ArticleSensors (Basel, Switzerland)2020
Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled 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.
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
25 citing papers in PubMed, 4 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- The Use of Wearable Devices to Measure Sedentary Behavior during COVID-19: Systematic Review and Future Recommendations.Sensors (Basel, Switzerland) · 2023Pooled it
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Electromyographic features for quantitative assessment of lower limb spasticity after stroke: a feasibility study.Medical & biological engineering & computing · 2026Article
- Rehabilitation assessment of upper limb motor function in stroke patients based on semi-quantitative information.Frontiers in robotics and AI · 2026Article
- Development and preliminary validation of a multimodal instrument for spasticity quantification using a composite index: A pilot study.PloS one · 2026Article
- Ultrasound-based DenseNet201 outperforms breast imaging reporting and data system and ResNet50 in predicting HER2 status in invasive breast cancer.American journal of translational research · 2025Article
- Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation.Frontiers in bioengineering and biotechnology · 2025Article
- Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion.Frontiers in neurology · 2024Article
- Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force.Frontiers in neuroscience · 2024Article
- Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements.BioMed research international · 2024Review
- An Integrated Approach for Real-Time Monitoring of Knee Dynamics with IMUs and Multichannel EMG.Sensors (Basel, Switzerland) · 2023Article
- Prediction of Spasticity through Upper Limb Active Range of Motion in Stroke Survivors: A Generalized Estimating Equation Model.Bioengineering (Basel, Switzerland) · 2023Article
- Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation.Sensors (Basel, Switzerland) · 2023Article
- Quantitative assessment of spasticity: a narrative review of novel approaches and technologies.Frontiers in neurology · 2023Review
- Exploring risk factors for cervical lymph node metastasis in papillary thyroid microcarcinoma: construction of a novel population-based predictive model.BMC endocrine disorders · 2022Article
- 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
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms-including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons-were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.
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What 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.