Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors
Open accessAbstract
This work presents the development and implementation of a unified multi-sensor human motion capture and gesture recognition system that can distinguish between and classify six different gestures. Data was collected from eleven participants using a subset of five wireless motion sensors (inertial measurement units) attached to their arms and upper body from a complete motion capture system. We compare Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios and evaluate the results. Our study indicates that near perfect classification accuracies are achievable for small gestures and that the speed of classification is sufficient to allow interactivity. However, such accuracies are more difficult to obtain when a participant does not participate in training, indicating that more work needs to be done in this area to create a system that can be used by the general population.
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Gestures as a jab, upper cut, throw, raise, block and sway were detected with greater than 85% accuracy using SVM and ANN.
“Gestures as a jab, upper cut, throw, raise, block and sway were detected with greater than 85% accuracy [184].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [184]
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Review of Wearable Devices and Data Collection Considerations for Connected Health
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Real-Time Gesture Recognition Using 3D Sensory Data and a Light Convolutional Neural Network
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- Authors
- Alavi, S., Arsenault, D., & Whitehead, A.
- Affiliations
- Carleton University (CA)
- Corresponding author
- Whitehead, Anthony
- ORCID
- Alavi, Shamir
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2016-04-28
- Volume
- 16
- Issue
- 5
- Pages
- 605
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC4883296
- OpenAlex
- W2342843801
- Cited by
- 47
- References
- 54
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 14 · 2019: 4 · 2018: 6 · 2017: 1 · 2016: 2
- FWCI
- 6.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Carleton University's Institutional Repository (MacOdrum Library, Carleton University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- gesture recognition, quaternions, wearable motion sensors, inertial measurement unit, human motion capture, wireless sensors, gesture classification, support vector machine, artificial neural networks, multi-sensor fusion
- MeSH terms
- Gestures, Humans, Motion, Neural Networks, Computer, Support Vector Machine
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