Optimisation of Composite Briquette Made From Sawdust / Rice Husk Using Starch and Clay Binder

Upper limb spasticity (ULS) is a common pathophysiological changes manifest by structural damage towards the central nervous system (CNS) that includes brain and spinal cord. The current clinical practice of assessment utilizes Modified Ashworth Scale (MAS) as subjective tool to measure severity spasticity. Lack objective value, poor sensitivity in detecting minimal changes, dependency interpretation assessing clinicians are several reasons inter intra-rater variability measurement using MAS. These limit use MAS diagnosing, treating, monitoring especially inexperienced clinicians, hence leading inadequate management. To overcome this problem, study carried out quantify develop data-driven model ULS detection based on characteristics detect existence according identified adopted train machine learning models for smart diagnosis purpose assist physicians effectively manage