Evidence map›Paper›PMID 35342901›Full record

ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2020

Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance.

Momona Yamagami, Katherine M Steele, Samuel A Burden

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 59 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Momona YamagamiUniversity of Washington Seattle, WA.
Katherine M SteeleUniversity of Washington Seattle, WA.
Samuel A BurdenUniversity of Washington Seattle, WA.
University of Washington · US

Funding

SCH: Ubiquitous rehabilitation to improve movement after neurologic injury R01EB021935 · NIBIB · UNIVERSITY OF WASHINGTON · PI STEELE, KATHERINE M · 2015 to 2018
$1.6M
NIBIB NIH HHS R01 EB021935
6 · The paper itself

Abstract

Manual device interaction requires precise coordination which may be difficult for users with motor impairments. Muscle interfaces provide alternative interaction methods that may enhance performance, but have not yet been evaluated for simple (eg. mouse tracking) and complex (eg. driving) continuous tasks. Control theory enables us to probe continuous task performance by separating user input into intent and error correction to quantify how motor impairments impact device interaction. We compared the effectiveness of a manual versus a muscle interface for eleven users without and three users with motor impairments performing continuous tasks. Both user groups preferred and performed better with the muscle versus the manual interface for the complex continuous task. These results suggest muscle interfaces and algorithms that can detect and augment user intent may be especially useful for future design of interfaces for continuous tasks.

Indexed as

accessibilitycontrol theoryelectromyographyHuman-centered computing → User modelsinteractionmotor impairmentsmuscle interfacesUser intentUser studies

Identifiers

PMID35342901
PMCPMC8956205
OpenAlexW3032595247

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.