TY - GEN
T1 - Motion Intention Estimation of Finger Motions with Spatial Variations of HD EMG Signals
AU - Danwatta Sanjaya, Vipula Bandara
AU - Chongzaijiao, He
AU - Arata, Jumpei
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 21K18105.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Estimation of motion intention of human hand has many applications in robotics and other human centred areas. Especially with wearable robotic applications biosignal based estimation of human hand motions are widely used. However, based on the construction of the muscles for finger motions, and the higher number of independent motions of the human hand, estimation of finger motions accurately and effectively remains a challenge with current techniques. On the other hand, high density electromyography (HDEMG), has the capability to provide a high resolution spatial activation image of the muscle group under its measurement. In this study HDEMG signals were used to estimate the finger motions, by using the spatial variations of the surface HDEMG signals during the different finger motions. Thus, features of Gabor filters and error-correcting output codes method was used to classify six motion classes of finger motions. Results showed the proposed methodology can successfully classify the motions with a higher accuracy, using the spatial information contained in HDEMG data.
AB - Estimation of motion intention of human hand has many applications in robotics and other human centred areas. Especially with wearable robotic applications biosignal based estimation of human hand motions are widely used. However, based on the construction of the muscles for finger motions, and the higher number of independent motions of the human hand, estimation of finger motions accurately and effectively remains a challenge with current techniques. On the other hand, high density electromyography (HDEMG), has the capability to provide a high resolution spatial activation image of the muscle group under its measurement. In this study HDEMG signals were used to estimate the finger motions, by using the spatial variations of the surface HDEMG signals during the different finger motions. Thus, features of Gabor filters and error-correcting output codes method was used to classify six motion classes of finger motions. Results showed the proposed methodology can successfully classify the motions with a higher accuracy, using the spatial information contained in HDEMG data.
UR - http://www.scopus.com/inward/record.url?scp=85159607107&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159607107&partnerID=8YFLogxK
U2 - 10.1109/ICCAE56788.2023.10111336
DO - 10.1109/ICCAE56788.2023.10111336
M3 - Conference contribution
AN - SCOPUS:85159607107
T3 - 2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023
SP - 356
EP - 360
BT - 2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Computer and Automation Engineering, ICCAE 2023
Y2 - 3 March 2023 through 5 March 2023
ER -