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Faizan Haque

dblp:161/3309 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1 · 1 first-author

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
1 paper
Interaction techniques and input · 70% Wearable and physiological sensing · 30%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › target selection › pointing
mid-air pointing
0.212015
Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors · CHI 2015
Interaction techniques and input › target selection › pointing
pointing and clicking
0.212015
Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors · CHI 2015
Interaction techniques and input
large display interaction
0.112015
Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors · CHI 2015

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

user study · 0.2signal filtering · 0.2inertial measurement unit sensing · 0.2
YearPublicationVenuePosition
2015 Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors
abstract
We describe a mid-air, barehand pointing and clicking interaction technique using electromyographic (EMG) and inertial measurement unit (IMU) input from a consumer armband device. The technique uses enhanced pointer feedback to convey state, a custom pointer acceleration function tuned for angular inertial motion, and correction and filtering techniques to minimize side-effects when combining EMG and IMU input. By replicating a previous large display study using a motion capture pointing technique, we show the EMG and IMU technique is only 430 to 790 ms slower and has acceptable error rates for targets greater than 48 mm. Our work demonstrates that consumer-level EMG and IMU sensing is practical for distant pointing and clicking on large displays.
Faizan Haque, Mathieu Nancel, Daniel Vogel 0001
CHI1