A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone
Abstract
Activity recognition plays an essential role in bridging the gap between the low-level sensor data and the high-level applications in ambient-assisted living systems. With the aim to obtain satisfactory recognition rate and adapt to various application scenarios, a variety of sensors have been exploited, among which, smartphone-embedded inertial sensors are widely applied due to its convenience, low cost, and intrusiveness. In this paper, we explore the power of triaxial accelerometer and gyroscope built-in a smartphone in recognizing human physical activities in situations, where they are used simultaneously or separately. A novel feature selection approach is then proposed in order to select a subset of discriminant features, construct an online activity recognizer with better generalization ability, and reduce the smartphone power consumption. Experimental results on a publicly available data set show that the fusion of both accelerometer and gyroscope data contributes to obtain better recognition performance than that of using single source data, and that the proposed feature selector outperforms three other comparative approaches in terms of four performance measures. In addition, great improvement in time performance can be achieved with an effective feature selector, indicating the way of power saving and its applicability to real-world activity recognition.
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Existing HAR solutions often rely on fully supervised techniques.
“Existing HAR solutions often rely on fully supervised techniques [15, 25].”
From Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion (2021) · cites this paper as [25]
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- Authors
- Wang, A., Chen, G., Yang, J., Zhao, S., & Chang, C.-Y.
- Affiliations
- Hefei University of Technology (CN)
- Chuzhou University (CN)
- Tamkang University (TW)
- Venue
- IEEE Sensors Journal
- Published
- 2016-06
- Volume
- 16
- Issue
- 11
- Pages
- 4566-4578
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2316564986
- Semantic Scholar
- 833ee85d79ddb6fa68a637efa91c654c5829d85c
- Cited by
- 324
- References
- 46
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 26 · 2023: 36 · 2022: 55 · 2021: 42 · 2020: 51 · 2019: 35 · 2018: 28 · 2017: 27 · 2016: 3
- FWCI
- 21.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Sensors Journal (publishedVersion)
- Tamkang University Institutional Repository (TKUIR) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61305064, 61472057)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- human activity recognition, smartphone inertial sensors, accelerometer, gyroscope, sensor fusion, feature selection, ambient assisted living, smartphone power consumption, wearable sensors
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