Please go to Google Scholar, ResearchGate, or ORCiD for a full publication list.
Journal Publications
2023
[J17] X. Bao, Q. Zhang, N. Fragnito, J. Wang, & N. Sharma* (2023). A clustering-based method for estimating pennation angle from B-mode ultrasound images. Wearable Technologies, 4 (2023): e6.
[J16] Q. Zhang, K. Lambeth, Z. Sun, A. Dodson, X. Bao, and N. Sharma*, “Evaluation of a Fused Sonomyography and Electromyography-based Control on a Cable-Driven Ankle Exoskeleton”, IEEE Trans. Robot., 39 (3), pp. 2183 - 2202, 2023.
[J15] Q. Zhang, K. Lambeth, A. Iyer, Z. Sun, and N. Sharma*, “Ultrasound Imaging-based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction”, IEEE Trans. Control Syst. Technol., 31(3), pp. 989-1005, 2023.
2022
[J14] Q. Zhang, N. Fragnito, X. Bao, and N. Sharma*, “A Deep Learning Method to Predict Ankle Joint Moment during Versatile Walking Tasks with Ultrasound Imaging: A Framework for Assistive Devices Control”, Wearable Technologies, 3 (2022): e20.
[J13] Q. Zhang, V. Nalam, X. Tu, M. Li, J. Si, M. Lewek, and H. Huang*, “Imposing Healthy Hip Movement Pattern and Range by Exoskeleton Control for Individualized Assistance”, IEEE Robot. Autom. Lett., 7 (4), pp. 11126-11133, 2022.
[J12] Q. Zhang, A. Myers, N. Fragnito, J. R. Franz, and N. Sharma*, “Fused Ultrasound And Electromyography-Driven Neuromuscular Model To Improve Plantarexion Moment Prediction Across Walking Speeds”, J. Neuroeng. Rehabil., vol. 19, no. 86, 2022.
[J11] Q. Zhang, W. H. Clark, J. R. Franz, and N. Sharma*, “Personalized Fusion of Ultrasound and Electromyography-Derived Neuromuscular Features Increases Prediction Accuracy of Ankle Moment during Plantarflexion,” Biomed. Signal Process Control, vol. 71, pp. 103100, 2022.
[J10] Q. Zhang, A. Iyer, K. Lambeth, K. Kim, and N. Sharma*, “Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation,” Sensors, vol. 22, no. 1, pp. 335, 2022.
[J9] M. Vahidreza, Q. Zhang, X. Bao, and N. Sharma*, “An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton”, IEEE Trans. Control Syst. Technol., vol. 30, no. 3, pp. 1021-1036, 2022.
2021
[J8] Q. Zhang, A. Iyer, Z. Sun, K. Kim, and N. Sharma*, “A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, pp. 1944-1954, 2021.
[J7] Q. Zhang, A. Iyer, K. Kim*, and N. Sharma*, “Evaluation of Noninvasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging,” IEEE Trans. Biomed. Eng., vol. 68, no. 3, pp. 1044–1055, 2021.
[J6] M. Vahidreza, Q. Zhang, X. Bao, B. Dicianno, and N. Sharma*, “Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation using Iterative Learning and Fatigue Optimization”, Front. Robot. AI, 8: 711388, 2021.
2020
[J5] Q. Zhang, K. Kim*, and N. Sharma*, “Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 1, pp. 318–327, 2020.
[J4] Q. Zhang, D. Sun, W. Qian, X. Xiao, and Z. Guo*, “Modeling and control of a cable-driven rotary series elastic actuator for an upper limb rehabilitation robot,” Front. Neurorobot., vol. 14, pp. 13, 2020.
Before 2020
[J3] Q. Zhang, Y. Wang, and X. H. Xiao*, “Effects of Ground Compliance on Bipedal Robot Walking Dynamic Property”, Journal of the Chinese Society of Mechanical Engineers, 2016, 37(4): 335-342.
[J2] Y. Wang, Q. Zhang, and X. H. Xiao*, “Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling,” ROBOT, 2016, 38(2): 248-256. (In Chinese)
[J1] Q. Zhang, X. H. Xiao*, Y. Wang, et al, “Compliant joint for biped robot considering energy consumption optimization”, Journal of Central South University, 2015, 46(11): 4070-4076. (In Chinese)
[J17] X. Bao, Q. Zhang, N. Fragnito, J. Wang, & N. Sharma* (2023). A clustering-based method for estimating pennation angle from B-mode ultrasound images. Wearable Technologies, 4 (2023): e6.
[J16] Q. Zhang, K. Lambeth, Z. Sun, A. Dodson, X. Bao, and N. Sharma*, “Evaluation of a Fused Sonomyography and Electromyography-based Control on a Cable-Driven Ankle Exoskeleton”, IEEE Trans. Robot., 39 (3), pp. 2183 - 2202, 2023.
[J15] Q. Zhang, K. Lambeth, A. Iyer, Z. Sun, and N. Sharma*, “Ultrasound Imaging-based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction”, IEEE Trans. Control Syst. Technol., 31(3), pp. 989-1005, 2023.
2022
[J14] Q. Zhang, N. Fragnito, X. Bao, and N. Sharma*, “A Deep Learning Method to Predict Ankle Joint Moment during Versatile Walking Tasks with Ultrasound Imaging: A Framework for Assistive Devices Control”, Wearable Technologies, 3 (2022): e20.
[J13] Q. Zhang, V. Nalam, X. Tu, M. Li, J. Si, M. Lewek, and H. Huang*, “Imposing Healthy Hip Movement Pattern and Range by Exoskeleton Control for Individualized Assistance”, IEEE Robot. Autom. Lett., 7 (4), pp. 11126-11133, 2022.
[J12] Q. Zhang, A. Myers, N. Fragnito, J. R. Franz, and N. Sharma*, “Fused Ultrasound And Electromyography-Driven Neuromuscular Model To Improve Plantarexion Moment Prediction Across Walking Speeds”, J. Neuroeng. Rehabil., vol. 19, no. 86, 2022.
[J11] Q. Zhang, W. H. Clark, J. R. Franz, and N. Sharma*, “Personalized Fusion of Ultrasound and Electromyography-Derived Neuromuscular Features Increases Prediction Accuracy of Ankle Moment during Plantarflexion,” Biomed. Signal Process Control, vol. 71, pp. 103100, 2022.
[J10] Q. Zhang, A. Iyer, K. Lambeth, K. Kim, and N. Sharma*, “Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation,” Sensors, vol. 22, no. 1, pp. 335, 2022.
[J9] M. Vahidreza, Q. Zhang, X. Bao, and N. Sharma*, “An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton”, IEEE Trans. Control Syst. Technol., vol. 30, no. 3, pp. 1021-1036, 2022.
2021
[J8] Q. Zhang, A. Iyer, Z. Sun, K. Kim, and N. Sharma*, “A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, pp. 1944-1954, 2021.
[J7] Q. Zhang, A. Iyer, K. Kim*, and N. Sharma*, “Evaluation of Noninvasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging,” IEEE Trans. Biomed. Eng., vol. 68, no. 3, pp. 1044–1055, 2021.
[J6] M. Vahidreza, Q. Zhang, X. Bao, B. Dicianno, and N. Sharma*, “Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation using Iterative Learning and Fatigue Optimization”, Front. Robot. AI, 8: 711388, 2021.
2020
[J5] Q. Zhang, K. Kim*, and N. Sharma*, “Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 1, pp. 318–327, 2020.
[J4] Q. Zhang, D. Sun, W. Qian, X. Xiao, and Z. Guo*, “Modeling and control of a cable-driven rotary series elastic actuator for an upper limb rehabilitation robot,” Front. Neurorobot., vol. 14, pp. 13, 2020.
Before 2020
[J3] Q. Zhang, Y. Wang, and X. H. Xiao*, “Effects of Ground Compliance on Bipedal Robot Walking Dynamic Property”, Journal of the Chinese Society of Mechanical Engineers, 2016, 37(4): 335-342.
[J2] Y. Wang, Q. Zhang, and X. H. Xiao*, “Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling,” ROBOT, 2016, 38(2): 248-256. (In Chinese)
[J1] Q. Zhang, X. H. Xiao*, Y. Wang, et al, “Compliant joint for biped robot considering energy consumption optimization”, Journal of Central South University, 2015, 46(11): 4070-4076. (In Chinese)