In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

Virtual Reality (VR) has had significant advances in rehabilitation, due to the gamification of cognitive activities that facilitate treatment. On the other hand, Immersive Virtual Reality (IVR) produces outstanding results due to the interactive features with the user. This work introduces a VR application for memory rehabilitation by walking through a maze and using the Oculus Go head-mounted display (HMD) technology. The mechanics of the game require memorizing geometric shapes while the player progresses in two modes, autonomous or manual, with two levels of difficulty depending on the number of elements to remember. The application is developed in the Unity 3D video game engine considering the optimization of computational resources to improve the performance in the processing and maintaining adequate benefits for the user, while the generated data is stored and sent to a remote server. The maze task was assessed with 29 subjects in a controlled environment. The obtained results show a significant correlation between participants’ response accuracy in both the maze task and a face–pair test. Thus, the proposed task is able to perform memory assessments.

The COVID-19 pandemic has changed people’s lives and the way in which certain services are provided. Such changes are not uncommon in healthcare services and they will have to adapt to the new situation by increasing the number of services remotely offered. Limited mobility has resulted in interruption of treatments that traditionally have been administered through face-to-face modalities, especially those related to cognitive impairments. In this telerehabilitation approach, both the patient and the specialist physician enter a virtual reality (VR) environment where they can interact in real time through avatars. A spaced retrieval (SR) task is implemented in the system to analyze cognitive performance. An experimental group (n = 20) performed the SR task in telerehabilitation mode, whereas a control group (n = 20) performed the SR task through a traditional face-to-face mode. The obtained results showed that it is possible to carry out cognitive rehabilitation processes through a telerehabilitation modality in conjunction with VR. The cost-effectiveness of the system will also contribute to making healthcare systems more efficient, overcoming both geographical and temporal limitations.