🧑‍💻️About Me
I build artificail emotional intelligence for social robots and virtual agents to make them understand, adaptively respond, and influence human behavior in socially and emotionally meaningful ways. To achieve this goal, my work connects three fields: robotics, artificial intelligence, and human science.
If you are interested in this, feel free to reach out. I welcome opportunities for collaboration, discussion, and innovation.
- Social Robotics
- Human-Robot Interaction
- Affective Computing
- Robot Learning from Demonstration
- Humanoid
Ph.D. Candidate in Electrical and Computer Engineering
Oakland University (MI-USA)
M.Sc. Mechatronics Engineering
University of Tehran and Azad University of Qazvin (Iran)
B.Sc. Robotics Engineering
Hamedan University of Technology (Iran)
Effect of robotic delivery of physical activity and fall prevention exercise in older adults: a pilot cohort study
Abstract:
Introduction
The high prevalence of falls, lack of stability and balance, and general physical deconditioning are concerning issues for longevity and quality of life for adults aged 65 years and older. Although supervised delivery of the Otago Exercise Program (OEP) has demonstrated evidence of effectiveness in reducing fall risk of older adults, opportunities for ongoing unsupervised exercise performance are warranted. An option to facilitate exercise and performance of health behaviors may be via a social robot. The purpose of this study was to examine feasibility and initial outcomes of a robot-delivered fall prevention exercise program for community-dwelling older adults.
Emotionally Specific Backchanneling in Social Human-Robot Interaction and Human-Human Interaction
Abstract:
Backchanneling models, designed to enhance the interactive capabilities of robots, have primarily been trained on human-human interaction data. However, applying such data directly to social robots raises concerns due to dissimilarities in the way humans and robots exhibit verbal and nonverbal behaviors, particularly in the domain of emotional backchannels. This research aims to address this gap by conducting an exploratory study on the differences in human backchanneling behaviors during interactions with humans and social robots in various emotional contexts (e.g., happy and sad). Our findings reveal significant variations in emotionally specific backchannels between human-human and human-robot interactions under different emotional contexts. These results highlight the importance of designing backchanneling models that are tailored for human-robot interactions.
Robot-mediated Group Instruction for Children with ASD: A Pilot Study
Abstract:
Children diagnosed with autism spectrum disorder (ASD) typically work towards acquiring skills to participate in a regular classroom setting such as attending and appropriately responding to an instructor’s requests. Social robots have the potential to support children with ASD in learning group-interaction skills. However, the majority of studies that target children with ASD’s interactions with social robots have been limited to one-on-one interactions. Group interaction sessions present unique challenges such as the unpredictable behaviors of the other children participating in the group intervention session and shared attention from the instructor. We present the design of a robot-mediated group interaction intervention for children with ASD to enable them to practice the skills required to participate in a classroom. We also present a study investigating differences in children’s learning behaviors during robot-led and human-led group interventions over multiple intervention sessions. Results of this study suggests that children with ASD’s learning behaviors are similar during human and robot instruction. Furthermore, preliminary results of this study suggest that a novelty effect was not observed when children interacted with the robot over multiple sessions.
Balance Strategy for Human Imitation by a NAO Humanoid Robot
Abstract:
This paper presents an ankle-based balance strategy for a NAO humanoid robot while imitating the human motions. In this approach, first, an inverted pendulum model based on the computed Center of Mass (CoM) is introduced and then, the support polygon is computed for each double support and single support phases. Center of the support polygon is assumed as the reference for balance controller and Ground projection of the Center of Mass (GCoM) is considered as the balance criteria. Using ankle joints correction, GCoM is restricted to the center of the support polygon. In order to control the balance criteria a Proportional-Integral-Derivative (PID) controller is used. The coefficients are first estimated using Ziegler-Nichols method; then, they were tuned by considering advantages of the imitation process. Implementation of the proposed approach leads to a better result in preserving the balance of the robot in soft realtime imitation of human whole-body and quasi-static motions. The proposed approach is validated by performing simulation and practical tests on a NAO H-25 version 4 robot.