Browsing by Author "Asmarani Ahmad Puzi"

Muscle spasticity is a condition that occurs in patients with neurological disorders when their muscles are stiff, tight, and resistant to stretching. The current assessment method, relying on the subjective judgment of therapists using the Modified Ashworth Scale (MAS), introduces variability that may affect the rehabilitation process. In addition, many existing computational models are not aligned with clinical standards such as MAS, limiting their practical adoption in clinical settings. They also often overlook direction-specific movement phases and fail to distinguish muscle responses across different axes and muscle groups. To address the limitations, this research aimed to develop a quantitative assessment method by muscle spasticity characteristics based on mechanomyography (MMG) signals and MAS levels, utilising machine learning techniques. A Quantitative Spasticity Assessment Technology (QSAT) platform has been developed which consists of two sensors that were tri-axial accelerometer mechanomyography (ACC-MMG) functioning in measure acceleration of biceps and triceps muscle contraction and potentiometer to measure the angular position of forearm during flexion and extension movement. A comprehensive investigation was conducted to assess muscle spasticity level by recording ACC-MMG signals from patients' forearm musculature during flexion and extension movements using QSAT platform. A total of 30 patients with neurological disorders were classified into five MAS levels (0, 1, 1+, 2, and 3), along with 10 healthy subjects serving as a baseline group. The pre-processed data comprised 48 extracted features from ACC-MMG signals along the x, y, and z axes for both flexion and extension movements of the biceps and triceps. These features corresponded to the longitudinal, lateral, and transverse muscle orientations. For both flexion and extension movements, machine learning models were trained using the selected subset of 25 significant features and the full set of 48 features respectively, with performance comparisons made to identify the most effective approach. Various machine learning models algorithms, including Linear Discriminant Analysis (LDA), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN), were tested. The KNN-based classifier demonstrated the highest performance using a 90% training and 10% testing data split surpassing the performance of other classifiers. Specifically, at k = 15 using Euclidean distance, the KNN achieved an accuracy of 91.29% for flexion using the significant features, with corresponding precision, recall, and F1-score of 91.64%, 91.25%, and 91.47%, respectively. For extension, the same configuration resulted with 96.30% for extension using the full feature set, with precision, recall, and F1-score of 96.53%, 96.30%, and 96.33%, respectively. These results indicate high classification performance, with minimal false positives or false negatives, particularly in distinguishing between different MAS levels. This research suggests that the muscle characteristic model embedded in the QSAT can serve as a standardised and objective assessment tool for measuring the spasticity level of the affected limb using computational method, leading to support clinical evaluations and enabling more effective rehabilitation strategies.

Robotic assisted training platforms have become a significant alternative to conventional training platforms as clinical therapeutic assistance to accommodate the increasing demand for neurological disorder physical treatments. Patients with neurological disorders usually experience conditions where their muscles are stiff, tight and prone to resist upon stretching, which in essence define muscle spasticity. The current method of muscle spasticity assessment is based on subjective assessment by therapists who heavily rely on their inner intuition, experience and skills. Based on the assessment, proper rehabilitation training tasks are prescribed as part of the training regimen. This, however, could be proven ineffective over the long run if the assessment is not done accurately. More so, in the case of robotic-assisted training systems used in training tasks, the deficiency in accurate information on muscle spasticity could largely affect any control strategy adopted to govern the robotic system. In order to address this problem, the research proposed to leverage on a synergetic combination of Modified Ashworth Scale (MAS) spasticity assessment tool and adaptive-impedance controller framed under a hybrid automata (HA) model applied on a patented upper limb rehabilitation platform, namely the Automated Muscle Spasticity Assessment System (A- MSAS). This required a dedicated spasticity characteristics model with control strategy during the assessment of muscle spasticity and an adaptive control based on impedance dynamics for the execution of the training tasks by A-MSAS. Spasticity characteristics model was developed using classification method and position-based impedance controller was adopted in strategizing the control of the A-MSAS. The latter was achieved through a dynamic mapping of the patient’s recovery parameters to the control parameters. The research involved clinical measurements of muscle spasticity from 39 subjects diagnosed with neurological disorders to classify the MAS scores quantitatively. From the research of assessment regimen it was found that by using spasticity characteristics model, the rate in predicting the MAS score of the subjects was 92.86% accurate. Meanwhile for training regimen, the adopted control strategy has resulted in an average angular velocity reduction, by 28.75% for pre-catch phase while average angular velocity increase which there were observable boosts by 46.46% for post-catch phases. The controller objective has been proven by allowing a degree of compliance even as A-MSAS platform dynamically deviated from the desired trajectory; proportional to the feedback received. Based on the findings, it was conclusively justified that an objective spasticity assessment prior to the training task would enhance the adapt- ability of the control strategy. This leads to a minimized muscle strain instigated from the feedback of spasticity characteristics pattern, hence warranting a more effective rehabilitation training.