Multiple regression analysis of repetitive finger tapping parameters

Miler-Jerkovic, Vera; Djurić-Jovičić, Milica; Perovic-Belic, Minja; Ječmenica-Lukić, Milica; Petrović, Igor; Radovanović, Saša; Kostić, Vladimir; Popović, Mirjana

doi:10.1109/telfor.2014.7034465

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Multiple Linear Regression (MLR) is the most fundamental and straightforward approach for experimental analysis and data processing in analytical contexts. It serves as a sophisticated statistical approach for elucidating correlations between multiple input predictors and a single response variable [36,37]. The response variables y exhibit linear correlations with multiple predictor variables.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

“…These values are determined through the process of training the samples. The majority of studies that have used MLR to predict upper limb performance in stroke rehabilitation have found a strong correlation between the MLR and the clinical scales [9,33,36]. In this study, the data sets were divided into two, which allocated 100 data sets for training and 50 data sets for testing.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

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Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Mazlan

2023

IJIE

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Various predictive models, both linear and non-linear, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Artificial Neural Network (ANN), were frequently employed for predicting the clinical scores of stroke patients. Nonetheless, the effectiveness of these predictive models is somewhat impacted by how features are selected from the data to serve as inputs for the model. Hence, it's crucial to explore an ideal feature selection method to attain the most accurate prediction performance. This study primarily aims to evaluate the performance of two non-motorized three-degree-of-freedom devices, namely iRest and ReHAD using MLR, PLS and ANN predictive models and to examine the usefulness of including a hand grip function with the assessment device. The results reveal that ReHAD coupled with non-linear model (i.e. ANN) has a better prediction performance compared to iRest and at once proving that by including the hand grip function into the assessment device may increase the prediction accuracy in predicting Motor Assessment Scale (MAS) score of stroke subjects. Furthermore, these findings imply that there is a substantial association between kinematic variables and MAS scores, and as such the ANN model with a feature selection of twelve kinematic variables can predict stroke patients' MAS scores.

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Section: Multiple Linear Regressionmentioning

confidence: 99%

Section: Multiple Linear Regressionmentioning

confidence: 99%

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Mazlan

2023

IJIE

View full text Add to dashboard Cite

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“…Multiple Linear Regression (MLR) approaches is the basic and simple method for data processing in analytical data [19]. MLR is a powerful statistical tool finding relationships between one dependent and multiple independent variables [20][21][22]. Several studies shows that four kinematic variables were high enough to result a good predictive performance of a regression model [6,23].…”

Section: Performance Of Assessmentmentioning

confidence: 99%

Non-motorised Rehabilitation Device for Performance Assessment in Upper Limb Stroke Rehabilitation: A Pilot Study

Mazlan

Rahman

KaderIbrahim

et al. 2022

IJIE

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Stroke patients with upper limb disability restricted to carry out their activities of daily living. The patient needs a motivation to recover from a stroke and the patient also needs to go through a rehabilitation process at the same time. The conventional rehabilitation process scoring systems are always subjective, lack reliability and relies heavily on the ability of the trained physiotherapist that providing only rough estimates on motor function. On the other hand, robot-based assessments are objective, repeatable, and could potentially reduce the assessment time. Therefore, a simple non-motorized device was developed as a tool to objectively assess hand function of stroke patients. This study was carried out to investigate the suitability of using thedeveloped device with stroke patient populations and to evaluate the performance of clinical scores prediction of the stroke patients. A total of five patients with upper limb disability following stroke consented to take part in this study. Twelve predictive variables were investigated, relating to the total movement time, velocity, strategy, accuracy, and smoothness from three robotic assessment modules which are Draw I, Draw Diamond and Draw Circle. The hardware for measuring elbow angle hasbeen developed to measurethe shoulder movement performedby patient during the assessment process. In addition to that, the shoulder movement calculation method has been proposed and validated.The findings indicatethat the performance of prediction for all assessment modules hasbeen increased after implementing the shoulder movement calculation. It is recommended this calculation method to be used in conjunction with kinematic variables to carry out the data acquisition process in the future for improvement of effectiveness and accuracy of the robotic assessment.

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“…Multiple Linear Regression (MLR) approaches is the basic and simple method for experimental and data processing in analytical data [30]. MLR is a powerful statistical tool finding relationships between one dependent and multiple independent variables [31][32][33]. In MLR, the dependent variables y is linearly correlated to multiple independent variables x1, x2, …, xn.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

“…These parameters are estimated by training the samples. Most analysis to predict the upper limb assessment in stroke rehabilitation using MLR shows strong correlation with the clinical scales [13,27,32].…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Multiple Linear Regression in Predicting Motor Assessment Scale of Stroke Patients

Mazlan

Rahman

Ibrahim³

et al. 2021

IJIE

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The Multiple Linear Regression (MLR) is a predictive model that was commonly used to predict the clinical score of stroke patients. However, the performance of the predictive model slightly depends on the method of feature selection on the data as input predictor to the model. Therefore, appropriate feature selection method needs to be investigated in order to give an optimum performance of the prediction. This paper aims (i) to develop predictive model for Motor Assessment Scale (MAS) prediction of stroke patients, (ii) to establish relationship between kinematic variables and MAS score using a predictive model, (iii) to evaluate the prediction performance of a predictive model based on root mean squared error (RMSE) and coefficient of determination R2. Three types of feature selection methods involve in this study which are the combination of all kinematic variables, the combination of the best four or less kinematic variables, and the combination of kinematic variables based on p < 0.05. The prediction performance of MLR model between two assessment devices (iRest and ReHAD) has been compared. As the result, MLR model for ReHAD with the combination of kinematic variables that has p < 0.05 as input predictor has the best performance with Draw I (RMSEte = 1.9228, R2 = 0.8623), Draw Diamond (RMSEte = 2.6136, R2 = 0.7477), and Draw Circle (RMSEte = 2.1756, R2 = 0.8268). These finding suggest that the relationship between kinematic variables and MAS score of stoke patients is strong, and the MLR model with feature selection of kinematic variables that has p < 0.05 is able to predict the MAS score of stroke patients using the kinematic variables extracted from the assessment device.

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Multiple regression analysis of repetitive finger tapping parameters

Cited by 4 publications

References 10 publications

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Non-motorised Rehabilitation Device for Performance Assessment in Upper Limb Stroke Rehabilitation: A Pilot Study

Multiple Linear Regression in Predicting Motor Assessment Scale of Stroke Patients

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