Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor
Open accessAbstract
The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics.
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The system, which comprised of VRH and integrated IMUs, achieved 91% accuracy.
“The system, which comprised of VRH and integrated IMUs, achieved 91% accuracy [181].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [181]
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Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
- Authors
- Hachaj, T., & Piekarczyk, M.
- Affiliations
- Uniwersytet Komisji Edukacji Narodowej w Krakowie (PL)
- Corresponding author
- Hachaj, Tomasz
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-12-08
- Volume
- 19
- Issue
- 24
- Pages
- 5408
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6960875
- OpenAlex
- W2991765348
- Cited by
- 30
- References
- 60
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 7 · 2022: 2 · 2021: 7 · 2020: 5
- FWCI
- 3.31
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Narodowe Centrum Nauki (2015/17/D/ST6/04051)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- 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
- Keywords
- head gesture recognition, inertial measurement unit, motion capture, dynamic time warping, bagging, principal component analysis, artificial neural networks, random forest, assistive technology, wheelchair control
- MeSH terms
- Virtual Reality, Algorithms, Head, Humans, Motion, Pattern Recognition, Automated, Principal Component Analysis, Accelerometry
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