@article{zhang_zhu_huang_yu_sen huang_lopez-sanchez_devine_abdelhady_zheng_bulea_et al._2024, title={Actuator optimization and deep learning-based control of pediatric knee exoskeleton for community-based mobility assistance}, volume={97}, ISSN={["0957-4158"]}, url={https://doi.org/10.1016/j.mechatronics.2023.103109}, DOI={10.1016/j.mechatronics.2023.103109}, abstractNote={Lightweight and smart exoskeletons offer the potential to improve mobility in children. State-of-the-art pediatric exoskeletons are typically clinic-based since they are either tethered or portable but cumbersome and their design is often not optimized across a range of environments and users. To facilitate pediatric exoskeleton in community settings, we first proposed an actuator optimization framework that identified the optimal design parameters for both motor and transmission while minimizing the actuator mass and satisfying the output torque, speed, bandwidth, and resistance torque requirements. Guided by the optimization results, we customized a simple, lightweight actuator that met all mechatronic constraints for our portable exoskeleton (1.78 kg unilateral). Secondly, we adopted deep learning (Long Short Term Memory) based on gait phase estimation to facilitate stable control for community use. The models accurately estimated the gait phase on irregular walking patterns (accuracy 94.60%) without explicit training in children (typically developing and with cerebral palsy). The controller results demonstrated an elevated ability to adapt to the irregular gait patterns of the child with cerebral palsy. The experimental results in the child with typical development and four healthy adults demonstrated accurate assistive torque tracking performance (accuracy 97.00%) at different walking speeds (i.e., under uncertain torque to wearers). This work presented a holistic solution that includes both hardware innovation (actuator optimization framework) and software innovation (deep learning-based control) towards the paradigm shift of pediatric exoskeletons from clinic to community setting.}, journal={MECHATRONICS}, author={Zhang, Sainan and Zhu, Junxi and Huang, Tzu-Hao and Yu, Shuangyue and Sen Huang, Jin and Lopez-Sanchez, Ivan and Devine, Taylor and Abdelhady, Mohamed and Zheng, Minghui and Bulea, Thomas C. and et al.}, year={2024}, month={Feb} }
 @article{luo_jiang_zhang_zhu_yu_silva_wang_rouse_zhou_yuk_et al._2024, title={Experiment-free exoskeleton assistance via learning in simulation}, volume={630}, ISSN={["1476-4687"]}, url={https://doi.org/10.1038/s41586-024-07382-4}, DOI={10.1038/s41586-024-07382-4}, number={8016}, journal={NATURE}, author={Luo, Shuzhen and Jiang, Menghan and Zhang, Sainan and Zhu, Junxi and Yu, Shuangyue and Silva, Israel Dominguez and Wang, Tian and Rouse, Elliott and Zhou, Bolei and Yuk, Hyunwoo and et al.}, year={2024}, month={Jun} }
 @article{yu_yang_huang_zhu_visco_hameed_stein_zhou_su_2023, title={Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction}, volume={1}, ISSN={["1573-9686"]}, url={https://publons.com/wos-op/publon/59334038/}, DOI={10.1007/S10439-023-03151-Y}, abstractNote={Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ([Formula: see text]: 6.3%) and predict 200-ms-ahead ([Formula: see text]: 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices.}, journal={ANNALS OF BIOMEDICAL ENGINEERING}, author={Yu, Shuangyue and Yang, Jianfu and Huang, Tzu-Hao and Zhu, Junxi and Visco, Christopher J. and Hameed, Farah and Stein, Joel and Zhou, Xianlian and Su, Hao}, year={2023}, month={Jan} }
 @article{zhu_jiao_dominguez_yu_su_2022, title={Design and Backdrivability Modeling of a Portable High Torque Robotic Knee Prosthesis With Intrinsic Compliance for Agile Activities}, volume={6}, ISSN={["1941-014X"]}, url={http://dx.doi.org/10.1109/tmech.2022.3176255}, DOI={10.1109/TMECH.2022.3176255}, abstractNote={High-performance prostheses are crucial to enable versatile activities like walking, squatting, and running for lower extremity amputees. State-of-the-art prostheses are either not powerful enough to support demanding activities or have low compliance (low backdrivability) due to the use of high speed ratio transmission. Besides speed ratio, gearbox design is also crucial to the compliance of wearable robots, but its role is typically ignored in the design process. This article proposed an analytical backdrive torque model that accurately estimates the backdrive torque from both motor and transmission to inform the robot design. Following this model, this article also proposed methods for gear transmission design to improve compliance by reducing inertia of the knee prosthesis. We developed a knee prosthesis using a high torque actuator (built-in 9:1 planetary gear) with a customized 4:1 low-inertia planetary gearbox. Benchtop experiments show the backdrive torque model is accurate and proposed prosthesis can produce 200 Nm high peak torque (shield temperature <60 °C), high compliance (2.6 Nm backdrive torque), and high control accuracy (2.7/8.1/1.7 Nm RMS tracking errors for 1.25 m/s walking, 2 m/s running, and 0.25 Hz squatting, that are 5.4%/4.1%/1.4% of desired peak torques). Three able-bodied subject experiments showed our prosthesis could support agile and high-demanding activities.}, number={4}, journal={IEEE-ASME TRANSACTIONS ON MECHATRONICS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhu, Junxi and Jiao, Chunhai and Dominguez, Israel and Yu, Shuangyue and Su, Hao}, year={2022}, month={Jun} }
 @article{huang_zhang_yu_maclean_zhu_di lallo_jiao_bulea_zheng_su_2022, title={Modeling and Stiffness-Based Continuous Torque Control of Lightweight Quasi-Direct-Drive Knee Exoskeletons for Versatile Walking Assistance}, volume={38}, ISSN={["1941-0468"]}, url={https://doi.org/10.1109/TRO.2022.3170287}, DOI={10.1109/TRO.2022.3170287}, abstractNote={State-of-the-art exoskeletons are typically limited by the low control bandwidth and small-range stiffness of actuators, which are based on high gear ratios and elastic components (e.g., series elastic actuators). Furthermore, most exoskeletons are based on discrete gait phase detection and/or discrete stiffness control, resulting in discontinuous torque profiles. To fill these two gaps, we developed a portable, lightweight knee exoskeleton using quasi-direct-drive (QDD) actuation that provides 14 N·m torque (36.8% biological joint moment for overground walking). This article presents 1) stiffness modeling of torque-controlled QDD exoskeletons and 2) stiffness-based continuous torque controller that estimates knee joint moment in real-time. Experimental tests found that the exoskeleton had a high bandwidth of stiffness control (16 Hz under 100 N·m/rad) and high torque tracking accuracy with 0.34 N·m root mean square error (6.22%) across 0–350 N·m/rad large-range stiffness. The continuous controller was able to estimate knee moments accurately and smoothly for three walking speeds and their transitions. Experimental results with eight able-bodied subjects demonstrated that our exoskeleton was able to reduce the muscle activities of all eight measured knee and ankle muscles by 8.60%–15.22% relative to the unpowered condition and two knee flexors and one ankle plantar flexor by 1.92%–10.24% relative to the baseline (no exoskeleton) condition.}, number={3}, journal={IEEE TRANSACTIONS ON ROBOTICS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Huang, Tzu-Hao and Zhang, Sainan and Yu, Shuangyue and MacLean, Mhairi K. and Zhu, Junxi and Di Lallo, Antonio and Jiao, Chunhai and Bulea, Thomas C. and Zheng, Minghui and Su, Hao}, year={2022}, month={Jun}, pages={1442–1459} }
 @article{zhu_teolis_biassou_tabb_jabin_lavi_2022, title={Tracking the Adaptation and Compensation Processes of Patients’ Brain Arterial Network to an Evolving Glioblastoma}, volume={44}, url={https://doi.org/10.1109/TPAMI.2020.3008379}, DOI={10.1109/TPAMI.2020.3008379}, abstractNote={The brain's vascular network dynamically affects its development and core functions. It rapidly responds to abnormal conditions by adjusting properties of the network, aiding stabilization and regulation of brain activities. Tracking prominent arterial changes has clear clinical and surgical advantages. However, the arterial network functions as a system; thus, local changes may imply global compensatory effects that could impact the dynamic progression of a disease. We developed automated personalized system-level analysis methods of the compensatory arterial changes and mean blood flow behavior from a patient's clinical images. By applying our approach to data from a patient with aggressive brain cancer compared with healthy individuals, we found unique spatiotemporal patterns of the arterial network that could assist in predicting the evolution of glioblastoma over time. Our personalized approach provides a valuable analysis tool that could augment current clinical assessments of the progression of glioblastoma and other neurological disorders affecting the brain.}, number={1}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhu, Junxi and Teolis, Spencer and Biassou, Nadia and Tabb, Amy and Jabin, Pierre-Emmanuel and Lavi, Orit}, year={2022}, month={Jan}, pages={488–501} }
 @article{design and actuator optimization of lightweight and compliant knee exoskeleton for mobility assistance of children with crouch gait_2021, url={https://publons.com/wos-op/publon/60178700/}, journal={ArXiv}, year={2021} }
 @article{medical robots for infectious diseases <i>lessons and challenges from the covid</i>-<i>19 pandemic</i>_2021, url={https://publons.com/wos-op/publon/45470709/}, DOI={10.1109/MRA.2020.3045671}, abstractNote={Medical robots can play an important role in mitigating the spread of infectious diseases and delivering quality care to patients during the COVID-19 pandemic. Methods and procedures involving medical robots in the continuum of care, ranging from disease prevention, screening, diagnosis, treatment, and home care, have been extensively deployed and also present incredible opportunities for future development. This article provides an overview of the current state of the art, highlighting the enabling technologies and unmet needs for prospective technological advances within the next five to 10 years. We also identify key research and knowledge barriers that need to be addressed in developing effective and flexible solutions to ensure preparedness for rapid and scalable deployment to combat infectious diseases.}, journal={IEEE Robotics & Automation Magazine}, year={2021} }
 @article{block_yavarimanesh_natarajan_carek_mousavi_chandrasekhar_kim_zhu_schifitto_mestha_et al._2020, title={Conventional pulse transit times as markers of blood pressure changes in humans}, volume={10}, url={http://dx.doi.org/10.1038/s41598-020-73143-8}, DOI={10.1038/s41598-020-73143-8}, abstractNote={Abstract Pulse transit time (PTT) represents a potential approach for cuff-less blood pressure (BP) monitoring. Conventionally, PTT is determined by (1) measuring (a) ECG and ear, finger, or toe PPG waveforms or (b) two of these PPG waveforms and (2) detecting the time delay between the waveforms. The conventional PTTs (cPTTs) were compared in terms of correlation with BP in humans. Thirty-two volunteers [50% female; 52 (17) (mean (SD)) years; 25% hypertensive] were studied. The four waveforms and manual cuff BP were recorded before and after slow breathing, mental arithmetic, cold pressor, and sublingual nitroglycerin. Six cPTTs were detected as the time delays between the ECG R-wave and ear PPG foot, R-wave and finger PPG foot [finger pulse arrival time (PAT)], R-wave and toe PPG foot (toe PAT), ear and finger PPG feet, ear and toe PPG feet, and finger and toe PPG feet. These time delays were also detected via PPG peaks. The best correlation by a substantial extent was between toe PAT via the PPG foot and systolic BP [− 0.63 ± 0.05 (mean ± SE); p < 0.001 via one-way ANOVA]. Toe PAT is superior to other cPTTs including the popular finger PAT as a marker of changes in BP and systolic BP in particular.}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Block, Robert C. and Yavarimanesh, Mohammad and Natarajan, Keerthana and Carek, Andrew and Mousavi, Azin and Chandrasekhar, Anand and Kim, Chang-Sei and Zhu, Junxi and Schifitto, Giovanni and Mestha, Lalit K. and et al.}, year={2020}, month={Oct} }
 @article{medical robots for infectious diseases: lessons and challenges from the covid-19 pandemic_2020, url={https://publons.com/wos-op/publon/65912949/}, journal={ArXiv}, year={2020} }
 @article{zhu_jin_bighamian_kim_shipley_hahn_2019, title={Semiadaptive Infusion Control of Medications With Excitatory Dose-Dependent Effects}, volume={27}, url={https://doi.org/10.1109/TCST.2018.2815551}, DOI={10.1109/TCST.2018.2815551}, abstractNote={This brief presents a closed-loop control approach to infusion of medications that exhibit excitatory dose-dependent effects. A unique challenge associated with closed-loop control of such medications is that the upper limit of the medication-induced excitatory response is unknown, presenting a severe challenge in estimating the parameters in the traditional dose-response model. To address this challenge, we proposed a new dose-response model and semiadaptive (SA) control approach applicable to the closed-loop infusion control of excitatory medications. The new dose-response model eliminates the need for a priori knowledge of the upper limit of the medication-induced response via a novel parameterization to capture local dose-response relationship from the baseline to a target set point and a nonlinear function to convert the depressive response to an excitatory response. The SA control approach makes it possible to apply the well-established MRAC technique to the new dose-response model via selective adaptation of high-sensitivity parameters. We examined the efficacy of the proposed approach using an example of heart rate response to a vasoactive medication norepinephrine. System identification analysis using experimental data and in-silico controller testing suggested that the new dose-response model could faithfully reproduce the experimental data, and that the SA controller could effectively regulate the response in a wide range of simulated subjects.}, number={4}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhu, Junxi and Jin, Xin and Bighamian, Ramin and Kim, Chang-Sei and Shipley, Steven T. and Hahn, Jin-Oh}, year={2019}, month={Jul}, pages={1735–1743} }