On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures.

Jonas Große Sundrup, Katja Mombaur

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Jonas Große SundrupCanada Excellence Research Chair Human-Centred Robotics and Machine Intelligence, Systems Design Engineering & Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Katja MombaurCanada Excellence Research Chair Human-Centred Robotics and Machine Intelligence, Systems Design Engineering & Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Funding

Federal Ministry of Education and Research, Germany/Bundesministerium für Bildung und Forschung, Deutschland 16SV7772

6 · The paper itself

Abstract

We investigate the distribution of muscle signatures of human hand gestures under Dynamic Time Warping. For this we present a k-Nearest-Neighbors classifier using Dynamic Time Warping for the distance estimate. To understand the resulting classification performance, we investigate the distribution of the recorded samples and derive a method of assessing the separability of a set of gestures. In addition to this, we present and evaluate two approaches with reduced real-time computational cost with regards to their effectiveness and the mechanics behind them. We further investigate the impact of different parameters with regards to practical usability and background rejection, allowing fine-tuning of the induced classification procedure.

Indexed as

GesturesMusclesCluster AnalysisHumansRecordsUpper Extremityclassificationdynamic time warpingEMGgesture recognitionsignal processing

Identifiers

PMID37687896

DOI10.3390/s23177441

PMCPMC10490578

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.