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Gila Schein

dblp:332/0564 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

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
Wearable and physiological sensing · 67% Health and well-being technologies · 33%
Artificial intelligence
1 paper
Video understanding and tracking · 100%

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

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing
electrical impedance tomography
0.612022
MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement · UIST 2022
Wearable and physiological sensing
muscle activity sensing
0.612022
MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement · UIST 2022
Health and well-being technologies › rehabilitation
physical therapy
0.612022
MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement · UIST 2022
Computer vision › Video understanding and tracking
motion tracking
0.212022
MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement · UIST 2022

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

user study · 1.1optical motion tracking · 1.1electrical impedance tomography · 1.1
YearPublicationVenuePosition
2022 MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement
abstract
Unsupervised physical rehabilitation traditionally has used motion tracking to determine correct exercise execution. However, motion tracking is not representative of the assessment of physical therapists, which focus on muscle engagement. In this paper, we investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises by showing users whether they target the right muscle groups. To accomplish this, we use wearable electrical impedance tomography (EIT) to monitor muscle engagement and visualize the current state on a virtual muscle-skeleton avatar. We use additional optical motion tracking to also monitor the user’s movement. We conducted a user study with 10 participants that compares exercise execution while seeing muscle + motion data vs. motion data only, and also presented the recorded data to a group of physical therapists for post-rehabilitation analysis. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy during rehabilitation, and post-rehabilitation evaluation for physical therapists.
Junyi Zhu 0001, Yuxuan Lei, Aashini Shah, Gila Schein, Hamid Ghaednia, Joseph H. Schwab, Casper Harteveld, Stefanie Mueller 0001
UIST4