ArticleSensors (Basel, Switzerland)2022
Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches.Sensors (Basel, Switzerland) · 2025Pooled it
- Assessment of surface EMG biomarkers in sarcopenic motor dysfunction during postural stabilization.GeroScience · 2026Article
- Discovery of key surface electromyography features during walking for discerning high and low muscle mass using machine learning analysis.Scientific reports · 2026Article
- Neuromuscular characteristics of dynamic balance control in patients with sarcopenia: a combined analysis of surface electromyography and limits of stability.Frontiers in aging neuroscience · 2026Article
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- Preliminary Study on Wearable Smart Socks with Hydrogel Electrodes for Surface Electromyography-Based Muscle Activity Assessment.Sensors (Basel, Switzerland) · 2025Article
- A wearable approach for Sarcopenia diagnosis using stimulated muscle contraction signal.Biomedical engineering letters · 2025Article
- A Transfer Learning Approach for Toe Walking Recognition Using Surface Electromyography on Leg Muscles.Sensors (Basel, Switzerland) · 2025Article
- Wearable Fabric System for Sarcopenia Detection.Biosensors · 2024Article
- Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data.Journal of neuroengineering and rehabilitation · 2024Article
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength.Sensors (Basel, Switzerland) · 2023Article
- Development of a Biomechanical Device for Parameter Quantification Associated with the Sit-to-Stand Movement.Sensors (Basel, Switzerland) · 2023Article
- Portable Digital Monitoring System for Sarcopenia Screening and Diagnosis.Geriatrics (Basel, Switzerland) · 2022Article
- A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications.Biosensors · 2022Review
- Muscle Mass Measurement Using Machine Learning Algorithms with Electrical Impedance Myography.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Sarcopenia is a geriatric condition characterized by a loss of strength and muscle mass, with a high impact on health status, functional independence and quality of life in older adults. [d=TT, ]To reduce the effects of the disease, just the diagnostic is not enough, it is necessary more than recognition.To reduce the effects of the disease, it is important to recognize the level and progression of sarcopenia early. Surface electromyography is becoming increasingly relevant for the prevention and diagnosis of sarcopenia, also due to a wide diffusion of smart and minimally invasive wearable devices suitable for electromyographic monitoring. The purpose of this work is manifold. The first aim is the design and implementation of a hardware/software platform. It is based on the elaboration of surface electromyographic signals extracted from the Gastrocnemius Lateralis and Tibialis Anterior muscles, useful to analyze the strength of the muscles with the purpose of distinguishing three different "confidence" levels of sarcopenia. The second aim is to compare the efficiency of state of the art supervised classifiers in the evaluation of sarcopenia. The experimentation stage was performed on an "augmented" dataset starting from data acquired from 32 patients. The latter were distributed in an unbalanced manner on 3 "confidence" levels of sarcopenia. The obtained results in terms of classification accuracy demonstrated the ability of the proposed platform to distinguish different sarcopenia "confidence" levels, with highest accuracy value given by Support Vector Machine classifier, outperforming the other classifiers by an average of 7.7%.
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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.