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The Passive Operating Mode of the Linear Optical Gesture SensorCZUSZYNSKI, K. , RUMINSKI, J. , WTOREK, J. |
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Author keywords
gesture recognition, human computer interaction, photodiodes, interactive system, wearable sensors
References keywords
gesture(26), sensor(15), recognition(12), sensors(6), ruminski(6), interaction(6), devices(6), czuszynski(6), comput(6), wtorek(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 145 - 156
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01018
Web of Science Accession Number: 000426449500018
SCOPUS ID: 85043282873
Abstract
The study evaluates the influence of natural light conditions on the effectiveness of the linear optical gesture sensor, working in the presence of ambient light only (passive mode). The orientations of the device in reference to the light source were modified in order to verify the sensitivity of the sensor. A criterion for the differentiation between two states - possible gesture and no gesture - was proposed. Additionally, different light conditions and possible features were investigated, relevant for the decision of switching between the passive and active modes of the device. The criterion was evaluated based on the specificity and sensitivity analysis of the binary ambient light condition classifier. The elaborated classifier predicts ambient light conditions with the accuracy of 85.15%. Understanding the light conditions, the hand pose can be detected. The achieved accuracy of the hand poses classifier trained on the data obtained in the passive mode in favorable light conditions was 98.76%. It was also shown that the passive operating mode of the linear gesture sensor reduces the total energy consumption by 93.34%, resulting in 0.132mA. It was concluded that optical linear sensor could be efficiently used in various lighting conditions. |
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[1] Gesture Recognition With the Linear Optical Sensor and Recurrent Neural Networks, Czuszynski, Krzysztof, Ruminski, Jacek, Kwasniewska, Alicja, IEEE Sensors Journal, ISSN 1530-437X, Issue 13, Volume 18, 2018.
Digital Object Identifier: 10.1109/JSEN.2018.2834968 [CrossRef]
[2] The intelligent vehicle target recognition algorithm based on target infrared features combined with lidar, Zhang, Wanyi, Fu, Xiuhua, Li, Wei, Computer Communications, ISSN 0140-3664, Issue , 2020.
Digital Object Identifier: 10.1016/j.comcom.2020.03.013 [CrossRef]
[3] A novel crosstalk-free piezoresistive/self-capacitive bimodal sensor for human-robot collaboration, Liu, Zhe, Chen, Diansheng, Ma, Junlin, Wang, Xinchi, Wang, Tingyu, Jia, Daiyu, Liu, Yutong, Sensors and Actuators A: Physical, ISSN 0924-4247, Issue , 2024.
Digital Object Identifier: 10.1016/j.sna.2023.114910 [CrossRef]
[4] Towards Contactless, Hand Gestures-Based Control of Devices, Czuszynski, Krzysztof, Ruminski, Jacek, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, ISBN 978-1-5090-6684-1, 2018.
Digital Object Identifier: 10.1109/IECON.2018.8591446 [CrossRef]
[5] Digits Recognition with Quadrant Photodiode and Convolutional Neural Network, Janczyk, Kamil, Czuszynski, Krzysztof, Ruminski, Jacek, 2018 11th International Conference on Human System Interaction (HSI), ISBN 978-1-5386-5024-0, 2018.
Digital Object Identifier: 10.1109/HSI.2018.8431246 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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