Expressy

Wilkinson, Gerard; Kharrufa, Ahmed; Hook, Jonathan; Pursglove, Bradley; Wood, Gavin; Haeuser, Hendrik; Hammerla, Nils Y.; Hodges, Steve; Olivier, Patrick

doi:10.1145/2858036.2858223

Cited by 32 publications

(4 citation statements)

References 41 publications

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“…The growing availability of smartwatches and smart rings have prompted researchers to explore new forms of humancomputer interaction and augmentation. Duet [14], VibRing [3] and Expressy [68] leverage the smartwatch or smart ring to add expressiveness to touch-based interactions on a phone or tablet device. Digital digits [52] and "Ring Form Factor" [15] surveyed and explored the design space of smart rings for interaction.…”

Section: Wrist and Finger Interactionsmentioning

confidence: 99%

Wrist

Yeo

Lee

Kim

et al. 2019

Proceedings of the 21st International Conference on Human-Computer Interaction With Mobile Devices and Services

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To better explore the incorporation of pointing and gesturing into ubiquitous computing, we introduce WRIST, an interaction and sensing technique that leverages the dexterity of human wrist motion. WRIST employs a sensor fusion approach which combines inertial measurement unit (IMU) data from a smartwatch and a smart ring. The relative orientation difference of the two devices is measured as the wrist rotation that is independent from arm rotation, which is also position and orientation invariant. Employing our test hardware, we demonstrate that WRIST affords and enables a number of novel yet simplistic interaction techniques, such as (i) macro-micro pointing without explicit mode switching and (ii) wrist gesture recognition when the hand is held in different orientations (e.g., raised or lowered). We report on two studies to evaluate the proposed techniques and we

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Section: Wrist and Finger Interactionsmentioning

confidence: 99%

Wrist

Yeo

Lee

Kim

et al. 2019

Proceedings of the 21st International Conference on Human-Computer Interaction With Mobile Devices and Services

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“…Wilkinson et al. describe a model for expressive touch interaction, beginning with the intention phase prior to touch, followed by the enrichment phase during touch, and concluding with the follow-up or recovery phase [48]. These two models are reflected in the interaction design of prior techniques that make use of hovering hand gestures before and after surface contacts [1,9,10,13,19,34,50].…”

Section: Above and Around Surface Interactionmentioning

confidence: 99%

Interstices

Webb

Fowler

Kerne

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Our observations of landscape architecture students revealed a new phenomenon-interstices. Their bimanual interactions with a pen and touch surface involved various sustained hand gestures, interleaved between their regular commands. Positioning of the non-preferred hand indicates anticipated actions, including: sustained hovering near the surface; pulled back but still floating above the surface; and resting in their laps. We ran a second study with 14 landscape architect students which confirmed our observations, and uncovered a new interstice i.e. stabilizing the preferred hand while handwriting. We conclude with directions for future research and challenges for designers and researchers.

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“…Activity recognition is "the ability of the intelligent system to deduce temporally contextualized knowledge regarding the state of the user on the basis of a set of heterogeneous sensor readings" (Pecora & Cirillo, 2009). Our work on activity recognition forms part of larger body of research around wearable computing to build computational systems which can capture behavioural data 'in the wild' to enable more objective and accurate pictures of practices in education and beyond (Ploetz, 2010;Khan et al, 2015;Khan et al, 2016;Miu et al, 2015;Wilkinson et al, 2016).…”

Section: Educational Context To Solementioning

confidence: 99%

Supporting the smart teacher: an agenda for the use of embedded sensing in novel learning spaces

Preston

Lazem

Kharrufa

et al. 2018

Smart Learn. Environ.

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We converge the disciplines of context-aware computing, human-computer interaction and pedagogic research practice to propose an agenda for the use of embedded sensing for novel learning spaces. In this case, embedded sensing is the identification and analysis of in-the moment individual, group and class level behavioural data from students engaged in physical learning activities. Our work is motivated by the challenges and opportunities for teachers inherent in the rise of the design, development and evaluation of novel learning spaces augmented with multidevice technology. We present a framework for the use of embedded sensing, its relationship related and emerging work in the fields of social learning analytics and smart learning, and a practical illustration of SOLE (Sugata Mitra's Self Organised Learning Environments). Our agenda addresses the conceptualization, data collection, and analysis of learning; zooming in on hard-to-identify individual-withingroup learning processes. For the educational researcher, we propose a contextsensitive, dynamic and situated approach that can inform analytic frameworks and development of tools for sense-making. For the teacher-inquirer, the smart teacher, we propose that this approach directly addresses issues linked to the complexity of the 'what and how' of education-based evaluation and assessment of students in unstructured and multidevice learning spaces more broadly.

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Expressy

Cited by 32 publications

References 41 publications

Wrist

Wrist

Interstices

Supporting the smart teacher: an agenda for the use of embedded sensing in novel learning spaces

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