Social IoT has become a sensitive topic in the last years, mainly due to the attraction of social networks and the related digital activities amongst the population. These techniques are gaining even more importance in the current period, in which digital tools are the only ones allowed to maintain social distancing due to the COVID-19 restrictions. In order to aid patients and elderly people in-home healthcare context, this article explores the usage of facial patient images and emotional detection. In this regard, a Social IoT approach is proposed, which is based on a camera connected home, allowing medical examinations at a distance by keeping posted the preferred contacts of the patient. A facial expression analysis is done to infer the patient’s emotional state, thus communicating to the doctor and the emergency contacts any change in the patient’s state (pain, suffering, etc.). The proposed facial expression recognition system consists of three main steps:during the image preprocessing phase, face detection and normalization are performed; the feature extraction process involves the computation of discriminative patterns using the Spatial Pyramid Technique; finally, an expression recognition model is built using a multi-class linear Support Vector Machine classifier. The performance of the proposed system has been tested on two challenging benchmarks for facial expression recognition, namely KDEF and GENKI-4K, which show that the proposed system overcomes state-of-the-art methods.