This study aimed at the estimation of the 3D lower-limb joint kinematics during a sit-to-stand and a squat exercises using a new affordable motion capture system. Utilizing a reduced number of affordable visual inertial measurement units and markerless data, the study investigates the performance of these modalities in comparison to a reference stereophotogrammetric system. Indeed, markerless data are easily accessible from an RGB image, but few studies investigated their accuracy to perform inverse kinematics for rehabilitation exercise. Thus, ankle, knee, and hip joint center positions and joint angles were obtained through a novel sliding windows inverse kinematics algorithm. Joint angles were estimated with an average error of 8.1deg when inertial and visual data were used and 13.4deg when using solely markerless data. Joint center positions also displayed an estimation error reduced by 2.5 times when using the proposed approach over purely markerless data. These results, associated with the real affordability and ease of use of the proposed system open the door to future field applications in both rehabilitation and sport.