Spasticity rule-based system modeling for upper limb spasticity diagnosis according to modified ashworth scale

This thesis presents the diagnostic modeling of a Spasticity Rule-based System to develop the Upper Limb Spasticity Measurement Device (ULSMeD), a diagnosis device designed for quantitatively assessing spasticity. Spasticity is characterized by increased muscle tone and exaggerated tendon jerks, significantly impairing mobility, and quality of life. Current assessment methods are subjective and rely on clinical judgment, leading to issues with consistency and accuracy. This study bridges the gap by employing a comprehensive approach that combines clinical assessment techniques with mathematical modeling. It examines the range of motion (ROM) and force resistance across a 208 clinical dataset. The research includes a multi-phase approach, beginning with identifying a quantitative relationship between the Modified Ashworth Scale (MAS) profile and angular acceleration. Following this finding, a Spasticity Rule-Based System was developed for the decision-making system, leading to the development of the ULSMeD using sensor technologies. The device's prototype integrates components of the MPU6050 angle sensor and FSR402 force sensor embedded with the Internet of Things (IoT) via a mobile phone User Interface. Sensor testing showed exceptional performance with over 99.6% accuracy in angle measurements and approximately 98.5% accuracy in force measurements. Clinicians played a main role in the evaluation phase, providing feedback on the device's usability and functionality. The device was evaluated in clinical settings with four clinicians showing a 60% exact accuracy and an ICC of 73.5%. This research marks a significant advancement in spasticity assessment, offering a more objective, standardized, and user-friendly tool. It sets up the foundation for upcoming improvements in diagnosing spasticity, which could have a positive impact on patient outcomes during stroke rehabilitation.