Learning and performance with gesture guides

Anderson, Fraser; Bischof, Walter F.

doi:10.1145/2470654.2466143

Cited by 44 publications

(67 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most gesture work in HCI investigated short-term retention of gestures [6,7]. However, we agree with previous discussions [1,21] that gesture sets should also be studied with respect to long-term recall in order to truly understand gesture memorability. In the following sections, we introduce a novel multi-finger chord vocabulary for off-the-shelf tablets.…”

Section: Mappings In Interfacessupporting

confidence: 87%

“…Yet, today's commercial tablet interfaces provide a limited set of input gestures. Gestural interface design for multi-touch interfaces is still emerging; we face similar challenges and goals today as designers did for keyboard-and-mouse interfaces in the 80s: (1) increase input expressivity, e.g. by providing keyboard shortcuts, or adding additional buttons or wheels to mouse devices; and (2) design task-action mappings that facilitate learning and memorization [22,23], e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-finger chords for hand-held tablets

Wagner

Lecolinet

Selker

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Despite the demonstrated benefits of multi-finger input, todays gesture vocabularies offer a limited number of postures and gestures. Previous research designed several posture sets, but does not address the limited human capacity of retaining them. We present a multi-finger chord vocabulary, introduce a novel hand-centric approach to detect the identity of fingers on off-the-shelf hand-held tablets, and report on the detection accuracy. A between-subjects experiment comparing 'random' to a 'categorized' chord-command mapping found that users retained categorized mappings more accurately over one week than random ones. In response to the logical posture-language structure, people adapted to logical memorization strategies, such as 'exclusion', 'order', and 'category', to minimize the amount of information to retain. We conclude that structured chord-command mappings support learning, short-, and long-term retention of chordcommand mappings.

show abstract

Section: Mappings In Interfacessupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Multi-finger chords for hand-held tablets

Wagner

Lecolinet

Selker

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…Several researchers have examined related gesture prediction methods [Freeman et al 2009;Appert and Bau 2010;Bennett et al 2011;Kristensson and Denby 2011]. Anderson and Bischof [2013] conducted experiments to determine whether Schmidt's [1991] guidance hypothesis applies with guidance-based interactive methods (Section 2.9). They compared user performance using four different interfaces for providing guidance: crib notes, which provided static representations of the gestures and are continually displayed in the corner of the display; static-tracing representations, which showed nonresponsive versions of all gestures at the starting location of the gesture; dynamic-tracing, equivalent to OctoPocus; and adaptive-tracing, which was identical to the static-tracing condition except that the guide disappeared at a progressively earlier point through the training set (disappearing at the end of the gesture in the first training event and at the start of the gesture in the last training event).…”

Section: Extended Learningmentioning

confidence: 99%

Supporting Novice to Expert Transitions in User Interfaces

et al. 2014

View full text Add to dashboard Cite

Interface design guidelines encourage designers to provide high-performance mechanisms for expert users. However, research shows that many expert interface components are seldom used and that there is a tendency for users to persistently fail to adopt faster methods for completing their work. This article summarizes and organizes research relevant to supporting users in making successful transitions to expert levels of performance. First, we provide a brief introduction to the underlying human factors of skill acquisition relevant to interaction with computer systems. We then present our focus, which is a review of the state of the art in user interfaces that promote expertise development. The review of interface research is based around four domains of performance improvement: intramodal improvement that occurs as a factor of repetition and practice with a single method of interaction; intermodal improvement that occurs when users switch from one method to another that has a higher performance ceiling; vocabulary extension, in which the user broadens his or her knowledge of the range of functions available; and task mapping, which examines the ways in which users perform their tasks. The review emphasizes the relationship between interface techniques and the human factors that explain their relative success.

show abstract

“…Users of both systems showed improvement in learning the mapping of gestures. Anderson and Bischof studied various gesture guides and found high guidance can hinder learning of the motor component [2].…”

Section: Technology-based Motor Skill Trainingmentioning

confidence: 99%

YouMove

Anderson

Grossman

Matejka

et al. 2013

Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology

Self Cite

258

View full text Add to dashboard Cite

YouMove is a novel system that allows users to record and learn physical movement sequences. The recording system is designed to be simple, allowing anyone to create and share training content. The training system uses recorded data to train the user using a large-scale augmented reality mirror. The system trains the user through a series of stages that gradually reduce the user's reliance on guidance and feedback. This paper discusses the design and implementation of YouMove and its interactive mirror. We also present a user study in which YouMove was shown to improve learning and short-term retention by a factor of 2 compared to a traditional video demonstration.

show abstract

Learning and performance with gesture guides

Cited by 44 publications

References 38 publications

Multi-finger chords for hand-held tablets

Multi-finger chords for hand-held tablets

Supporting Novice to Expert Transitions in User Interfaces

YouMove

Contact Info

Product

Resources

About