Papers
8
Total Citations
94
H-Index
5
About
Chunzhi Yi is a researcher whose work sits at the intersection of human motion analysis, wearable sensing, and human-robot interaction. Drawing on expertise in inertial measurement units (IMUs), surface electromyography (sEMG), and machine learning, Yi has made meaningful contributions to how machines perceive, interpret, and respond to human movement. Yi's most influential work — an improved complementary filter for three-dimensional body orientation estimation (2018, 44 citations) — advanced the accuracy of IMU-based motion tracking by dynamically fusing quaternion computations, with applications spanning robotics, rehabilitation, and navigation. Building on this foundation, subsequent research tackled critical challenges in joint angle estimation, including a novel method for unifying reference frames across multiple IMUs, strengthening the reliability of biomechanical and clinical assessments. Yi has also pushed forward sEMG-based control interfaces for wearable robots, investigating how muscle fatigue degrades classifier performance and proposing training strategies to counter it. A 2023 end-to-end locomotion prediction algorithm — handling both discrete motion states and continuous joint kinematics across varied terrains — reflects Yi's broader ambition to enable adaptive assistive robotics. More recent explorations into vision-based robotic manipulation and cognitive load's influence on human-robot trust signal an expanding research agenda with considerable promise for the field.
Research Focus
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Top Papers
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