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ItemAutomatic assessment system for quantification and classification of pure tremor and Parkinsonian tremor( 2019)With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor(essential tremor). Tremor is an almost incurable disease, and it gets worse with the increase in age and improper diagnosis. The clinicians provide the diagnosis which can only limit the severity, but the patients have to visit the doctor frequently. The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In most of the countries especially which are developing or underdeveloped due to the inadequate facility or lack of rehabilitation centers for neurological disorder patient the monitoring and adequate assistance are not possible. Moreover, the improper coordination of the patient with the doctors could lead to a severer case of tremor and early ambulatory condition. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease motor rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. Automatic quantification of severity scores can help the clinician to accurately and quickly recognize the severity of tremor in the patient. Hence they can provide the necessary quantity of dosage of drugs to the patient. Continuous quantification of severity of tremor can also help the clinician and caretakers in assessing the improvement in patients concerning the diagnosis being assigned to them. Thereby the dosage of drugs can be reduced or increased accordingly, and the caretakers can provide less or more frequent assistance. The current trends in technological advances have been assertive in solving critical healthcare problems. Various devices integrated with the machine learning tools can prove highly beneficial in building an automatic assessment tool for quantification of tremor severity in agreement to the clinical rating scale. In our research, we focus on developing a system for automatic quantification and classification of tremor which can provide accurate severity scores and differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device. In this research, a study was conducted in the neuro clinic to assess the upper wrist movement of the patient suffering from pure(essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed per the Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fft based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as an input feature for various classification tools for distinguishing the PT and ET tremor types. K-nearest neighbor-based approach gave superior performance results in the quantification of tremor severity while SVM classifier using radial basis kernel showed excellent results in the classification of both tremor types. Thus, an automatic system for efficient quantification and classification of tremor was developed using feature extraction methods and supervised learning classification tools.