018. Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, HI, USA 18 - 21 Jul 2018 IEEE . doi:10.1109/EMBC.2018.8513115

TitleHuman activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks
AuthorsZebin, T., Sperrin, M., Peek, N. and Casson, A.J.
TypeConference paper
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.

KeywordsDeep Learning, Activity Recognition
Year2018
Conference 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
File2018_embc_tahmina_lstm.pdf
Publication dates
Published29 Oct 2018
Journal citationpp. 4385-4388
ISSN1558-4615
1557-170X
ISBN9781538636466
9781538636459
9781538636473
Digital Object Identifier (DOI)doi:10.1109/EMBC.2018.8513115
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/abstract/document/8513115
Web address (URL)https://ieeexplore.ieee.org/abstract/document/8513115

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