TY - GEN
T1 - Wearable wristworn gesture recognition using echo state network
AU - Wang, Weipeng
AU - Liang, Xiangpeng
AU - Assaad, Maher
AU - Heidari, Hadi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This paper presents a novel gesture sensing system for prosthetic limb control based on a pressure sensor array embedded in a wristband. The tendon movement which produces pressure change around the wrist can be detected by pressure sensors. A microcontroller is used to gather the data from the sensors, followed by transmitting the data into a computer. A user interface is developed in LabVIEW, which presents the value of each sensor and display the waveform in real-time. Moreover, the data pattern of each gesture varies from different users due to the non-uniform subtle tendon movement. To overcome this challenge, Echo State Network (ESN), a supervised learning network, is applied to the data for calibrating different users. The results of gesture recognition show that the ESN has a good performance in multiple dimensional classifications. For experimental data collected from six participants, the proposed system classifies five gestures with an accuracy of 87.3%.
AB - This paper presents a novel gesture sensing system for prosthetic limb control based on a pressure sensor array embedded in a wristband. The tendon movement which produces pressure change around the wrist can be detected by pressure sensors. A microcontroller is used to gather the data from the sensors, followed by transmitting the data into a computer. A user interface is developed in LabVIEW, which presents the value of each sensor and display the waveform in real-time. Moreover, the data pattern of each gesture varies from different users due to the non-uniform subtle tendon movement. To overcome this challenge, Echo State Network (ESN), a supervised learning network, is applied to the data for calibrating different users. The results of gesture recognition show that the ESN has a good performance in multiple dimensional classifications. For experimental data collected from six participants, the proposed system classifies five gestures with an accuracy of 87.3%.
KW - Echo State Network
KW - Force-Sensing Resistor
KW - Gesture recognition
KW - Human-machine interaction
UR - https://www.scopus.com/pages/publications/85079115893
U2 - 10.1109/ICECS46596.2019.8965219
DO - 10.1109/ICECS46596.2019.8965219
M3 - Conference contribution
AN - SCOPUS:85079115893
T3 - 2019 26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019
SP - 875
EP - 878
BT - 2019 26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019
Y2 - 27 November 2019 through 29 November 2019
ER -