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

Tahmina Zebin, Matthew Sperrin, Niels Peek, Alexander J. Casson

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)
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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.

Original languageEnglish
Pages (from-to)1-4
Number of pages4
Journal35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publication statusPublished - Jul 2018


  • Physical Activity Recognition
  • Deep learning

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