Human gait analysis is an essential indicator for physical and neuroglial health of an individual. Recent developments in deep-learning approaches to computer vision make possible new techniques for body segment and joint detection from photos and video frames. In this thesis, we propose a deep learning approach for non-invasive video-based gait analysis using two RGB cameras that would be suitable for routine gait monitoring in senior care and rehabilitation centers. Due to modularity and the low cost of implementation, it is considered an affordable solution for such centers. Furthermore, since the solution does not require any markers or sensors to be worn, it is a pervasive and easy method for daily usage. Our proposed deep-learning approach starts by calibrating both the intrinsic and extrinsic parameters of the cameras. Next, video streams captured from two RGB cameras are used as input, and OpenPose and HyperPose deep-learning frameworks are used to localize the main body key points, including the joints and skeleton based on Body 25 and COCO models, respectively. The 2D parameter outputs from the frameworks are triangulated into 3D vector spaces for further analysis. In order to reduce the noises in our data, we applied median and dual pass butter worth filters to the data. Finally gait parameters has been extracted measured and compared to the manually evaluated ground truth data which has been capture via manual measurement of a domain expert. The approach was evaluated in a laboratory setting similar to an institutional hallway in five types of trials: walking back and forth in a straight line while turning out of frame, walking back and forth in a straight line while turning in frame, circular walking, walking with a cane and a walker. The method brings promising results compared to more expensive and restrictive approaches that use up to 16 cameras and require markers or sensors.

Pulse wave velocity (PWV) and augmentation index (AI) are independent predictors of cardiovascular health. However, the comparability of multiple oscillometric modalities currently available for their assessment was not studied in detail. In the present study, we aimed to evaluate the relationship between indices of arterial stiffness assessed by diastolic and suprasystolic oscillometry. In total, 56 volunteers from the general population (23 males; median age 70 years [interquartile range: 65–72 years]) were recruited into observational feasibility study to evaluate the carotid-femoral/aortic PWV (cf/aoPWV), brachial-ankle PWV (baPWV), and AI assessed by 2 devices: Vicorder (VI) applying diastolic, right-sided oscillometry for the determination of all 3 indices, and Vascular explorer (VE) implementing single-point, suprasystolic brachial oscillometry (SSBO) pulse wave analysis for the assessment of cfPWV and AI. Within- and between-device correlations of measured parameters were analyzed. Furthermore, agreement of repeated measurements, intra- and inter-observer concordances were determined and compared for both devices. In VI, both baPWVand cfPWVinter-correlatedwell and showed good level of agreement with bilateral baPWVmeasured byVE (baPWV[VI]– baPWV[VE]R: overall concordance correlation coefficient [OCCC]¼0.484, mean difference¼1.94 m/s; cfPWV[VI]–baPWV[- VE]R: OCCC¼0.493, mean difference¼1.0m/s). In contrast, SSBO derived aortic PWA (cf/aoPWA[VE]) displayed only weak correlation with cfPWV(VI) (r¼0.196; P¼0.04) and ipsilateral baPWV (cf/ aoPWV[VE]R–baPWV[VE]R: r¼0.166; P¼0.08). cf/aoPWA(VE) correlated strongly with AI(VE) (right-sided: r¼0.725, P<0.001). AI exhibited marginal between-device agreement (right-sided: OCCC¼ 0.298, mean difference: 6.12%). All considered parameters showed good-to-excellent repeatability giving OCCC > 0.9 for 2-point-PWV modes and right-sided AI(VE). Intra- and inter-observer concordances were similarly high except for AI yielding a trend toward better reproducibility in VE (interobserver–OCCC[VI] vs [VE]¼0.774 vs 0.844; intraobserver OCCC[VI] vs [VE]¼0.613 vs 0.769). Both diastolic oscillometry-derived PWV modes, and AI measured either with VI or VE, are comparable and reliable alternatives for the assessment of arterial stiffness. Aortic PWV assessed by SSBO in VE is not related to the corresponding indices determined by traditional diastolic oscillometry.