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

Two useless research ideas put together make this doubly useless.

1. Predicting failure to recover.

2. Assessments
   

The latest crapola here:

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

Sulaiman Mazlan 1 , Hisyam Abdul Rahman 1* , Abdul Rahman A.A Emhemed2,3 ,
Siti Nor Zawani Ahmmad 4 , Muhammad Khair Noordin5 , Nurul Aisyah Mohd
Rostam Alhusni6 , Muhammad Najib Abdullah7
1Faculty of Electrical and Electronic Engineering,
Universiti Tun Hussein Onn Malaysia, Johor, MALAYSIA
2Faculty of Technical Engineering,
Bright Star University, El-Brega, LIBYA
3Faculty of Engineering,
Bani Waleed University, LIBYA
4Insrumentation and Control Engineering Section,
Universiti Kuala Lumpur, MITEC, Johor, MALAYSIA
5School of Education, Faculty of Social Sciences and Humanities,
Universiti Teknologi Malaysia, Johor, MALAYSIA
6Occupational Therapy Department,
SOCSO Tun Razak Rehabilitation Centre, Melaka, MALAYSIA
7Techcare Innovation Sdn. Bhd., Johor Bahru, MALAYSIA
*Corresponding Author
DOI: https://doi.org/10.30880/ijie.2023.15.04.020
Received 16 February 2023; Accepted 12 September 2023; Available online 28 August 2023

Abstract: 

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.