TY - GEN
T1 - JISAP
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
AU - Zhou, Tian
AU - Cha, Jackie S.
AU - Gonzalez, Glebys T.
AU - Sundaram, Chandru P.
AU - Wachs, Juan P.
AU - Yu, Denny
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - In Robot-Assisted Surgery, predicting surgeon attributes such as task workload, operation performance, and expertise levels is important in providing tailored assistance. This paper proposes Joint Inference for Surgeon Attributes Prediction (JISAP), a computational framework to jointly infer surgeon attributes (i.e., task workload, operation performance, and expertise level) from multimodal physiological signals (heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity). JISAP was evaluated with a dataset of twelve surgeons operating on the da Vinci Skills Simulator. It was found that JISAP can simultaneously predict surgeon attributes with a percentage error of 11.05%. Additionally, joint inference was found to outperform isolated inference with a boost of 10%.
AB - In Robot-Assisted Surgery, predicting surgeon attributes such as task workload, operation performance, and expertise levels is important in providing tailored assistance. This paper proposes Joint Inference for Surgeon Attributes Prediction (JISAP), a computational framework to jointly infer surgeon attributes (i.e., task workload, operation performance, and expertise level) from multimodal physiological signals (heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity). JISAP was evaluated with a dataset of twelve surgeons operating on the da Vinci Skills Simulator. It was found that JISAP can simultaneously predict surgeon attributes with a percentage error of 11.05%. Additionally, joint inference was found to outperform isolated inference with a boost of 10%.
UR - http://www.scopus.com/inward/record.url?scp=85081162093&partnerID=8YFLogxK
U2 - 10.1109/IROS40897.2019.8968097
DO - 10.1109/IROS40897.2019.8968097
M3 - Conference contribution
AN - SCOPUS:85081162093
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2246
EP - 2251
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PB - Institute of Electrical and Electronics Engineers
Y2 - 3 November 2019 through 8 November 2019
ER -