Linking Haptic Parameters to the Emotional Space for Mediated Social Touch

Frontiers in Computer Science Journal


Social touch is essential for creating and maintaining strong interpersonal bonds amongst humans. However, when distance separates users, they often rely on voice and video communication technologies to stay connected with each other, and the lack of tactile interactions between users lowers the quality of the social interactions. In this research, we investigated haptic patterns to communicate five tactile messages comprising of four types of social touch (high five, handshake, caress, and asking for attention) and one physiological signal (the pulse of a heartbeat), delivered on the hand through a haptic glove. Since social interactions are highly dependent on their context, we conceived two interaction scenarios for each of the five tactile messages, conveying distinct emotions being spread across the circumplex model of emotions. We conducted two user studies: in the first one participants tuned the parameters of haptic patterns to convey tactile messages in each scenario, and a follow up study tested naïve participants to assess the validity of these patterns. Our results show that all haptic patterns were recognized above chance level, and the well-defined parameter clusters had a higher recognition rate, reinforcing the hypothesis that some social touches have more universal patterns than others. We also observed parallels between the parameters’ levels and the type of emotions they conveyed based on their mapping in the circumplex model of emotions.


Latest Publications

Sustainable AI: Environmental Implications, Challenges and Opportunities

Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Max Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood

MLSys - 2022

Looper: an end-to-end ML platform for product decisions

Igor L. Markov, Hanson Wang, Nitya Kasturi, Shaun Singh, Mia Garrard, Yin Huang, Sze Wai Yuen, Sarah Tran, Zehui Wang, Igor Glotov, Tanvi Gupta, Peng Chen, Boshuang Huang, Xiaowen Xie, Michael Belkin, Sal Uryasev, Sam Howie, Eytan Bakshy, Norm Zhou

KDD - 2022