J
Jianfu Yang
Researcher at City University of New York
Publications - 5
Citations - 112
Jianfu Yang is an academic researcher from City University of New York. The author has contributed to research in topics: Exoskeleton & Torque density. The author has an hindex of 3, co-authored 4 publications receiving 31 citations.
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Journal ArticleDOI
Quasi-Direct Drive Actuation for a Lightweight Hip Exoskeleton With High Backdrivability and High Bandwidth
Shuangyue Yu,Tzu-Hao Huang,Xiaolong Yang,Chunhai Jiao,Jianfu Yang,Yue Chen,Jingang Yi,Hao Su +7 more
TL;DR: The design and human–robot interaction modeling of a portable hip exoskeleton based on a custom quasi-direct drive actuation with performance improvement compared with state-of-the-art exoskeletons is described and demonstrated.
Posted Content
Quasi-Direct Drive Actuation for a Lightweight Hip Exoskeleton with High Backdrivability and High Bandwidth
Shuangyue Yu,Tzu-Hao Huang,Xiaolong Yang,Chunhai Jiao,Jianfu Yang,Hang Hu,Sainan Zhang,Yue Chen,Jingang Yi,Hao Su +9 more
TL;DR: In this paper, the authors describe the design and human-robot interaction modeling of a portable hip exoskeleton based on a custom quasi-direct drive (QDD) actuation (i.e., a high torque density motor with low ratio gear).
Proceedings ArticleDOI
Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors
TL;DR: This paper presents a portable inertial measurement unit (IMU)-based motion sensing system and proposed an adaptive gait phase detection approach for non-steady state walking and multiple activities monitoring and demonstrates that the sensing suit can not only detect the gait status in any transient state but also generalize to multiple activities.
Journal ArticleDOI
Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction
Shuangyue Yu,Jianfu Yang,Junxi Zhu,Christopher J. Visco,Farah Hameed,Joel Stein,Xianlian Alex Zhou,Hao Su +7 more
Proceedings ArticleDOI
Soft Physiology Sensors and Machine Learning to Enhance Spinal Cord Injury and Stroke Rehabilitation Outcomes in Home Settings
Tzu-Hao Huang,Jianfu Yang,Eljona Pushaj,Viktor Silvanov,Shuangyue Yu,Xiaolong Yang,Hao Su,Shuo-Hsiu Chang,Gerard E. Francisco +8 more
TL;DR: The design and fabrication of a textilebased soft Electromyography (EMG) sensor and machinelearning-based methods to detect muscle spasticity are presented and has the potential to enable home-based rehabilitation and encourage more manipulation for function ADLs and independence in SCI and stroke.