ArticleFrontiers in neuroscience2019
A Regression-Based Framework for Quantitative Assessment of Muscle Spasticity Using Combined EMG and Inertial Data From Wearable Sensors.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Technology-assisted assessment of spasticity: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- An Intelligent Wearable EMG Sensing Framework for Athlete Neuromuscular Monitoring and Performance Progression Assessment.Biosensors · 2026Article
- Multimodal Information Fusion for Control of Rehabilitation Robots in Motor Dysfunction: A Review.Bioengineering (Basel, Switzerland) · 2026Review
- Electromyographic features for quantitative assessment of lower limb spasticity after stroke: a feasibility study.Medical & biological engineering & computing · 2026Article
- Development and preliminary validation of a multimodal instrument for spasticity quantification using a composite index: A pilot study.PloS one · 2026Article
- Advancements in Understanding Spasticity: A Neuromusculoskeletal Modeling Perspective.Journal of clinical medicine · 2025Review
- Automated assessment of upper limb spasticity in stroke patients with fusion of multichannel surface electromyography features.Journal of rehabilitation medicine · 2025Article
- Multimodal machine learning to predict response to ultrasound-guided botulinum and vibration therapy in muscle spasticity a clinical and imaging correlation study.Frontiers in bioengineering and biotechnology · 2025Article
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Volitional Control of the Paretic Hand Post-Stroke Increases Finger Stiffness and Resistance to Robot-Assisted Movement.Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics · 2024Article
- Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems.Frontiers in neuroscience · 2024Article
- Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements.BioMed research international · 2024Review
- Quantitative assessment of muscle fatigue during rowing ergometer exercise using wavelet analysis of surface electromyography (sEMG).Frontiers in bioengineering and biotechnology · 2024Article
- Prediction of Spasticity through Upper Limb Active Range of Motion in Stroke Survivors: A Generalized Estimating Equation Model.Bioengineering (Basel, Switzerland) · 2023Article
- A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation.Healthcare (Basel, Switzerland) · 2023Review
- A Wireless, High-Quality, Soft and Portable Wrist-Worn System for sEMG Signal Detection.Micromachines · 2023Article
- Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision.Diagnostics (Basel, Switzerland) · 2023Article
- Design of a Multi-Sensor System for Exploring the Relation between Finger Spasticity and Voluntary Movement in Patients with Stroke.Sensors (Basel, Switzerland) · 2022Article
- Inertia Sensors for Measuring Spasticity of the Ankle Plantarflexors Using the Modified Tardieu Scale-A Proof of Concept Study.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There have always been practical demands for objective and accurate assessment of muscle spasticity beyond its clinical routine. A novel regression-based framework for quantitative assessment of muscle spasticity is proposed in this paper using wearable surface electromyogram (EMG) and inertial sensors combined with a simple examination procedure. Sixteen subjects with elbow flexor or extensor (i.e., biceps brachii muscle or triceps brachii muscle) spasticity and eight healthy subjects were recruited for the study. The EMG and inertial data were recorded from each subject when a series of passive elbow stretches with different stretch velocities were conducted. In the proposed framework, both lambda model and kinematic model were constructed from the recorded data, and biomarkers were extracted respectively from the two models to describe the neurogenic component and biomechanical component of the muscle spasticity, respectively. Subsequently, three evaluation methods using supervised machine learning algorithms including single-/multi-variable linear regression and support vector regression (SVR) were applied to calibrate biomarkers from each single model or combination of two models into evaluation scores. Each of these evaluation scores can be regarded as a prediction of the modified Ashworth scale (MAS) grade for spasticity assessment with the same meaning and clinical interpretation. In order to validate performance of three proposed methods within the framework, a 24-fold leave-one-out cross validation was conducted for all subjects. Both methods with each individual model achieved satisfactory performance, with low mean square error (MSE, 0.14 and 0.47) between the resultant evaluation score and the MAS. By contrast, the method using SVR to fuse biomarkers from both models outperformed other two methods with the lowest MSE at 0.059. The experimental results demonstrated the usability and feasibility of the proposed framework, and it provides an objective, quantitative and convenient solution to spasticity assessment, suitable for clinical, community, and home-based rehabilitation.
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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.