Dual-channel cascade pose estimation network trained on infrared thermal image and groundtruth annotation for real-time gait measurement

Real-time spatiotemporal parameter measurement for gait analysis is challenging. Previous techniques for 3D motion analysis, such as inertial measurement units, marker based motion analysis or the use of depth cameras, require expensive equipment, highly skilled staff and limits feasibility for sustainable applications. In this paper a dual-channel cascaded network to perform contactless real-time 3D human pose estimation using a single infrared thermal video as an input is proposed. An algorithm to calculate gait spatiotemporal parameters is presented by tracking estimated joint locations. Additionally, a training dataset composed of infrared thermal images and groundtruth annotations has been developed. The annotation represents a set of 3D joint locations from infrared optical trackers, which is considered to be the gold standard in clinical applications. On the proposed dataset, our pose estimation framework achieved a 3D human pose mean error of below 21 mm and outperforms state-of-the-art methods. The results reveal that the proposed system achieves competitive skeleton tracking performance on par with the other motion capture devices and exhibited good agreement with a marker-based three-dimensional motion analysis system (3DMA) over a range of spatiotemporal parameters. Moreover, the process is shown to distinguish differences in over-ground gait parameters of older adults with and without Hemiplegia's disease. We believe that the proposed approaches can measure selected spatiotemporal gait parameters and could be effectively used in clinical or home settings.