Jiaying Tan

About

Jiaying Tan is a researcher at the forefront of embedded systems and assistive robotics, with a focus on hardware acceleration for prosthetic technologies. Her work centers on developing efficient, on-board machine learning solutions for robotic transtibial prostheses, enabling real-time adaptation to user gait patterns. Her most-cited paper, "Implementing a SoC-FPGA Based Acceleration System for On-Board SVM Training for Robotic Transtibial Prostheses" (2018, 8 citations), introduces a novel system-on-chip with field-programmable gate array (SoC-FPGA) architecture that accelerates support vector machine (SVM) model training directly on the prosthetic device. This contribution addresses a critical challenge in prosthetics: the need for low-latency, energy-efficient computation to personalize assistive responses without relying on external processing. By demonstrating a hardware prototype that implements SVM training algorithms in real time, Tan’s work bridges the gap between advanced machine learning and practical, wearable robotics. Her research holds promise for improving mobility and quality of life for amputees, showcasing how custom hardware can transform assistive technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   Implementing a SoC-FPGA Based Acceleration System for On-Board SVM Training for Robotic Transtibial Prostheses

   8 citations · 2018

Key Collaborators

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