Activity recognition using a combination of high gain observer and deep learning computer vision algorithms Article Swipe
Ali Nouriani
,
Robert A. McGovern
,
Rajesh Rajamani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.iswa.2023.200213
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.iswa.2023.200213
Inertial sensors have become increasingly popular in human activity classification due to their ease of use and affordability. This paper proposes a novel algorithm for human activity recognition that is a combination of a high-gain observer and deep learning computer vision classification algorithms. The nonlinear high-gain observer designed using Lyapunov analysis accurately estimates the attitude of the chest of a human subject using measurements from a single Inertial Measurement Unit (IMU). The signals processed by the observer are then converted into spectrograms to obtain “images” of the frequency response of the signals. The images for activities from a dataset of 7 human subjects are annotated and used for training/ fine-tuning of several well-known deep learning algorithms for image processing. The results from the best combination of our algorithms shows an exceptional accuracy of 98% for activity recognition. Using deep learning computer vision algorithms, this paper shows how to perform transfer learning from networks pre-trained on millions of images, thus showing how we can train a powerful deep learning network for activity recognition even with just small datasets. The algorithm that uses the high gain observer is shown to perform significantly better than an algorithm based on raw accelerometer and gyro signals.
Related Topics
Concepts
Artificial intelligence
Computer science
Accelerometer
Deep learning
Activity recognition
Observer (physics)
Inertial measurement unit
Algorithm
Spectrogram
Artificial neural network
Computer vision
Transfer of learning
Pattern recognition (psychology)
Machine learning
Operating system
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.iswa.2023.200213
- OA Status
- gold
- Cited By
- 7
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4324031214
All OpenAlex metadata
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https://openalex.org/W4324031214Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.iswa.2023.200213Digital Object Identifier
- Title
-
Activity recognition using a combination of high gain observer and deep learning computer vision algorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-03-13Full publication date if available
- Authors
-
Ali Nouriani, Robert A. McGovern, Rajesh RajamaniList of authors in order
- Landing page
-
https://doi.org/10.1016/j.iswa.2023.200213Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.iswa.2023.200213Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Accelerometer, Deep learning, Activity recognition, Observer (physics), Inertial measurement unit, Algorithm, Spectrogram, Artificial neural network, Computer vision, Transfer of learning, Pattern recognition (psychology), Machine learning, Operating system, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Elsevier BV |
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| primary_location.raw_source_name | Intelligent Systems with Applications |
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| publication_date | 2023-03-13 |
| publication_year | 2023 |
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