A spasticity assessment method for voluntary movement using data fusion and machine learning

Y Chen, S Yu, Q Cai, S Huang, K Ma, H Zheng… - … Signal Processing and …, 2021 - Elsevier
The assessment of spasticity under voluntary movement is helpful for the therapist to
comprehensively assess the patient's dyskinesia. However, current researches focus on …

AI-based automatic system for assessing upper-limb spasticity of patients with stroke through voluntary movement

IJ Lee, YH Hu, PC Hsiao, SY Yang… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Spasticity is a common complication for patients with stroke, but only few studies investigate
the relation between spasticity and voluntary movement. This study proposed a novel …

Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision

J Yee, CY Low, N Mohamad Hashim, NA Che Zakaria… - Diagnostics, 2023 - mdpi.com
The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The
qualitative description of MAS has resulted in ambiguity during spasticity assessment. This …

Biomechanical parameter assessment for classification of Parkinson's disease on clinical scale

AH Butt, E Rovini, D Esposito, G Rossi… - International …, 2017 - journals.sagepub.com
The primary goal of this study was to investigate computerized assessment methods to
classify motor dysfunctioning of patients with Parkinson's disease on the clinical scale. In this …

A novel quantitative spasticity evaluation method based on surface electromyogram signals and adaptive neuro fuzzy inference system

S Yu, Y Chen, Q Cai, K Ma, H Zheng… - Frontiers in neuroscience, 2020 - frontiersin.org
Stroke patients often suffer from spasticity. Before treatment of spasticity, there are often
practical demands for objective and quantitative assessment of muscle spasticity. However …

A regression-based framework for quantitative assessment of muscle spasticity using combined EMG and inertial data from wearable sensors

X Zhang, X Tang, X Zhu, X Gao, X Chen… - Frontiers in …, 2019 - frontiersin.org
There have always been practical demands for objective and accurate assessment of
muscle spasticity beyond its clinical routine. A novel regression-based framework for …

Quantifying spasticity with limited swinging cycles using pendulum test based on phase amplitude coupling

CH Yeh, HWV Young, CY Wang… - … on Neural Systems …, 2016 - ieeexplore.ieee.org
Parameters derived from the goniometer measures in the Pendulum test are insufficient in
describing the function of abnormal muscle activity in the spasticity. To explore a quantitative …

An automatic evaluation method for Parkinson's dyskinesia using finger tapping video for small samples

Z Li, K Lu, M Cai, X Liu, Y Wang, J Yang - Journal of Medical and …, 2022 - Springer
Purpose The assessment of dyskinesia in Parkinson's disease (PD) based on Artificial
Intelligence technology is a significant and challenging task. Based on the representative …

[HTML][HTML] Artificial neural network learns clinical assessment of spasticity in modified Ashworth scale

JH Park, Y Kim, KJ Lee, YS Yoon, SH Kang… - Archives of Physical …, 2019 - Elsevier
Objective To propose an artificial intelligence (AI)-based decision-making rule in modified
Ashworth scale (MAS) that draws maximum agreement from multiple human raters and to …

Analysis of machine learning-based assessment for elbow spasticity using inertial sensors

JY Kim, G Park, SA Lee, Y Nam - Sensors, 2020 - mdpi.com
Spasticity is a frequently observed symptom in patients with neurological impairments.
Spastic movements of their upper and lower limbs are periodically measured to evaluate …