Qiang Zhang's Website
Peer-review Journal Publications
Please go to Google Scholar or ORCiD for a full publication list.
Evaluation of a Fused Sonomyography and Electromyography-based Control on a Cable-Driven Ankle Exoskeleton
[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., 2022 (DOI: 10.1109/TRO.2023.3236958).
A Deep Learning Method to Predict Ankle Joint Moment during Versatile Walking Tasks with Ultrasound Imaging: A Framework for Assistive Devices Control
[J15] 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.
Imposing Healthy Hip Movement Pattern and Range by Exoskeleton Control for Individualized Assistance
[J14] 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., vol. 7, no. 4, pp. 11126-11133, 2022.
Fused Ultrasound And Electromyography-Driven Neuromuscular Model To Improve Plantarexion Moment Prediction Across Walking Speeds
[J13] 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.
Ultrasound Imaging-based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction
[J12] 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., 2022 (DOI: 10.1109/TCST.2022.3207999).
Personalized Fusion of Ultrasound and Electromyography-Derived Neuromuscular Features Increases Prediction Accuracy of Ankle Moment during Plantarflexion
[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.
Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation
[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.
An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton
[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.
A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study
[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.
Evaluation of Noninvasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging
[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.
Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation using Iterative Learning and Fatigue Optimization
[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.
Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography
[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.
Modeling and control of a cable-driven rotary series elastic actuator for an upper limb rehabilitation robot
[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.
Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling
[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)
Peer-review Conference Publications
A Robotic Assistance Personalization Control Approach of Hip
Exoskeletons for Gait Symmetry Improvement
[C16] Q. Zhang, X. Tu, J. Si, M. Lewek, and H. Huang*, “A Robotic Assistance Personalization Control Approach of Hip Exoskeletons for Gait Symmetry Improvement,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, under review.
Normalizing Hip Movement Pattern and Range by Exoskeleton Control for Individualized Assistance
[C15] Q. Zhang, V. Nalam, X. Tu, M. Li, J. Si, M. Lewek, H. Huang*, “Normalizing Hip Movement Pattern and Range by Exoskeleton Control for Individualized Assistance,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
An Online Actor-Critic Identifier with Sampled Fatigue Measurements for Optimal Adaptive Control of FES and an Electric Motor
[C14] A. Iyer, M. Singh, Q. Zhang, Z. Sun, and N. Sharma*, “An Online Actor-Critic Identifier with Sampled Fatigue Measurements for Optimal Adaptive Control of FES and an Electric motor,” in IEEE Conference on Control Technology and Applications (CCTA), pp. 714-719. IEEE, 2022.
Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation
[C13] Q. Zhang, A. Iyer, K. Lambeth, K. Kim, and N. Sharma*, “Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation”, in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2021, pp. 5948-5952.
Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model
[C12] Q. Zhang, N. Fragnito, A. Myers, and N. Sharma*, “Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model”, in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2021, pp. 6267-6272.
A Tube-based Model Predictive Control Method for Joint Angle Tracking with Functional Electrical Stimulation and An Electric Motor Assist
[C11] Z. Sun, X. Bao, Q. Zhang, K. Lambeth, and N. Sharma*, “A Tube-based Model Predictive Control Method for Joint Angle Tracking with Functional Electrical Stimulation and An Electric Motor Assist,” in Proc. Amer. Control Conf., 2021, pp. 1390-1395.
Volitional contractility assessment of plantar flexors by using noninvasive neuromuscular measurements
[C10] Q. Zhang, A. Iyer, K. Kim*, and N. Sharma*, “Volitional contractility assessment of plantar flexors by using noninvasive neuromuscular measurements,” in 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). IEEE, 2020, pp. 515–520.
Analysis of Tremor During Grasp Using Ultrasound Imaging: Preliminary Study
[C9] A. Iyer, Z. Sheng, Q. Zhang, K. Kim, and N. Sharma*, “Analysis of Tremor During Grasp Using Ultrasound Imaging: Preliminary Study”, in 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). IEEE, 2020, pp. 533–538.
Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation
[C8] Q. Zhang, A. Iyer, Z. Sun, A. Dodson, and N. Sharma*, “Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation,” in Dynamic Systems and Control Conference, Vol. 84270, p. V001T14A003, American Society of Mechanical Engineers, 2020.
Ultrasound based Sensing and Control of Functional Electrical Stimulation for Ankle Joint Dorsiflexion: Preliminary Study
[C7] Q. Zhang, A. Iyer, N. Sharma*, “Ultrasound based Sensing and Control of Functional Electrical Stimulation for Ankle Joint Dorsiflexion: Preliminary Study,” in 2020 International Symposium on Wearable Robotics. Springer, 2020, pp. 207-311.
Ankle dorsiflexion strength monitoring by combining sonomyography and electromyography
[C6] Q. Zhang, Z. Sheng, F. Moore-Clingenpeel, K. Kim, and N. Sharma*, “Ankle dorsiflexion strength monitoring by combining sonomyography and electromyography,” in Proc. Int. Conf. Rehabil. Robot. IEEE, 2019, pp. 240–245.
Observer design for a nonlinear neuromuscular system with multi-rate sampled and delayed output measurements
[C5] Q. Zhang, Z. Sheng, K. Kim, and N. Sharma*, “Observer design for a nonlinear neuromuscular system with multi-rate sampled and delayed output measurements,” in Proc. Amer. Control Conf. IEEE, 2019, pp. 872–877.
Neural-network based iterative learning control of a hybrid exoskeleton with an MPC allocation strategy
[C4] V. Molazadeh, Q. Zhang, X. Bao, and N. Sharma*, “Neural-network based iterative learning control of a hybrid exoskeleton with an MPC allocation strategy,” in Dynamic Systems and Control Conference, vol. 59148, p. V001T05A011. American Society of Mechanical Engineers, 2019.
A study of flexible energy-saving joint for biped robots considering the sagittal plane motion
[C1] Q. Zhang, L. Teng, Y. Wang, T. Xie, and X. Xiao*, “A study of flexible energy-saving joint for biped robots considering the sagittal plane motion,” in Lecture Notes in Computer Science, 2015, vol. 9245, pp. 333–344.