Social interactions are known to influence human health. To reduce the negative impact that some behaviors could have on our health, real-time monitoring of social interactions is desired. This paper presents a study of sensor modalities and the design of a multi-sensor framework that allows the study and real-time analysis of both in-person and virtual social interaction environments. The framework consists of three multi-sensor nodes and a central unit. Results show validation of the variety of sensor data collected from a single sensor node and behavioral information that can be identified due to data synchronization from multiple sensor nodes.