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John Majikes

dblp:156/1836 · also John J. Majikes · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1

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
Human-robot interaction · 77% Learning and educational technologies · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
animal-computer interaction
0.312017
Balancing noise sensitivity, response latency, and posture accuracy for a computer-assisted canine posture training system · Int. J. Hum. Comput. Stud. 2017
Learning and educational technologies › skill training
computer training
0.112017
Balancing noise sensitivity, response latency, and posture accuracy for a computer-assisted canine posture training system · Int. J. Hum. Comput. Stud. 2017
YearPublicationVenuePosition
2017 Balancing noise sensitivity, response latency, and posture accuracy for a computer-assisted canine posture training system
John Majikes, Rita Brugarolas Brufau, Michael Winters, Sherrie Yuschak, Sean Mealin, Katherine Walker, Barbara L. Sherman, Alper Bozkurt, David L. Roberts 0001
Int. J. Hum. Comput. Stud.1
2015 Knowledge engineering for unsupervised canine posture detection from IMU data
abstract
Training animals is a process that requires a significant investment of time and energy on the part of the trainer. One of the most basic training tasks is to train dogs to perform postures on cue. While it might be easy for a human trainer to see when an animal has performed the desired posture, it is much more difficult for a computer to determine this. Most work in this area uses accelerometer and/or gyroscopic data to produce data from an animal's current state, but this has limitations. Take for example a normal standing posture. From an accelerometer's perspective, it closely resembles the "laying down" posture, but the posture can look very different if the animal is standing still, versus walking, versus running, and might look completely different from a "standing on incline" posture. A human trainer can instantly tell the difference between these postures and behaviors, but the process is much more difficult for a computer.
Michael Winters, Rita Brugarolas Brufau, John Majikes, Sean Mealin, Sherrie Yuschak, Barbara L. Sherman, Alper Bozkurt, David L. Roberts 0001
Advances in Computer Entertainment3