Abstract 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.
