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Updated: Aug 19, 2026
3D Kinematic Gait Analysis for Preclinical Studies in Rodents
Published on: August 3, 2019
GaitIntent: A Kinematic Dataset Focusing on Steady-State to Transitional Locomotion for Lower-Limb Motion
Benyue Su1, Baoqian Wang2,3, Zhixin Ge4
1School of Artificial Intelligence, Tongling University, Tongling, Anhui, 244061, China. bysu@aqnu.edu.cn.
None:
Motion intent recognition (MIR), the real-time interpretation of user movement, is crucial for advanced motion rehabilitation. MIR enables responsive control of prostheses and exoskeletons, enhancing mobility for lower-limb impaired individuals. Most existing datasets prioritize steady-state locomotion, often overlooking complex transitional movements critical in daily activities, which limits anticipatory control development. We introduce GaitIntent, a novel kinematic dataset. Inspired by gait symmetry, it uses sound limb kinematics for predictive MIR for the affected side-an approach less comprehensively covered. We collected multi-node Inertial Measurement Units (IMUs) data from the thigh, shank, and foot of 11 subjects' sound limbs performing 13 daily activities, emphasizing eight distinct transitional movements. GaitIntent provides a valuable resource for developing low-latency, robust algorithms for wearable rehabilitation and human-centric control systems.
