Distinguishing Patients With a Coordination Disorder From Healthy Controls Using Local Features of Movement Trajectories During the Finger-to-Nose Test

Aguilar, Venustiano Soancatl; Manzanera, Octavio Martinez; Sival, Deborah A; Maurits, Natasha M.; Roerdink, Jos B. T. M.

doi:10.1109/tbme.2018.2878626

Cited by 4 publications

(14 citation statements)

References 33 publications

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“…These data were collected as part of a larger project aiming to quantify the SARA performances in children with clinical coordination disorders, that were partially analysed in previous studies concerning the assessment of the SARA finger to nose test, alone 11,12 . The study was conducted in accordance with the principles of the Declaration of Helsinki (2013) and the research and integrity codes of the University Medical Center Groningen (UMCG).…”

Section: Methodsmentioning

confidence: 99%

“…We based our feature extraction strategy on the known differences in smoothness and regularity of movement between EOA, DCD and healthy age-matched controls, while taking into account the results of our previous studies 11,12 . Details are provided in the Appendix.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Here, we used 34 features to obtain the high classification accuracy. This may not always be necessary, depending on the exact classification task: in another study from our group 11 , where we tried to distinguish between healthy controls and movement disorder patients (Adult Onset Ataxia, EOA and DCD combined)…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

“…Similarly, in two previous studies from our own group we extracted features from IMU signals obtained during finger to nose test performance to distinguish between EOA and DCD patients and healthy controls. We achieved 84% classification accuracy in distinguishing EOA patients and healthy controls, using only local curvature and instantaneous speed 11 . Comparing EOA and DCD patients and healthy controls, we correctly classified 87.4% of healthy controls, 74.4% of EOA and 24.8% of DCD patients by using different features such as intertrial variability, similarity of the movement trajectory to a straight line and principal component analysis to decompose the movements as input to train a random forest classifier 12 .…”

Section: Introductionmentioning

confidence: 99%

“…Features were selected based on their classification performance in earlier studies and their relevance for quantifying the most common problems a patient may present during clinical evaluation, such as dysmetria. For the finger to nose test we extracted features similar to those used by Martinez-Manzanera et al (2018) and added local curvature and instantaneous speed in line with Aguilar et al (2019). For the finger chasing test, we incorporated features that assess smoothness of movement 13 , similar to the features used for the finger to nose test.…”

Section: Introductionmentioning

confidence: 99%

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Instrumented classification of patients with early onset ataxia or developmental coordination disorder and healthy control children combining information from three upper limb SARA tests

Dominguez-Vega

Dubber

Elting

et al. 2021

European Journal of Paediatric Neurology

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Section: Methodsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Instrumented classification of patients with early onset ataxia or developmental coordination disorder and healthy control children combining information from three upper limb SARA tests

Dominguez-Vega

Dubber

Elting

et al. 2021

European Journal of Paediatric Neurology

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Non-invasive Approach for Disease Diagnosis

Gade

Baskar

2020

Machine Learning and Information Processing

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2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings

Tang

Ooijen

Sival

et al. 2022

Sensors

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Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer’s assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders.

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Distinguishing Patients With a Coordination Disorder From Healthy Controls Using Local Features of Movement Trajectories During the Finger-to-Nose Test

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Cited by 4 publications

References 33 publications

Instrumented classification of patients with early onset ataxia or developmental coordination disorder and healthy control children combining information from three upper limb SARA tests

Instrumented classification of patients with early onset ataxia or developmental coordination disorder and healthy control children combining information from three upper limb SARA tests

Non-invasive Approach for Disease Diagnosis

2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings

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