ArticleFrontiers in bioengineering and biotechnology2020
Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Shoulder Muscle Activity Through Surface Electromyography and Estimation of Physiological Fatigue Using the T-Fast Test in Patients With Glenohumeral Instability: A Cross-Sectional Study.Physiotherapy research international : the journal for researchers and clinicians in physical therapy · 2025Trial
- Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals.Microsystems & nanoengineering · 2024Article
- Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements.Sensors (Basel, Switzerland) · 2022Article
- Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a great demand for convenient and quantitative assessment of upper-limb traumatic peripheral nerve injuries (PNIs) beyond their clinical routine. This would contribute to improved PNI management and rehabilitation.
objectiveThe aim of this study was to develop a novel surface EMG examination method for quantitatively evaluating traumatic upper-limb PNIs.
methodsExperiments were conducted to collect surface EMG data from forearm muscles on both sides of seven male subjects during their performance of eight designated hand and wrist motion tasks. All participants were clinically diagnosed as unilateral traumatic upper-limb PNIs on the ulnar nerve, median nerve, or radial nerve. Ten healthy control participants were also enrolled in the study. A novel framework consisting of two modules was also proposed for data analysis. One module was first used to identify whether a PNI occurs on a tested forearm using a machine learning algorithm by extracting and classifying features from surface EMG data. The second module was then used to quantitatively evaluate the degree of injury on three individual nerves on the examined arm.
resultsThe evaluation scores yielded by the proposed method were highly consistent with the clinical assessment decisions for three nerves of all 34 examined arms (7 × 2 + 10 × 2), with a sensitivity of 81.82%, specificity of 98.90%, and significate linear correlation (
conclusionThis study offers a useful tool for PNI assessment and helps to promote extensive clinical applications of surface EMG.
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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.