Wanli Guan

Papers

Total Citations

H-Index

About

Dr. Wanli Guan is a rising researcher in biomechanics and human motion analysis, with a focus on intelligent sensing systems for locomotion. Their most cited work, "Gait phase recognition of multi-mode locomotion based on multi-layer perceptron for the plantar pressure measurement system" (2023), has already garnered 9 citations, signaling early impact in the field. This paper introduces a novel approach using multi-layer perceptron (MLP) neural networks to accurately classify gait phases across diverse locomotion modes—such as walking, running, and stair climbing—by analyzing plantar pressure data. Dr. Guan’s contribution lies in bridging machine learning with wearable sensor technology, enabling more precise, real-time monitoring of human movement. This work holds promise for applications in rehabilitation, prosthetics, and sports science, where understanding gait dynamics is critical. By demonstrating how deep learning can enhance the interpretation of pressure patterns, Dr. Guan is paving the way for smarter, adaptive assistive devices. Their research reflects a growing trend toward data-driven, personalized biomechanical analysis, making them a notable emerging voice in the intersection of artificial intelligence and human motion.