What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Statistics
The scientist’s investigation covers issues in Human–computer interaction, Gesture, Multimedia, Mobile device and Gesture recognition.
His work carried out in the field of Human–computer interaction brings together such families of science as User interface, Gesture elicitation, Mobile computing and Laptop.
To a larger extent, he studies Artificial intelligence with the aim of understanding Gesture.
His Multimedia research is multidisciplinary, incorporating elements of Web page, World Wide Web, Web navigation and Set.
His Mobile device research integrates issues from Speech output, Simulation and Index finger.
Gesture recognition is a subfield of Computer vision that he studies.
His most cited work include:
- User-defined gestures for surface computing (865 citations)
- The aligned rank transform for nonparametric factorial analyses using only anova procedures (751 citations)
- Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes (674 citations)
What are the main themes of his work throughout his whole career to date?
Jacob O. Wobbrock mainly focuses on Human–computer interaction, Multimedia, Gesture, Mobile device and Artificial intelligence.
Jacob O. Wobbrock has included themes like Stylus, Input device, User interface, Joystick and Touchpad in his Human–computer interaction study.
His Multimedia study combines topics from a wide range of disciplines, such as Sign language, American Sign Language, Data compression and Set.
His research in Gesture intersects with topics in Pseudocode, Speech recognition, Simulation and Text entry.
His Mobile device research includes themes of Situational ethics and Mobile computing.
The Artificial intelligence study combines topics in areas such as Touchscreen, Computer vision and Natural language processing.
He most often published in these fields:
- Human–computer interaction (35.51%)
- Multimedia (22.43%)
- Gesture (21.96%)
What were the highlights of his more recent work (between 2018-2021)?
- Human–computer interaction (35.51%)
- Gesture (21.96%)
- Mobile device (16.82%)
In recent papers he was focusing on the following fields of study:
Human–computer interaction, Gesture, Mobile device, Set and Text entry are his primary areas of study.
His Human–computer interaction research is multidisciplinary, relying on both Situational ethics and Adaptive support.
The study incorporates disciplines such as Cursor and Foot in addition to Gesture.
Jacob O. Wobbrock combines subjects such as Android, Wearable computer and Data science with his study of Mobile device.
Jacob O. Wobbrock interconnects Icon, Medical education and Identification in the investigation of issues within Set.
His study in Text entry is interdisciplinary in nature, drawing from both Speech input, Speech output, Artificial intelligence and Laptop.
Between 2018 and 2021, his most popular works were:
- A Systematic Review of Gesture Elicitation Studies: What Can We Learn from 216 Studies? (17 citations)
- Aligned Rank Transform [R package ARTool version 0.10.8] (10 citations)
- Crowdlicit: A System for Conducting Distributed End-User Elicitation and Identification Studies (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Statistics
His main research concerns Human–computer interaction, Situational ethics, Android, Mobile device and World Wide Web.
His Human–computer interaction research incorporates themes from Gesture elicitation, Foot and Generative grammar.
His Situational ethics study incorporates themes from Space, User modeling and Distraction.
His biological study spans a wide range of topics, including Software design pattern, Affect perception, Applied psychology and Radio button.
Much of his study explores Mobile device relationship to Design pattern.
His studies deal with areas such as Perspective and User-centered design as well as World Wide Web.
This overview was generated by a machine learning system which analysed the scientist’s
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