Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks
Open accessAbstract
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.
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[200] proposes a harmonic loss function and [207] combines LSTM with batch normalization to achieve 92% accuracy with raw accelerometer and gyroscope data.
“For example, [200] proposes a harmonic loss function and [207] combines LSTM with batch normalization to achieve 92% accuracy with raw accelerometer and gyroscope data.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [207]
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- Authors
- Zebin, T., Sperrin, M., Peek, N., & Casson, A. J.
- Affiliations
- University of Manchester (GB)
- Venue
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2018-07
- Volume
- 2018
- Pages
- 1-4
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2899372133
- Cited by
- 107
- References
- 17
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 8 · 2023: 14 · 2022: 24 · 2021: 21 · 2020: 15 · 2019: 8
- FWCI
- 3.32
- Type
- conference paper
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- WestminsterResearch (University of Westminster) (acceptedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Medical Research Council (MC_PC_13042)
- Engineering and Physical Sciences Research Council (EP/P010148/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- human activity recognition, inertial sensors, accelerometer, gyroscope, long short-term memory, recurrent neural networks, batch normalization, temporal correlation, wearable sensors, activities of daily living
- MeSH terms
- Machine Learning, Algorithms, Humans, Human Activities, Movement, Neural Networks, Computer
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