Boxing Punch Detection and Classification Using Motion Tape and Machine Learning.

Shih-Chao Huang, Taylor Pierce, Yun-An Lin, Kenneth J Loh

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

4 authors.

Shih-Chao HuangActive, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0009-0008-4571-9226

Taylor PierceDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Yun-An LinActive, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0001-9440-6164

Kenneth J LohActive, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0003-1448-6251

Funding

Office of Naval Research N00014-23-1-2647

6 · The paper itself

Abstract

The objective of this study is to classify the types of boxing punches using machine learning algorithms that processed skin-strain time history measurements from a self-adhesive, elastic fabric, wearable sensor called Motion Tape. A human participant study was designed to capture movements during boxing training. Subjects were asked to perform multiple sets of punches during the entire test, which consisted of jabs and hooks with and without striking a heavy bag. The collected Motion Tape data was used to train and compare time series classification algorithms to identify the types of punches performed and associated conditions. The results demonstrated that Motion Tape, in combination with machine learning techniques, could effectively classify different punch types based on skin-strain measurements. These findings highlighted the potential of the system as an effective tool for human performance analysis in sports and biomechanics applications.

Indexed as

Machine LearningAlgorithmsBiomechanical PhenomenaHumansMotionMovementWearable Electronic DevicesclassificationInceptionTimeMiniRocketmovementsportssupervised learningtime series transformertrainingwearable sensors

Identifiers

PMID40871890

DOI10.3390/s25165027

PMCPMC12390462

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.