Social activities are a fundamental form of social interaction in our daily life. Current smart systems based on human-computer interaction (e.g. for security, safety, and healthcare applications) may significantly benefit and often require an understanding of users’ individual and group activities performed. Recent advancements in Wi-Fi signal analysis suggest that this pervasive communication infrastructure can also represent a convenient, non-invasive, contactless sensing method to detect human activities. In this paper, we propose a data-level fusion method based on Wi-Fi Channel State Information (CSI) analysis to recognize social activities (e.g., walking together) and gestures (e.g., hand-shaking) in an indoor environment. Our results show that off-the-shelf Wi-Fi devices can be effectively used as a contact-less sensing method for social activity recognition alternative to other approaches such as those based on computer vision and wearable sensors.