TY - GEN
T1 - Adapting Random Forest Classifier Based on Single and Multiple Features for Surface Electromyography Signal Recognition
AU - Zhou, Tao
AU - Omisore, Olatunji Mumini
AU - Du, Wenjing
AU - Wang, Lei
AU - Zhang, Yuan
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (Grants No.61572231, No.U1713219), the Outstanding Youth Innovation Research Fund of SIAT-CAS (No.Y8G0381001), and The Enhancement Project for Shenzhen Biomedical Electronics Technology Public Service Platform.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Surface Electromyography (sEMG) signals have been recently adopted in developing control models for wearable interventional robots and related surgical systems' mechanical control problems. However, adapting sEMG signal models for intravascular robotic interventions is yet to receive any attention due to inability of obtaining continuously reliable signals from the human experts and real-time decoding and integration of the control outputs into the the robotic mechanism. Pattern recognition modeling and classification are vital steps towards developing effective sEMG-based signal models for surgeon-robot intravascular procedures, and these are focused in this paper. For this purpose, nine single features and nine groups of multiple features were investigated for hand motion recognition with the random forest classifier. The proposed random forest classifier was trained and evaluated using a dataset with recordings of twelve finger motions made by ten subjects from a publicly accessible database in the Ninapro project. An average motion classification accuracy of 84.11±3.99% and a best result of subject of 92.94% was obtained from the adapted classifier. Besides this, we also employed three performance metrics to explore more performance detail of the experimental results, and to showed the learning process of the training process.
AB - Surface Electromyography (sEMG) signals have been recently adopted in developing control models for wearable interventional robots and related surgical systems' mechanical control problems. However, adapting sEMG signal models for intravascular robotic interventions is yet to receive any attention due to inability of obtaining continuously reliable signals from the human experts and real-time decoding and integration of the control outputs into the the robotic mechanism. Pattern recognition modeling and classification are vital steps towards developing effective sEMG-based signal models for surgeon-robot intravascular procedures, and these are focused in this paper. For this purpose, nine single features and nine groups of multiple features were investigated for hand motion recognition with the random forest classifier. The proposed random forest classifier was trained and evaluated using a dataset with recordings of twelve finger motions made by ten subjects from a publicly accessible database in the Ninapro project. An average motion classification accuracy of 84.11±3.99% and a best result of subject of 92.94% was obtained from the adapted classifier. Besides this, we also employed three performance metrics to explore more performance detail of the experimental results, and to showed the learning process of the training process.
KW - Confusion Matrix
KW - Feature Extraction
KW - Random Forest
KW - Robotic Catheterization
KW - SEMG Pattern Recognition
UR - http://www.scopus.com/inward/record.url?scp=85079168403&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI48845.2019.8965719
DO - 10.1109/CISP-BMEI48845.2019.8965719
M3 - Conference contribution
AN - SCOPUS:85079168403
T3 - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
BT - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
A2 - Li, Qingli
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Y2 - 19 October 2019 through 21 October 2019
ER -
