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Stroke patients commonly suffered from motor impairments, and gait analysis is crucial for the effective guidance of poststroke rehabilitation. However, most existing 3D gait analysis systems require the attachment of optical markers to the body, and the systems are costly and spatially intensive. These restricted the use of the systems in general hospitals and communities. To address this issue, this study developed an affordable and markerless system to examine the gait characteristics of stroke patients, which was constructed by depth camera of Microsoft Azure Kinect, instrumented walkway, and analysis software. Spatiotemporal and kinematic parameters were measured directly, while kinetic parameters were derived via inverse dynamics using a 14-segment human body model. Sixteen healthy individuals and eight stroke patients were recruited for tests. The results showed that due to weakened lower limb strength in stroke patients, their peak joint moments, joint angles, and joint angular velocities decreased compared with healthy individuals. Consequently, stroke patients walked with reduced stride length, cadence, and single support time. Furthermore, the differences between the paretic and non-paretic sides of stroke patients were primarily observed at the knee joint (peak joint angle, angular velocity, and moment were significantly smaller compared to the non-paretic side) during swing phase. This suggests that rehabilitation training for stroke patients should place importance on knee joint. These findings demonstrate the significant potential of the present system in the gait analysis for stroke patients.
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