Learning Deep and Shallow Features for Human Activity Recognition
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CNN-extracted features are excellent compared to hand-crafted time and frequency domain features (FFT and DCT), as shown in [162].
“This allows CNN to learn excellent features as shown in [162], which compares the features extracted from CNN to hand-crafted time and frequency domain features (Fast Fourier Transform and Discrete Cosine Transform).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [162]
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“This allows CNN to learn excellent features as shown in [242], which compares the features extracted from CNN to hand-crafted time and frequency domain features (Fast Fourier Transform and Discrete Cosine Transform).”
MethodA survey on wearable sensor modality centred human activity recognition in health care
Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409 · see citing works
- Authors
- Sani, S., Massie, S., Wiratunga, N., & Cooper, K.
- Affiliations
- Robert Gordon University (GB)
- Corresponding author
- Massie, Stewart
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 469-482
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2737046835
- Cited by
- 45
- References
- 21
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 6 · 2022: 9 · 2021: 5 · 2020: 6 · 2019: 6 · 2018: 5
- FWCI
- 1.36
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Lecture notes in computer science (publishedVersion, public-domain)
- Open Access Institutional Repository at Robert Gordon University (Robert Gordon University) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- human activity recognition, wearable sensors, accelerometer, wrist-worn sensors, feature representation, convolutional neural networks, handcrafted features, frequency-domain features, low back pain, self-management, physical activity adherence
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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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