565 research outputs found
Introduction to Journal of Human-Robot Interaction Special Issue on Haptics in HRI: Cooperation and Communication
No full textJohn P. MacLean portrait
No full textPhotograph of Ohio author John P. MacLean (1848-1939). MacLean was born in Franklin, Ohio, and is remembered as a Universalist minister, historian and archaeologist. In addition to writings on Scottish history and the Shakers, his work included the books "A Manual of the Antiquity of Man" (1877), "The Mound Builders" (1879) and "Mastodon, Mammoth and Man" (1880)
John P. MacLean portrait
No full textPhotograph of Ohio author John P. MacLean (1848-1939). MacLean was born in Franklin, Ohio, and is remembered as a Universalist minister, historian and archaeologist. In addition to writings on Scottish history and the Shakers, his work included the books "A Manual of the Antiquity of Man" (1877), "The Mound Builders" (1879) and "Mastodon, Mammoth and Man" (1880)
Emulation of haptic feedback for manual interfaces
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.Includes bibliographical references (p. 329-339).by Karon E. MacLean.Ph.D
Estimation of isometric recruitment curves of electrically stimulated muscle
No full textThesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1988.Includes bibliographical references (leaves 199-201).by Karon E. MacLean.M.S
Season 1, Episode 10: Maclean
No full textMaclean. The name is synonymous with many things: great writing, fishing, and fire to name just a few. On this, the tenth and final episode of season one, author John Maclean joins the podcast, along with University of Montana researcher Brent Ruby and host Charlie Palmer to discuss South Canyon, the history of hotshots, and John’s current book project on the Yarnell Hill fire that killed nineteen Granite Mountain Hotshots.https://scholarworks.umt.edu/ontheline_podcasts/1009/thumbnail.jp
A recognition theorem for polynomial growth outer automorphisms of the free group
No full textFeighn and Handel’s recognition theorem for Out(F_n) provides invariants that canonically determine any forward rotationless outer automorphism of the free group. We ask to what extent those invariants can be extended to outer automorphisms with some periodic behavior. Many of the same constructions do not have natural analogs, in particular because of the possible lack of principal representatives in this setting. However, by restricting our attention to polynomial growth outer automorphisms and using train track technology, we are able to find a special set of lines in the free group that encode all the dynamical information of these non-forward rotationless maps.Ph. D.Includes bibliographical referencesIncludes vitaby By Gregory MacLean Schinke Fei
Feeling (key)pressed : comparing the ways in which force and self-reports reveal emotion
No full textInteractive human-computer systems can be enriched to interpret and respond to users’ affective states using computational emotion models, which necessitates the collection of authentic and spontaneous emotion data. Popular emotion modelling frameworks rely on convenient, yet static abstractions of emotion (e.g., Ekman's basic emotions and Russell's circumplex). These abstractions often oversimplify complex emotional experiences into single emotion categories. In turn, emotion models guided by such emotion annotations leave out significant aspects of the user's true, spontaneous emotional experience. Richer representations of emotion, negotiated and understood between participants and researchers, can be created using mixed-methods labelling--assigning an emotion descriptor to a recorded segment of experience--approaches. However, resulting emotion annotations are often not ready-to-use in computational models. In this thesis, we investigate (1) ways to improve meaningfulness of self-reported emotion annotations, and (2) to understand the implicit expression of emotion in touch pressure. For the first, we propose three strategies to interpret multiple versions of self-annotated dynamic emotion through combining (multi-label classification), extracting (of alignment metrics), and resolving (of conflicts between) emotion labels. We evaluate our label-resolution strategies using the FSR EEG Emotion-Labelled (FEEL) dataset (N=16). The FEEL dataset includes brain activity and keypress force data captured from a 10-minute video of user gameplay experience, annotated with two methods of self-reporting emotion--a continuous annotation and an interview. By featuring multi-pass self-report and user-calibrated scales, the data collection protocol prioritized the capture of genuine emotion evolution. We triangulate multiple self-annotated emotion reports and evaluate classification accuracy of our three proposed label resolution strategies. For our second research question, we compare models built on keypress force and brain activity data in an effort to understand the implicit expression of emotion in touch pressure. Finally, we reflect on the trade-offs of each strategy for developing computational models of emotion. Our findings suggest that touch-based models outperform those built on brain activity, and mixed-methods emotion annotations increase self-report meaningfulness.Science, Faculty ofComputer Science, Department ofGraduat
From devices to data and back again : a tale of computationally modelling affective touch
No full textEmotionally responsive Human-Robot Interaction (HRI) has captured our curiosity and imagination in fantastical ways throughout much of modern media. With touch being a valuable yet sorely missed emotion communication channel when in-person interaction is unrealistic for practical reasons, we could look to machine-mediated ways to bridge that distance. In this thesis, we investigate how we might enable machines to recognize natural and spontaneous emotional touch expressions in two parts.
First, we take a close look at ways machines engage with human emotion by examining examples of machines in three emotionally communicative roles: as a passive witness receiving and logging the emotional state of their (N=30) human counterparts, as an influential actor whose own breathing behaviour alters human fear response (N=103), and as a conduit for the transmission of emotion expression between human users (N=10 dyads and N=21 individuals).
Next, we argue that in order for devices to be truly emotionally reactive, they should address the time-varying and dynamic nature of emotional lived experience. Any computational or emotion recognition engine intended for use under realistic conditions should acknowledge that emotions will evolve over time. Machine responses may change with changing ‘emotion direction’ – acting in an encouraging way when the user is `happy and getting happier' vs. presenting calming behaviours for `happy but getting anxious'. To that end, we develop a multi-stage emotion self-reporting procedure for collecting N=16 users’ dynamic emotion expression during videogame play. From their keypress force controlling their in-game character, we benchmark individualized recognition performance for emotion direction, even finding it to exceed that of brain activity (as measured by continuous Electroencephalography (EEG)). For a proof-of-concept of a training process that generates models of true and spontaneous emotion expression evolving with the user, we then revise our protocol to be more flexible to naturalistic emotion expression. We build a custom tool to help with data collection and labelling of personal storytelling sessions and evaluate user impressions (N=5 with up to 3 stories each for a total of 10 sessions).
Finally, we conclude with actionable recommendations for advancing the training and machine recognition of naturalistic and dynamic emotion expression.Science, Faculty ofComputer Science, Department ofGraduat
Magic pen : a versatile digital manipulative for learning
No full textDigital manipulatives such as robots are an opportunity for interactive and engaging learning activities. The addition of haptic and specifically force feedback to digital manipulatives can enrich the learning of science-related concepts by building physical intuition. As a result, learners can design experiments and physically explore them to solve problems they have posed.
In my thesis, I present the evolution of the design and evaluation of a versatile digital manipulative – called MagicPen – in a human-centered design context. First, I investigate how force feedback can enable learners to fluidly express their ideas. I identify three core interactions as bases for physically assisted sketching (phasking).
Then, I show how using these interactions improves the accuracy of users’ drawings as well as their authority in creative works. In the next phase, I demonstrate the potential benefits of using force feedback in a collaborative learning framework, in a manner that is generalizable beyond the device we invented and lends insight on how haptics can empower digital manipulatives to express advanced concept by means of the behaviour of a virtual avatar and the respective feeling of force feedback.
This informs our device’s capability for learning advanced concepts in classroom settings and further considerations for the next iterations of the MagicPen. Based on the findings of how haptic feedback could assist with design and exploration in learning, In the last phase of my thesis, I propose a framework for physically assisted learning (PAL) which links the expression and exploration of an idea. Furthermore, I explain how to instantiate the PAL framework using available technologies and discuss a path forward to a larger vision of physically assisted
learning. PAL highlights the role of haptics in future "objects-to-think-with".Science, Faculty ofComputer Science, Department ofGraduat
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