MFE-HAR: Multiscale feature engineering for human activity recognition using wearable sensors
- Publisher:
- ACM
- Publication Type:
- Conference Proceeding
- Citation:
- ACM International Conference Proceeding Series, 2019, pp. 180-189
- Issue Date:
- 2019-11-12
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 3360774.3360787.pdf | Published version | 1.33 MB |
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© 2019 Association for Computing Machinery. Human activity recognition plays a key role in the application areas such as fitness tracking, healthcare and aged care support. However, inaccurate recognition results may cause an adverse effect on users or even an unpredictable accident. In order to improve the accuracy of human activity recognition, multi-device and deep learning based approaches have been proposed. However, they are not practical on a daily basis due to the limitations that devices are difficult to wear, and deep learning requires large training dataset and incurs expensive computational costs. To address this problem, we propose a novel approach, multiscale feature engineering for human activity recognition (MFE-HAR), which exploits the properties of arm movement from global and local scales using the accelerometer and gyroscope sensors on a single wearable device. Our method takes advantage of having important features at multiple scales over previous single-scale methods. We evaluated the performance of the proposed method on two public datasets and achieved the mean classification accuracy of 93% and 98% respectively. Our proposed system performs better than the state of the art multi-device based approaches, and is more practical for real-world applications.
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