Physiotherapy aims to improve the physical functionality of people, seeking to mitigate the disabilities caused by any injury, disorder or disease. In this context, several computational technologies have been developed in order to support the rehabilitation process, such as the end-user adaptable technologies. These technologies allow the physiotherapist to adapt applications and create activities with personalized characteristics according to the preferences and needs of each patient. This thesis proposes a low-cost approach based on interactive machine learning (iML) that aims to help physiotherapists to create personalized activities for their patients easily and without the need for software coding, from just a few examples in RGB video (captured by a digital video camera). To this end, we take advantage of pose estimation based on deep learning to track, in real time, the key joints of the human body from image data. This data is processed as time series using the Dynamic Time Warping algorithm in conjunction with the K-Nearest Neighbors algorithm to create a machine learning model. Additionally, we use an anomaly detection algorithm in order to automatically assess movements. The architecture of our approach has two modules: one for the physiotherapist to present personalized examples from which the system creates a model to recognize these movements; another to the patient performs personalized movements while the system evaluates the patient. We assessed the usability of our system with physiotherapists from five rehabilitation clinics. In addition, experts have clinically evaluated our machine learning model. The results indicate that our approach contributes to automatically assessing patients movements without direct monitoring by the physiotherapist, in addition to reducing the specialist s time required to train an adaptable system.