@article{kruse_schur_johnson-marcus_gilbert_di lallo_gao_su_2024, title={Assistive Technology's Potential to Improve Employment of People with Disabilities}, volume={1}, ISSN={["1573-3688"]}, DOI={10.1007/s10926-023-10164-w}, journal={JOURNAL OF OCCUPATIONAL REHABILITATION}, author={Kruse, Douglas and Schur, Lisa and Johnson-Marcus, Hazel-Anne and Gilbert, Lauren and Di Lallo, Antonio and Gao, Weibo and Su, Hao}, year={2024}, month={Jan} }
 @article{yu_huang_di lallo_zhang_wang_fu_su_2022, title={Bio-inspired design of a self-aligning, lightweight, and highly-compliant cable-driven knee exoskeleton}, volume={16}, ISSN={["1662-5161"]}, DOI={10.3389/fnhum.2022.1018160}, abstractNote={Powered knee exoskeletons have shown potential for mobility restoration and power augmentation. However, the benefits of exoskeletons are partially offset by some design challenges that still limit their positive effects on people. Among them, joint misalignment is a critical aspect mostly because the human knee joint movement is not a fixed-axis rotation. In addition, remarkable mass and stiffness are also limitations. Aiming to minimize joint misalignment, this paper proposes a bio-inspired knee exoskeleton with a joint design that mimics the human knee joint. Moreover, to accomplish a lightweight and high compliance design, a high stiffness cable-tension amplification mechanism is leveraged. Simulation results indicate our design can reduce 49.3 and 71.9% maximum total misalignment for walking and deep squatting activities, respectively. Experiments indicate that the exoskeleton has high compliance (0.4 and 0.1 Nm backdrive torque under unpowered and zero-torque modes, respectively), high control bandwidth (44 Hz), and high control accuracy (1.1 Nm root mean square tracking error, corresponding to 7.3% of the peak torque). This work demonstrates performance improvement compared with state-of-the-art exoskeletons.}, journal={FRONTIERS IN HUMAN NEUROSCIENCE}, author={Yu, Shuangyue and Huang, Tzu-Hao and Di Lallo, Antonio and Zhang, Sainan and Wang, Tian and Fu, Qiushi and Su, Hao}, year={2022}, month={Nov} }
 @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"]}, 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}, 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} }