Qiang Zhang

Bao, X., Zhang, Q., Fragnito, N., Wang, J., & Sharma, N. (2023). A clustering-based method for estimating pennation angle from B-mode ultrasound images. Wearable Technologies. https://doi.org/10.1017/wtc.2022.30

Zhang, Q., Lambeth, K., Sun, Z., Dodson, A., Bao, X., & Sharma, N. (2023, February 9). Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton. IEEE TRANSACTIONS ON ROBOTICS, Vol. 2. https://doi.org/10.1109/TRO.2023.3236958

Zhang, Q., Fragnito, N., Bao, X., & Sharma, N. (2022). A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control. Wearable Technologies. https://doi.org/10.1017/wtc.2022.18

Molazadeh, V., Zhang, Q., Bao, X., & Sharma, N. (2022). An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton. IEEE Transactions on Control Systems Technology, 30(3), 1021–1036. https://doi.org/10.1109/TCST.2021.3089885

Zhang, Q., Fragnito, N., Franz, J. R., & Sharma, N. (2022, January 11). Fused Ultrasound And Electromyography-Driven Neuromuscular Model To Improve Plantarflexion Moment Prediction Across Walking Speeds. https://doi.org/10.21203/rs.3.rs-1136552/v1

Zhang, Q., Fragnito, N., Franz, J. R., & Sharma, N. (2022). Fused Ultrasound and Electromyography-driven Neuromuscular Model to Improve Plantarflexion Moment Prediction across Walking Speeds. ResearchSquare. https://doi.org/10.21203/rs.3.rs-1136552

Zhang, Q., Fragnito, N., Franz, J. R., & Sharma, N. (2022). Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds. Journal of NeuroEngineering and Rehabilitation, 19(1). https://doi.org/10.1186/s12984-022-01061-z

Zhang, Q., Nalam, V., Tu, X., Li, M., Si, J., Lewek, M. D., & Huang, H. H. (2022). Imposing Healthy Hip Motion Pattern and Range by Exoskeleton Control for Individualized Assistance. IEEE Robotics and Automation Letters, 7(4), 11126–11133. https://doi.org/10.1109/LRA.2022.3196105

Zhang, Q., Clark, W. H., Franz, J. R., & Sharma, N. (2022). Personalized fusion of ultrasound and electromyography-derived neuromuscular features increases prediction accuracy of ankle moment during plantarflexion. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 71. https://doi.org/10.1016/j.bspc.2021.103100

Wang, Y., Zhang, Q., & Xiao, X.-H. (2022). Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling. ArXiv. https://doi.org/10.48550/arXiv.2208.07157

Zhang, Q., Iyer, A., Lambeth, K., Kim, K., & Sharma, N. (2022). Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation. SENSORS, 22(1). https://doi.org/10.3390/s22010335

Zhang, Q., Lambeth, K., Iyer, A., Sun, Z., & Sharma, N. (2022, September 30). Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol. 9. https://doi.org/10.1109/TCST.2022.3207999

Zhang, Q., Iyer, A., & Sharma, N. (2022). Ultrasound-Based Sensing and Control of Functional Electrical Stimulation for Ankle Joint Dorsiflexion: Preliminary Study. In Biosystems and Biorobotics (Vol. 27, pp. 307–311). https://doi.org/10.1007/978-3-030-69547-7_50

Zhang, Q., Iyer, A., Sun, Z., Kim, K., & Sharma, N. (2021). A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 29, 1944–1954. https://doi.org/10.1109/TNSRE.2021.3106900

Sun, Z., Bao, X., Zhang, Q., Lambeth, K., & Sharma, N. (2021). A Tube-based Model Predictive Control Method for Joint Angle Tracking with Functional Electrical Stimulation and An Electric Motor Assist. 2021 American Control Conference (ACC), 2021-May, 1390–1395. https://doi.org/10.23919/ACC50511.2021.9483084

Zhang, Q., Iyer, A., Kim, K., & Sharma, N. (2021). Evaluation of Non-Invasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 68(3), 1044–1055. https://doi.org/10.1109/TBME.2020.3014861

Zhang, Q., Fragnito, N., Myers, A., & Sharma, N. (2021). Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021-January, 6267–6272. https://doi.org/10.1109/EMBC46164.2021.9630046

Molazadeh, V., Zhang, Q., Bao, X., Dicianno, B. E., & Sharma, N. (2021). Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning. FRONTIERS IN ROBOTICS AND AI, 8. https://doi.org/10.3389/frobt.2021.711388

Zhang, Q., Iyer, A., Lambeth, K., Kim, K., & Sharma, N. (2021). Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 5948–5952. https://doi.org/10.1109/EMBC46164.2021.9630325

Iyer, A., Sheng, Z., Zhang, Q., Kim, K., & Sharma, N. (2020). Analysis of Tremor During Grasp Using Ultrasound Imaging: Preliminary Study. Proceedings of the 8th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 2020-November, 533–538. https://doi.org/10.1109/BioRob49111.2020.9224446

Zhang, Q., Sun, D., Qian, W., Xiao, X., & Guo, Z. (2020). Modeling and Control of a Cable-Driven Rotary Series Elastic Actuator for an Upper Limb Rehabilitation Robot. FRONTIERS IN NEUROROBOTICS, 14. https://doi.org/10.3389/fnbot.2020.00013

Zhang, Q., Kim, K., & Sharma, N. (2020). Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 28(1), 318–327. https://doi.org/10.1109/TNSRE.2019.2953588

Zhang, Q., Iyer, A., Sun, Z., Dodson, A., & Sharma, N. (2020). Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation [Paper]. Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions, 1(DSCC2020-3225). https://doi.org/10.1115/DSCC2020-3225

Zhang, Q., Iyer, A., Kim, K., & Sharma, N. (2020). Volitional Contractility Assessment of Plantar Flexors by Using Non-invasive Neuromuscular Measurements. 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 2020-November, 515–520. https://doi.org/10.1109/BioRob49111.2020.9224298

Zhang, Q., Sheng, Z., Moore-Clingenpeel, F., Kim, K., & Sharma, N. (2019). Ankle Dorsiflexion Strength Monitoring by Combining Sonomyography and Electromyography. 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), 2019-June, 240–245. https://doi.org/10.1109/ICORR.2019.8779530

Molazadeh, V., Zhang, Q., Bao, X., & Sharma, N. (2019). Neural-network based iterative learning control of a hybrid exoskeleton with an MPC allocation strategy. ASME 2019 Dynamic Systems and Control Conference, DSCC 2019, 1(DSCC2019-9191). https://doi.org/10.1115/DSCC2019-9191

Zhang, Q., Sheng, Z., Kim, K., & Sharma, N. (2019). Observer Design for a Nonlinear Neuromuscular System with Multi-rate Sampled and Delayed Output Measurements. 2019 American Control Conference (ACC), 2019-July, 872–877. https://doi.org/10.23919/acc.2019.8814473

Zhang, Q., Xu, B., Guo, Z., & Xiao, X. (2017). Design and modeling of a compact rotary series elastic actuator for an elbow rehabilitation robot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 44–56). https://doi.org/10.1007/978-3-319-65298-6_5

Zhang, Q., Wang, Y., & Xiao, X.-H. (2016). Effects of Ground Compliance on Bipedal Robot Walking Dynamic Property. Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'Eng Hsuebo Pao, 37(4), 335–342. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85025162616&partnerID=MN8TOARS

Zhang, Q., Xiao, X., & Guo, Z. (2016). Power efficiency-based stiffness optimization of a compliant actuator for underactuated bipedal robot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 186–197). https://doi.org/10.1007/978-3-319-43506-0_16

Wang, Y., Zhang, Q., & Xiao, X. (2016). Trajectory tracking control of the bionic joint actuated by pneumatic artificial muscle based on robust modeling. Jiqiren/Robot, 38(2), 248–256. https://doi.org/10.13973/j.cnki.robot.2016.0248

Zhang, Q., Teng, L., Wang, Y., Xie, T., & Xiao, X. (2015). A study of flexible energy-saving joint for biped robots considering sagittal plane motion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9245, pp. 333–344). https://doi.org/10.1007/978-3-319-22876-1_29

Zhang, Q., Xiao, X., Wang, Y., You, P., & Xie, T. (2015). Compliant joint for biped robot considering energy consumption optimization. Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), 46(11), 4070–4076. https://doi.org/10.11817/j.issn.1672-7207.2015.11.014