Kunal Gupta

Kunal Gupta

PhD Student

Kunal Gupta is a PhD candidate at the Empathic Computing Laboratory, at the University of Auckland, New Zealand, under the supervision of Prof. Mark Billinghurst. His research revolves around emotion recognition and representation in VR/AR  using physiological sensing and contextual information.

Prior to this, he worked as a User Experience Researcher at Google and as an Interaction Designer at a startup in India, for a total of around 5 years of industry experience.  He obtained a master’s degree in Human Interface Technology (MHIT) from the HITLab NZ at the University of Canterbury under the supervision of Prof Mark Billinghurst in 2015. As part of his master’s degree, he conducted some of the first research into the use of eye-gaze to enhance remote collaboration in wearable AR systems.

For more details about his research work, check out his website: kunalgupta.in

Publications

  • Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality
    Kunal Gupta, Ryo Hajika, Yun Suen Pai, Andreas Duenser, Martin Lochner, Mark Billinghurst

    K. Gupta, R. Hajika, Y. S. Pai, A. Duenser, M. Lochner and M. Billinghurst, "Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality," 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Atlanta, GA, USA, 2020, pp. 756-765, doi: 10.1109/VR46266.2020.1581313729558.

    BibTexLinksAbstract
    @inproceedings{gupta2020measuring,
    title={Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality},
    author={Gupta, Kunal and Hajika, Ryo and Pai, Yun Suen and Duenser, Andreas and Lochner, Martin and Billinghurst, Mark},
    booktitle={2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)},
    pages={756--765},
    year={2020},
    organization={IEEE}
    }
    View: https://ieeexplore.ieee.org/abstract/document/9089632
    With the advancement of Artificial Intelligence technology to make smart devices, understanding how humans develop trust in virtual agents is emerging as a critical research field. Through our research, we report on a novel methodology to investigate user’s trust in auditory assistance in a Virtual Reality (VR) based search task, under both high and low cognitive load and under varying levels of agent accuracy. We collected physiological sensor data such as electroencephalography (EEG), galvanic skin response (GSR), and heart-rate variability (HRV), subjective data through questionnaire such as System Trust Scale (STS), Subjective Mental Effort Questionnaire (SMEQ) and NASA-TLX. We also collected a behavioral measure of trust (congruency of users’ head motion in response to valid/ invalid verbal advice from the agent). Our results indicate that our custom VR environment enables researchers to measure and understand human trust in virtual agents using the matrices, and both cognitive load and agent accuracy play an important role in trust formation. We discuss the implications of the research and directions for future work.
  • NeuralDrum: Perceiving Brain Synchronicity in XR Drumming
    Y. S. Pai, Ryo Hajika, Kunla Gupta, Prasnth Sasikumar, Mark Billinghurst.

    Pai, Y. S., Hajika, R., Gupta, K., Sasikumar, P., & Billinghurst, M. (2020). NeuralDrum: Perceiving Brain Synchronicity in XR Drumming. In SIGGRAPH Asia 2020 Technical Communications (pp. 1-4).

    BibTexLinksAbstract
    @incollection{pai2020neuraldrum,
    title={NeuralDrum: Perceiving Brain Synchronicity in XR Drumming},
    author={Pai, Yun Suen and Hajika, Ryo and Gupta, Kunal and Sasikumar, Prasanth and Billinghurst, Mark},
    booktitle={SIGGRAPH Asia 2020 Technical Communications},
    pages={1--4},
    year={2020}
    }
    View: https://dl.acm.org/doi/abs/10.1145/3410700.3425434
    Brain synchronicity is a neurological phenomena where two or more individuals have their brain activation in phase when performing a shared activity. We present NeuralDrum, an extended reality (XR) drumming experience that allows two players to drum together while their brain signals are simultaneously measured. We calculate the Phase Locking Value (PLV) to determine their brain synchronicity and use this to directly affect their visual and auditory experience in the game, creating a closed feedback loop. In a pilot study, we logged and analysed the users’ brain signals as well as had them answer a subjective questionnaire regarding their perception of synchronicity with their partner and the overall experience. From the results, we discuss design implications to further improve NeuralDrum and propose methods to integrate brain synchronicity into interactive experiences.