ArticleAnnals of biomedical engineering2026
Portable Upper-limb Muscle Tone Assessment by Integrating Multi-sensor Signals.
Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeTraditional hypertonia diagnosis relies on the Modified Ashworth Scale (MAS), which is subjective and dependent on doctors' experience. Although previous studies have explored the use of force sensors and surface electromyography (sEMG), finding a reliable and valid detection method remains a challenge. This study aims to develop a simple yet effective platform that integrates biomechanical and sEMG data for upper-limb muscle tone assessment, providing a more objective and quantitative evaluation approach.
methodsA detection platform was developed to collect biomechanical and sEMG data from 59 subjects, including 49 patients (MAS Ⅰ = 21, MAS Ⅰ + = 16, MAS Ⅱ = 12) and 10 healthy individuals, at different movement speeds (15°/s, 20°/s, and 25°/s). The acquired data underwent feature extraction, including signal processing and statistical analysis. Dimensionality reduction was applied to optimize the extracted features, and these features were then integrated into a classification algorithm for further analysis.
resultsThe extracted features effectively distinguished patients from healthy individuals, with statistically significant differences (p < 0.01). Furthermore, the strong correlation between the extracted features and MAS scores (p < 0.01) confirmed the reliability of the proposed method. Finally, the classification algorithm demonstrated high consistency with clinical evaluations, validating its potential for clinical application in muscle tone assessment.
conclusionThis study introduces an objective and quantitative method for assessing muscle tone, shifting away from the traditional subjective MAS evaluation. By enhancing diagnostic accuracy, the proposed approach provides a more reliable basis for hypertonia diagnosis and treatment. The findings hold significant promise for optimizing clinical decision-making, ultimately improving patient management and therapeutic strategies.
Indexed as
Identifiers
41731265What OpenQuestion holds
Registered trials
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.