Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network
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Date
2018
Authors
Eyobu, Odongo Steven
Seog Han, Dong
Journal Title
Journal ISSN
Volume Title
Publisher
Sensors
Abstract
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition
of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from
human motion are categorically combined to formulate datasets that can be used for learning human
activities. However, successful learning of human activities from motion data involves the design
and use of proper feature representations of IMU sensor data and suitable classifiers. Furthermore,
the scarcity of labelled data is an impeding factor in the process of understanding the performance
capabilities of data-driven learning models. To tackle these challenges, two primary contributions are
in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach
is proposed. Second, an ensemble of data augmentations in feature space is proposed to take care
of the data scarcity problem. Performance tests were conducted on a deep long term short term
memory (LSTM) neural network architecture to explore the influence of feature representations
and the augmentations on activity recognition accuracy. The proposed feature extraction approach
combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR.
A performance evaluation of each augmentation approach is performed to show the influence on
classification accuracy. Finally, in addition to using our own dataset, the proposed data augmentation
technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset
and yields state-of-the-art accuracy results at various learning rates.
Description
Keywords
Human activity recognition, Data augmentation, Feature representation, Deep learning, Long short term memory, Inertial measurement unit sensor
Citation
Steven Eyobu, O., & Han, D. S. (2018). Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network. Sensors, 18(9), 2892. doi:10.3390/s18092892