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Our daily observations tell us that the delivery of social sentiments and emotions differs between strangers and romantic partners. This work explores how relationship status influences our delivery and perception of social touches and emotions, by evaluating the physics of contact interactions. In a study with human participants, strangers and romantically involved touchers delivered emotional messages to receivers’ forearms. Physical contact interactions were measured using a customized 3D tracking system. The results indicate that strangers and romantic receivers recognize emotional messages with similar accuracy, but with higher levels of valence and arousal between romantic partners. Further investigation into the contact interactions which underlie the higher levels of valence and arousal reveals that a toucher tunes their strategy with their romantic partner. For example, when stroking, romantic touchers use velocities preferential to C-tactile afferents, and maintain contact for longer durations with larger contact areas. Notwithstanding, while we show that relationship intimacy influences the deployment of touch strategies, such impact is relatively subtle compared to distinctions between gestures, emotional messages, and individual preferences. 

Across a plethora of social situations, we touch others in natural and intuitive ways to share thoughts and emotions, such as tapping to get one’s attention or caressing to soothe one’s anxiety. A deeper understanding of these human-to-human interactions will require, in part, the precise measurement of skin-to-skin physical contact. Among prior efforts, each measurement approach exhibits certain constraints, e.g., motion trackers do not capture the precise shape of skin surfaces, while pressure sensors impede skin-to-skin contact. In contrast, this work develops an interference-free 3D visual tracking system using a depth camera to measure the contact attributes between the bare hand of a toucher and the forearm of a receiver. The toucher’s hand is tracked as a posed and positioned mesh by fitting a hand model to detected 3D hand joints, whereas a receiver’s forearm is extracted as a 3D surface updated upon repeated skin contact. Based on a contact model involving point clouds, the spatiotemporal changes of hand-to-forearm contact are decomposed as six, high-resolution, time-series contact attributes, i.e., contact area, indentation depth, absolute velocity, and three orthogonal velocity components, together with contact duration. To examine the system’s capabilities and limitations, two types of experiments were performed. First, to evaluate its ability to discern human touches, one person delivered cued social messages, e.g., happiness, anger, sympathy, to another person using their preferred gestures. The results indicated that messages and gestures, as well as the identities of the touchers, were readily discerned from their contact attributes. Second, the system’s spatiotemporal accuracy was validated against measurements from independent devices, including an electromagnetic motion tracker, sensorized pressure mat, and laser displacement sensor. While validated here in the context of social communication, this system is extendable to human touch interactions such as maternal care of infants and massage therapy.