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Allie Schmiesing

dblp:198/3915 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · unresolved

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

Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 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
Immersive interaction · 77% Health and well-being technologies · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies › rehabilitation
motor rehabilitation
0.112017
Transfer of a skilled motor learning task between virtual and conventional environments · VR 2017

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

sequential visual isometric pinch task · 0.3between-environment training · 0.3
YearPublicationVenuePosition
2017 Transfer of a skilled motor learning task between virtual and conventional environments
abstract
Immersive, head-mounted virtual reality (HMD-VR) can be a potentially useful tool for motor rehabilitation. However, it is unclear whether the motor skills learned in HMD-VR transfer to the non-virtual world and vice-versa. Here we used a well-established test of skilled motor learning, the Sequential Visual Isometric Pinch Task (SVIPT), to train individuals in either an HMD-VR or conventional training (CT) environment. Participants were then tested in both environments. Our results show that participants who train in the CT environment have an improvement in motor performance when they transfer to the HMD-VR environment. In contrast, participants who train in the HMD-VR environment show a decrease in skill level when transferring to the CT environment. This has implications for how training in HMD-VR and CT may affect performance in different environments.
Julia Anglin, David Saldana, Allie Schmiesing, Sook-Lei Liew
VR3