HUMAN–MACHINE CO-ADAPTATION · ASSISTIVE ROBOTICS
Helping people and intelligent machines learn and adapt together.
I am Hai Jiang (蒋 海), an Assistant Professor in the Nakamura Laboratory at The University of Electro-Communications (UEC), Tokyo, Japan. My long-term goal is to develop intelligent machines that understand human intention, adapt to individual needs, and expand what people can do in everyday life.
My research connects biosignal and behavior understanding, continual learning, robot sensing and control, and human-centered design. Building on my work in myoelectric prostheses, I investigate how people and machines can improve together through interaction, and how evidence from virtual environments and real-world use can guide safer, more useful robotic systems.
Research vision
A machine’s technical performance matters when it translates into what a person can actually do. I aim to connect understanding people, learning through use, and designing useful machines, with less calibration, less operating effort, and greater freedom for the user. Prosthetic hands are a central research platform; the broader questions extend to assistive devices and human–robot interaction.
Understanding intention and behavior
Connecting biosignals, time-series segmentation, and multimodal sensing to understand intended movements, ongoing actions, and their outcomes.
Personalization and continual learning
Studying how machines can adapt with limited individual data and learn from use while retaining acquired skills, alongside changes in the user's own abilities.
Safe physical interaction
Combining perception, compliant mechanisms, and shared control to support reliable grasping and manipulation while preserving the user's control.
Virtual evaluation and real-world design
Using simulation, immersive environments, and user evaluation to compare designs and training approaches, then test and refine them on physical systems.
Research foundations and current work
My background spans biomedical signal processing, machine learning, prosthetic-hand mechanisms, and real-time control. In the Nakamura Laboratory, time-series data segmentation and robot manipulation contribute to this broader effort to connect human intention, machine learning, and useful physical action.
Recent updates
Selected publications
Development of a Myoelectric Prosthetic Finger Mechanism with Passive Stability for Improving Grasping Performance
Hai Jiang, Yuya Nakamura, Yuki Inoue, Kaigen Go, Yusuke Yamanoi, Yoshiko Yabuki, Shunta Togo, and Hiroshi Yokoi.
Journal of the Robotics Society of Japan, 44(4), 421–424.
DOITF2AngleNet: Continuous Finger Joint Angle Estimation Based on Multidimensional Time–Frequency Features of sEMG Signals
Hai Jiang, Yusuke Yamanoi, Peiji Chen, Xin Wang, Shixiong Chen, Xu Yong, Guanglin Li, Hiroshi Yokoi, and Xiaobei Jing.
Biomedical Signal Processing and Control, 107, 107833.
DOIPostural Synergy Analysis of Continuous Hand Movements for Application to Prosthetic Hands
Hai Jiang, Yusuke Yamanoi, Peiji Chen, Xu Yong, Guanglin Li, Hiroshi Yokoi, and Xiaobei Jing.
17th CISP-BMEI, 1–6.
DOISee the complete selected publication list →
COLLABORATION
Interested in machines that learn with people?
I welcome conversations with researchers, clinicians, engineers, and students working on related problems.