TY - GEN
T1 - Gesture Recognition Using Machine Learning for Light Communication Systems
AU - Webber, Julian
AU - Mehbodniya, Abolfazl
AU - Arafa, Ahmed
AU - Alwakeel, Ahmed
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Gesture recognition has a wide range of human-computer interface (HCI) applications in the home, commerce or office. However, the most widely used methods for recognizing gestures are computationally expensive and costly. We propose to apply gesture recognition to an existing visible light communication (VLC) system. Different finger motions are detected using a long short-term memory (LSTM) network operating on light transitions between fingers. At the receiver side, the platform utilizes a light-emitting diode and photodiode. The device can distinguish motions from gaps in direct light transmission, making it compatible with high-speed light communication systems. The accuracy of gesture identification was evaluated for five different gestures over a distance of 48 cm and the findings show the method is capable of successfully identifying the motions with 88 percent accuracy.
AB - Gesture recognition has a wide range of human-computer interface (HCI) applications in the home, commerce or office. However, the most widely used methods for recognizing gestures are computationally expensive and costly. We propose to apply gesture recognition to an existing visible light communication (VLC) system. Different finger motions are detected using a long short-term memory (LSTM) network operating on light transitions between fingers. At the receiver side, the platform utilizes a light-emitting diode and photodiode. The device can distinguish motions from gaps in direct light transmission, making it compatible with high-speed light communication systems. The accuracy of gesture identification was evaluated for five different gestures over a distance of 48 cm and the findings show the method is capable of successfully identifying the motions with 88 percent accuracy.
KW - gesture recognition
KW - human activity recognition
KW - LSTM
KW - machine learning
KW - VLC
UR - http://www.scopus.com/inward/record.url?scp=85129168142&partnerID=8YFLogxK
U2 - 10.1109/MECON53876.2022.9752211
DO - 10.1109/MECON53876.2022.9752211
M3 - Conference contribution
AN - SCOPUS:85129168142
T3 - 2022 International Mobile and Embedded Technology Conference, MECON 2022
SP - 52
EP - 56
BT - 2022 International Mobile and Embedded Technology Conference, MECON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Mobile and Embedded Technology Conference, MECON 2022
Y2 - 10 March 2022 through 11 March 2022
ER -