Dr. Barrett Ens is a Postdoctoral Research Fellow with experience designing interfaces for head-worn displays, aimed at pushing wearable capabilities beyond current micro-interactions. By incorporating new interactive devices and techniques with Augmented Reality, his previous work explored interfaces that support multi-view, analytic tasks for in-situ mobile workers and other everyday users. Along with additional experience in collaborative and social computing, 3D immersive interfaces, and visual analytics, Barrett brings his skills to the Empathic Computing Lab with hopes of creating rich collaborative experiences that support intuitive interaction with high-level cognitive tasks.
Barrett completed his PhD in Computer Science at the University of Manitoba under the supervision of Pourang Irani. Previously, he received a BSc in Computer Science with first class honours from the University of Manitoba and a BMus from the University of Calgary, where he studied classical guitar and specialized in Music Theory. In 2015 and 2016, Barrett completed a pair of research internships at Autodesk Research in Toronto under the supervision of Tovi Grossman and Fraser Anderson. In 2012, he joined Michael Haller at the Media Interaction Lab in Hagenberg, Austria in a summer exchange program. He has contributed to the program committees for the Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) and the ACM Symposium on Spatial User Interaction (SUI).
Mini-Me is an adaptive avatar for enhancing Mixed Reality (MR) remote collaboration between a local Augmented Reality (AR) user and a remote Virtual Reality (VR) user. The Mini-Me avatar represents the VR user’s gaze direction and body gestures while it transforms in size and orientation to stay within the AR user’s field of view. We tested Mini-Me in two collaborative scenarios: an asymmetric remote expert in VR assisting a local worker in AR, and a symmetric collaboration in urban planning. We found that the presence of the Mini-Me significantly improved Social Presence and the overall experience of MR collaboration.
Head and eye movement can be leveraged to improve the user’s interaction repertoire for wearable displays. Head movements are deliberate and accurate, and provide the current state-of-the-art pointing technique. Eye gaze can potentially be faster and more ergonomic, but suffers from low accuracy due to calibration errors and drift of wearable eye-tracking sensors. This work investigates precise, multimodal selection techniques using head motion and eye gaze. A comparison of speed and pointing accuracy reveals the relative merits of each method, including the achievable target size for robust selection. We demonstrate and discuss example applications for augmented reality, including compact menus with deep structure, and a proof-of-concept method for on-line correction of calibration drift.
Barrett Ens, Aaron Quigley, Hui-Shyong Yeo, Pourang Irani, Thammathip Piumsomboon, and Mark Billinghurst. 2018. Counterpoint: Exploring Mixed-Scale Gesture Interaction for AR Applications. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA '18). ACM, New York, NY, USA, Paper LBW120, 6 pages. DOI: https://doi.org/10.1145/3170427.3188513
Mikko Kytö, Barrett Ens, Thammathip Piumsomboon, Gun A. Lee, and Mark Billinghurst. 2018. Pinpointing: Precise Head- and Eye-Based Target Selection for Augmented Reality. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Paper 81, 14 pages. DOI: https://doi.org/10.1145/3173574.3173655
Lynda Gerry, Barrett Ens, Adam Drogemuller, Bruce Thomas, and Mark Billinghurst. 2018. Levity: A Virtual Reality System that Responds to Cognitive Load. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA '18). ACM, New York, NY, USA, Paper LBW610, 6 pages. DOI: https://doi.org/10.1145/3170427.3188479
Herbert, B., Ens, B., Weerasinghe, A., Billinghurst, M., & Wigley, G. (2018). Design considerations for combining augmented reality with intelligent tutors. Computers & Graphics, 77, 166-182.
Herbert, B., Billinghurst, M., Weerasinghe, A., Ens, B., & Wigley, G. (2018, December). A generalized, rapid authoring tool for intelligent tutoring systems. In Proceedings of the 30th Australian Conference on Computer-Human Interaction (pp. 368-373). ACM.
Ens, B., Quigley, A. J., Yeo, H. S., Irani, P., Piumsomboon, T., & Billinghurst, M. (2017). Exploring mixed-scale gesture interaction. SA'17 SIGGRAPH Asia 2017 Posters.
Ens, B., Quigley, A., Yeo, H. S., Irani, P., & Billinghurst, M. (2017, November). Multi-scale gestural interaction for augmented reality. In SIGGRAPH Asia 2017 Mobile Graphics & Interactive Applications (p. 11). ACM.
We present a multi-scale gestural interface for augmented reality applications. With virtual objects, gestural interactions such as pointing and grasping can be convenient and intuitive, however they are imprecise, socially awkward, and susceptible to fatigue. Our prototype application uses multiple sensors to detect gestures from both arm and hand motions (macro-scale), and finger gestures (micro-scale). Micro-gestures can provide precise input through a belt-worn sensor configuration, with the hand in a relaxed posture. We present an application that combines direct manipulation with microgestures for precise interaction, beyond the capabilities of direct manipulation alone.
Thammathip Piumsomboon, Gun A. Lee, Jonathon D. Hart, Barrett Ens, Robert W. Lindeman, Bruce H. Thomas, and Mark Billinghurst. 2018. Mini-Me: An Adaptive Avatar for Mixed Reality Remote Collaboration. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Paper 46, 13 pages. DOI: https://doi.org/10.1145/3173574.3173620
Ens, B., Lanir, J., Tang, A., Bateman, S., Lee, G., Piumsomboon, T., & Billinghurst, M. (2019). Revisiting collaboration through mixed reality: The evolution of groupware. International Journal of Human-Computer Studies.
Piumsomboon, T., Lee, G. A., Irlitti, A., Ens, B., Thomas, B. H., & Billinghurst, M. (2019, April). On the Shoulder of the Giant: A Multi-Scale Mixed Reality Collaboration with 360 Video Sharing and Tangible Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (p. 228). ACM.
Piumsomboon, T., Lee, G. A., Ens, B., Thomas, B. H., & Billinghurst, M. (2018). Superman vs giant: a study on spatial perception for a multi-scale mixed reality flying telepresence interface. IEEE transactions on visualization and computer graphics, 24(11), 2974-2982.
Piumsomboon, T., Dey, A., Ens, B., Lee, G., & Billinghurst, M. (2019). The effects of sharing awareness cues in collaborative mixed reality. Front. Rob, 6(5).
Lee, Y., Piumsomboon, T., Ens, B., Lee, G., Dey, A., & Billinghurst, M. (2017, November). A gaze-depth estimation technique with an implicit and continuous data acquisition for OST-HMDs. In Proceedings of the 27th International Conference on Artificial Reality and Telexistence and 22nd Eurographics Symposium on Virtual Environments: Posters and Demos (pp. 1-2). Eurographics Association.
The rapid developement of machine learning algorithms can be leveraged for potential software solutions in many domains including techniques for depth estimation of human eye gaze. In this paper, we propose an implicit and continuous data acquisition method for 3D gaze depth estimation for an optical see-Through head mounted display (OST-HMD) equipped with an eye tracker. Our method constantly monitoring and generating user gaze data for training our machine learning algorithm. The gaze data acquired through the eye-tracker include the inter-pupillary distance (IPD) and the gaze distance to the real andvirtual target for each eye.
Piumsomboon, T., Day, A., Ens, B., Lee, Y., Lee, G., & Billinghurst, M. (2017, November). Exploring enhancements for remote mixed reality collaboration. In SIGGRAPH Asia 2017 Mobile Graphics & Interactive Applications (p. 16). ACM.