Toward Personalized Rotator Cuff Physical Therapy Dosage Using a Machine Learning-Based Pilot Study with EMG.

AmirHossein MajidiRad, Iram Azam, Japp Adhikari, Mehrnoosh Damircheli

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2026. 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.

AmirHossein MajidiRadDepartment of Engineering Technology, Purdue University Northwest, Hammond, IN 46323, USA.ORCID 0000-0003-3524-338X

Iram AzamDepartment of Computer Information Technology, Purdue University Northwest, Hammond, IN 46323, USA.

Japp AdhikariDepartment of Computer Information Technology, Purdue University Northwest, Hammond, IN 46323, USA.

Mehrnoosh DamircheliDepartment of Mechanical Engineering, York College of Pennsylvania, York, PA 17403, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rotator cuff injuries are among the most common musculoskeletal conditions that affect shoulder function and can ultimately impact quality of life. While physical therapy is essential in the care of rotator cuff injuries, the ideal dose of therapeutic exercises continues to be a significant clinical dilemma because of the generalized nature of rehabilitation protocols. This pilot study proposes a machine learning approach to personalize rehabilitation using surface electromyography (sEMG) data collected from eight healthy individuals by testing four key shoulder movements: scaption, internal rotation, external rotation, and external rotation at 90° abduction. In this research, the XGBoost algorithm was used to model muscle activation patterns by achieving a high predictive accuracy (

Indexed as

electromyography (EMG)kinematic analysismachine learning in movement sciencerehabilitation engineeringrotator cuff injury

Identifiers

PMID42072276

DOI10.3390/bioengineering13040483

PMCPMC13113416

What OpenQuestion holds

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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.