Imaging and fusing time series for wearable sensor-based human activity recognition
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Recently, many efforts have been put into improving the generalizability of models in HAR.
“Recently, many efforts have been put into improving the generalizability of models in HAR [86,302,303].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [302]
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“During recent years, many efforts have been put into improving the generalizability of models in HAR [3, 219, 262].”
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- Authors
- Qin, Z., Zhang, Y., Meng, S., Qin, Z., & Choo, K.-K. R.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- The University of Texas at San Antonio (US)
- Corresponding author
- Choo, Kim-Kwang Raymond
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 53
- Pages
- 80-87
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2954709787
- Cited by
- 309
- References
- 53
- Citations by year
- 2026: 18 · 2025: 42 · 2024: 58 · 2023: 48 · 2022: 43 · 2021: 42 · 2020: 28 · 2019: 2 · 2017: 2 · 2016: 2 · 2014: 3 · 2013: 1 · 2012: 1
- FWCI
- 12.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Fusion (publishedVersion)
- Funders
- National Natural Science Foundation of China-Yunnan Joint Fund (U1401257)
- National Natural Science Foundation of China (61672135)
- Fundamental Research Funds for the Central Universities (2672018ZYGX2018J057)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, wearable sensors, time-series imaging, sensor fusion, heterogeneous sensor data, accelerometer, gyroscope, magnetometer, mobile device sensors, MHEALTH dataset
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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