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Jim Turner

dblp:61/2892 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 3

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Interaction techniques and input · 83% Wearable and physiological sensing · 17%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › input device
muscle-computer interface
0.222010
Making muscle-computer interfaces more practical · CHI 2010
Enabling always-available input with muscle-computer interfaces · UIST 2009
Interaction techniques and input › input sensing › gesture recognition
gesture classification
0.112009
Enabling always-available input with muscle-computer interfaces · UIST 2009
Wearable and physiological sensing
electromyography
0.012009
Enabling always-available input with muscle-computer interfaces · UIST 2009

Methods — techniques the papers use, named apart from their topics

gesture classification · 0.1cross-session calibration · 0.1real-time gesture recognition · 0.1EMG classification · 0.1
YearPublicationVenuePosition
2010 Making muscle-computer interfaces more practical
abstract
Recent work in muscle sensing has demonstrated the poten-tial of human-computer interfaces based on finger gestures sensed from electrodes on the upper forearm. While this approach holds much potential, previous work has given little attention to sensing finger gestures in the context of three important real-world requirements: sensing hardware suitable for mobile and off-desktop environments, elec-trodes that can be put on quickly without adhesives or gel, and gesture recognition techniques that require no new training or calibration after re-donning a muscle-sensing armband. In this note, we describe our approach to over-coming these challenges, and we demonstrate average clas-sification accuracies as high as 86 % for pinching with one of three fingers in a two-session, eight-person experiment.
T. Scott Saponas, Desney S. Tan, Dan Morris 0001, Jim Turner, James A. Landay
CHI4
2009 Enabling always-available input with muscle-computer interfaces
abstract
Previous work has demonstrated the viability of applying offline analysis to interpret forearm electromyography (EMG) and classify finger gestures on a physical surface. We extend those results to bring us closer to using muscle-computer interfaces for always-available input in real-world applications. We leverage existing taxonomies of natural human grips to develop a gesture set covering interaction in free space even when hands are busy with other objects. We present a system that classifies these gestures in real-time and we introduce a bi-manual paradigm that enables use in interactive systems. We report experimental results demonstrating four-finger classification accuracies averaging 79% for pinching, 85% while holding a travel mug, and 88% when carrying a weighted bag. We further show generalizability across different arm postures and explore the tradeoffs of providing real-time visual feedback.
T. Scott Saponas, Desney S. Tan, Dan Morris 0001, Ravin Balakrishnan, Jim Turner, James A. Landay
UIST5
2003 Creating a collaborative learning environment for children
abstract
This paper reports on a project to create a networked 3D play environment that aids collaborative learning. The specific user group the project is aimed at is secondary school children. The project explores the educational possibilities offered within the dynamics of massively multi-player online games (MMOG) and other commercial computer game genres.
Matt Southern, Jim Turner
IDC2