Vision-Based Classification of Social Gestures in Videochat Sessions
This addresses the problem of enabling mediated social touch in applications like ShareTable and SqueezeBands for users in remote communication, but it is incremental as it builds on existing systems and highlights limitations.
The paper tackled the problem of automatically recognizing social gestures like handshakes and hugs in videochat sessions using vision-based classification, achieving recognition accuracy for each gesture but noting that significant future work is needed for practical feasibility.
This paper describes the design and evaluation of the vision-based classification of social gestures, such as handshake, hug, high-five, etc. This is a component of the mediated social touch systems, which can be incorporated into ShareTable and SqueezeBands system to achieve automated gestures recognition and transmission of the touch between the users in real time. The results from our pilot study show the recognition accuracy of each gestures, and they indicate that significant future work is necessary to improve its practical feasibility in the mediated social touch applications.