This study aims at developing and evaluating an affordable and user-friendly motion capture system for human upper limbs’ joint kinematics estimation. The objective is to provide quantitative assessments during the clinical evaluation of poststroke patients performing daily living activities. The proposed system is based on the simultaneous use of affordable inertial measurement units, and a set of augmented reality markers tracked with an affordable RGB camera. Two practical calibration processes were developed to calibrate the sensors modules and then to determine their location on body segments. Then, all measured quantities were fused into a constrained extended Kalman filter based on an upper limbs’ biomechanical model. The proposed system was validated with nine healthy volunteers performing five daily living activities. Joint angles estimated using the proposed affordable system were compared with a gold standard stereophotogrammetric system. The results showed a low average rms difference (2.7°) along with a high average correlation (0.87). Note to Practitioners —This article was motivated by the problem of assessing human upper limbs’ mobility during a rehabilitation process. Existing systems are often inaccurate and/or not affordable. This article suggests a new approach by combining measurements from inertial measurement units and augmented reality markers into an adaptive filter that is taking into account the kinematics model of the human arm and its limitation to filter out unfeasible solutions. The system is also making use of a new very practical calibration method not requiring any external equipment while remaining very affordable. Experimental results suggest that the proposed system is able to estimate more accurately than state-of-the-art joint angles of the upper limbs.