Publications
Publications
Scientific Research Publications
Kayla Matheus, Marynel Vázquez, and Brian Scassellati. 2025. Ommie: The Design and Development of a Social Robot for Anxiety Reduction. J. Hum.-Robot Interact. 14, 2, Article 31 (June 2025), 34 pages. https://doi.org/10.1145/3706122
Abstract: This article discusses the design, development, and evaluation of Ommie, a novel socially assistive robot that supports deep breathing practices for the purposes of anxiety reduction. Research has shown that practicing deep breathing (breathing while extending one’s inhales, holds, and exhales) has a strong capacity to calm the autonomic nervous system and reduce anxiety. The robot’s primary function is to guide users through a series of deep breaths by way of haptic interactions and audio cues. We utilized a user-centered design approach and present our design methodology in addition to core decisions across robot morphology, tactility, and interactivity. As reported in prior work, the final robot prototype was tested with a two-cohort usability study (n = 43) at a local university wellness center, including participants with anxiety and those with varying levels of experience with deep breathing. Interacting with Ommie resulted in a significant reduction in STAI-6 anxiety measures across all participants, who also found the robot intuitive, approachable, and engaging. Participants also reported feelings of focus and companionship when using the robot, often elicited by the haptic interaction. This article describes how our design process and design goals contributed to these results showing Ommie’s capacity for supporting those with anxiety. Our work also serves as an example of how researchers can design robots for behavioral practices for mental health.
Kayla Matheus, Ellie Mamantov, Marynel Vázquez, and Brian Scassellati. 2023. Deep Breathing Phase Classification with a Social Robot for Mental Health. In Proceedings of the 25th International Conference on Multimodal Interaction (ICMI '23). Association for Computing Machinery, New York, NY, USA, 153–162. https://doi.org/10.1145/3577190.3614173
Abstract: Social robots are in a unique position to aid mental health by supporting engagement with behavioral interventions. One such behavioral intervention is the practice of deep breathing, which has been shown to physiologically reduce symptoms of anxiety. Multiple robots have been recently developed that support deep breathing, but none yet implement a method to detect how accurately an individual is performing the practice. Detecting breathing phases (i.e., inhaling, breath holding, or exhaling) is a challenge with these robots since often the robot is being manipulated or moved by the user, or the robot itself is moving to generate haptic feedback. Accordingly, we first present OMMDB: a novel, multimodal, public dataset made up of individuals performing deep breathing with an Ommie robot in multiple conditions of robot ego-motion. The dataset includes RGB video, inertial sensor data, and motor encoder data, as well as ground truth breathing data from a respiration belt. Our second contribution features experimental results with a convolutional long-short term memory neural network trained using OMMDB. These results show the system’s ability to be applied to the domain of deep breathing and generalize between individual users. We additionally show that our model is able to generalize across multiple types of robot ego-motion, reducing the need to train individual models for varying human-robot interaction conditions.
Kayla Matheus. 2023. Socially Assistive Robotics for Anxiety Reduction. In Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (HRI '23). Association for Computing Machinery, New York, NY, USA, 739–741. https://doi.org/10.1145/3568294.3579970
Abstract: Prior work in HRI for domains such as exercise, rehabilitation, and autism has shown how socially assistive robots (SARs) can successfully support behavioral practices. Applying these insights to mental health is an opportunity to support a large and growing population that is actively struggling. My research investigates utilizing SARs for supporting the therapeutic behavior of deep breathing for anxiety reduction. My prior work to date has focused on the design affordances required for an anxious population through the development of a new, haptically-based robot, Ommie. Future work explores how SARs for anxiety-reducing behaviors can maximize long-term, in-the-wild use through haptic interactions, perception technologies, and personalized motivational mechanisms.
K. Matheus, M. Vázquez and B. Scassellati, "A Social Robot for Anxiety Reduction via Deep Breathing," 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Napoli, Italy, 2022, pp. 89-94, doi: 10.1109/RO-MAN53752.2022.9900638.
Abstract: In this paper, we introduce Ommie, a novel robot that supports deep breathing practices for the purposes of anxiety reduction. The robot’s primary function is to guide users through a series of extended inhales, exhales, and holds by way of haptic interactions and audio cues. We present core design decisions during development, such as robot morphology and tactility, as well as the results of a usability study in collaboration with a local wellness center. Interacting with Ommie resulted in a significant reduction in STAI-6 anxiety measures, and participants found the robot intuitive, approachable, and engaging. Participants also reported feelings of focus and companionship when using the robot, often elicited by the haptic interaction. These results show promise in the robot’s capacity for supporting mental health.
K. Matheus and A. M. Dollar, "Benchmarking grasping and manipulation: Properties of the Objects of Daily Living," 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 2010, pp. 5020-5027, doi: 10.1109/IROS.2010.5649517. keywords: {Force;Friction;Grasping;Humans;Object recognition;Mobile robots},
Abstract: This paper presents a number of concepts related to benchmarking and evaluation of grasping and manipulation. A set of “Objects of Daily Living” based on a review of common domestic objects for manipulation as identified from sources in the literature is put forward, along with the physical properties of sample objects in those categories. Next, an experimental evaluation of the coefficient of static friction between these objects and a number of common household surfaces is performed. A key failure mode in unstructured object grasping occurs when the manipulator applies large contact forces that move the object out of grasp range. These results therefore give insight into the likelihood of a target object remaining in place to be successfully grasped in the presence of contact forces from the robot arm. This paper also presents a new classification of the Activities of Daily Living (ADLs), putting forth a standard categorization for the application of robotics in human environments. These topics and results have a number of uses related to benchmarking and performance evaluation in robotic manipulation, assistive technology, and prosthetics.
News Articles
Fast Company - “This Cute Little Robot is Designed to Help You Build Any Habit”
Huffington Post - “You Can’t Wear It, But This ‘MOTI’ Device Could Change Your Life”
Engadget - “This adorable desk gadget motivates you like Pavlov's bell”
Tech Crunch - “MOTI wants to help build positive habits with a little glowing robot”
Inc. - “Need to Develop a Healthy Habit? Meet the Robot That Can Coach You”