Fusion of Wearable and Contactless Sensors for Intelligent Gesture Recognition
Open accessAbstract
A novel approach of fusing datasets from multiple sensors using a hierarchical support vector machine (HSVM) algorithm is presented. The validation of this method is experimentally carried out using an intelligent learning system that combines two different data sources. The sensors are based on a contactless sensor, which is a radar that detects the movements of the hands and fingers, as well as a wearable sensor, which is a flexible pressure sensor array that measures pressure distribution around the wrist. A HSVM architecture is developed to effectively fuse different data types in terms of sampling rate, data format, and gesture information from the pressure sensors and radar. In this respect, the proposed method is compared with the classification results from each of the two sensors independently. Herein, datasets from 15 different participants are collected and analyzed. The results show that the radar on its own provides a mean classification accuracy of 76.7%, whereas the pressure sensors provide an accuracy of 69.0%. However, enhancing the pressure sensors' output results with radar using the proposed HSVM algorithm improves the classification accuracy to 92.5%.
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A novel method of data fusion from multiple sensors using a hierarchical SVM algorithm is presented.
“Additionally, a novel method of data fusion from multiple sensors using a hierarchical SVM (HSVM) algorithm is presented in Fig. 10b (Liang et al. 2019).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Liang et al. 2019
Upon analyzing datasets from 15 participants, standalone radar technology resulted in an average classification accuracy of 76.7%, while pressure sensors alone clocked an accuracy of 69.0%.
“Upon analyzing the datasets collected from a wide range of 15 participants, it was observed that standalone radar technology resulted in a decent average classification accuracy of 76.7%, while the pressure sensors alone clocked an accuracy of 69.0%.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Liang et al. 2019
The HSVM algorithm combines outputs from both pressure sensors and radar, pushing classification accuracy to 92.5%.
“However, a significant improvement was noticed when the HSVM algorithm was implemented, as it combines the outputs from both the pressure sensors and radar, pushing the classification accuracy rate to a commendable 92.5%.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Liang et al. 2019
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- Authors
- Liang, X., Li, H., Wang, W., Liu, Y., Ghannam, R., Fioranelli, F., & Heidari, H.
- Affiliations
- University of Glasgow (GB)
- Corresponding author
- Heidari, Hadi
- ORCID
- Venue
- Advanced Intelligent Systems
- Publisher
- Wiley
- Published
- 2019-09-30
- Volume
- 1
- Issue
- 7
- ISSN
- 2640-4567 (ISSN-L)
- OpenAlex
- W2969468083
- Cited by
- 64
- References
- 24
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 9 · 2023: 12 · 2022: 7 · 2021: 8 · 2020: 14 · 2019: 1
- FWCI
- 7.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advanced Intelligent Systems (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Funders
- Royal Society (RSG\R1\180269)
- Engineering and Physical Sciences Research Council (EP/R511705/1, EP/R041679/1)
- 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 › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- gesture recognition, sensor fusion, wearable sensors, pressure sensor array, hierarchical support vector machine
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