Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

dc.contributor.authorEyobu, Odongo Steven
dc.contributor.authorSeog Han, Dong
dc.date.accessioned2023-02-03T16:21:33Z
dc.date.available2023-02-03T16:21:33Z
dc.date.issued2018
dc.description.abstractWearable 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.en_US
dc.identifier.citationSteven 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/s18092892en_US
dc.identifier.other10.3390/s18092892
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7517
dc.language.isoenen_US
dc.publisherSensorsen_US
dc.subjectHuman activity recognitionen_US
dc.subjectData augmentationen_US
dc.subjectFeature representationen_US
dc.subjectDeep learningen_US
dc.subjectLong short term memoryen_US
dc.subjectInertial measurement unit sensoren_US
dc.titleFeature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Networken_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Feature Representation and Data Augmentation for.pdf
Size:
3.68 MB
Format:
Adobe Portable Document Format
Description:
Article
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: