User-Independent Hand Gesture Recognition Classification Models Using Sensor Fusion Article Swipe
J. Guillermo Colli Alfaro
,
Ana Luisa Trejos
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22041321
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22041321
Recently, it has been proven that targeting motor impairments as early as possible while using wearable mechatronic devices for assisted therapy can improve rehabilitation outcomes. However, despite the advanced progress on control methods for wearable mechatronic devices, the need for a more natural interface that allows for better control remains. To address this issue, electromyography (EMG)-based gesture recognition systems have been studied as a potential solution for human–machine interface applications. Recent studies have focused on developing user-independent gesture recognition interfaces to reduce calibration times for new users. Unfortunately, given the stochastic nature of EMG signals, the performance of these interfaces is negatively impacted. To address this issue, this work presents a user-independent gesture classification method based on a sensor fusion technique that combines EMG data and inertial measurement unit (IMU) data. The Myo Armband was used to measure muscle activity and motion data from healthy subjects. Participants were asked to perform seven types of gestures in four different arm positions while using the Myo on their dominant limb. Data obtained from 22 participants were used to classify the gestures using three different classification methods. Overall, average classification accuracies in the range of 67.5–84.6% were obtained, with the Adaptive Least-Squares Support Vector Machine model obtaining accuracies as high as 92.9%. These results suggest that by using the proposed sensor fusion approach, it is possible to achieve a more natural interface that allows better control of wearable mechatronic devices during robot assisted therapies.
Related Topics
Concepts
Gesture
Wearable computer
Inertial measurement unit
Gesture recognition
Computer science
Interface (matter)
Artificial intelligence
Sensor fusion
Support vector machine
Mechatronics
Exoskeleton
Human–computer interaction
Speech recognition
Machine learning
Pattern recognition (psychology)
Simulation
Embedded system
Parallel computing
Maximum bubble pressure method
Bubble
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22041321
- https://www.mdpi.com/1424-8220/22/4/1321/pdf?version=1644811215
- OA Status
- gold
- Cited By
- 58
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210963337
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4210963337Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22041321Digital Object Identifier
- Title
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User-Independent Hand Gesture Recognition Classification Models Using Sensor FusionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-09Full publication date if available
- Authors
-
J. Guillermo Colli Alfaro, Ana Luisa TrejosList of authors in order
- Landing page
-
https://doi.org/10.3390/s22041321Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/4/1321/pdf?version=1644811215Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/22/4/1321/pdf?version=1644811215Direct OA link when available
- Concepts
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Gesture, Wearable computer, Inertial measurement unit, Gesture recognition, Computer science, Interface (matter), Artificial intelligence, Sensor fusion, Support vector machine, Mechatronics, Exoskeleton, Human–computer interaction, Speech recognition, Machine learning, Pattern recognition (psychology), Simulation, Embedded system, Parallel computing, Maximum bubble pressure method, BubbleTop concepts (fields/topics) attached by OpenAlex
- Cited by
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58Total citation count in OpenAlex
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2025: 22, 2024: 14, 2023: 15, 2022: 7Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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