Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.
For a population that is moving towards an elderly stage of development, Parkinson's disease (PD) is characterized in the second place for the most common chronic progressive neurodegenerative illness in the world after Alzheimer's disease, which regularly affects older generation. In the next 30 years, this amount is estimated to double due to the increase in the number of ageing people, as age is the leading key risk feature for the start of PD. There are a variety of medications, such as levodopa available to treat PD. With the latest advancement in healthcare technology, current researches permit the monitoring of PD with the application of wearable sensor technology. From previous studies, researchers have realized the application of wearable sensors as a useful tool that had the capability to differentiate various types ofPD symptoms using uni-modal sensor or bi-modal sensors (accelerometer and gyroscope). Therefore, early diagnosis of PD through multimodal wearable technology can be considered for this aim. In this paper, the data are collected using on-body triaxial wearable sensors (accelerometer, gyroscope and magnetometer) for classifying people with Parkinson (PWP) from healthy controls. The system performance was characterized based on to-fold cross validation method, applying the proposed time and frequency domain features and classification algorithms. The strength ofthe proposed method has been evaluated through several performance measures. In summary, these results show that the proposed machine learning techniques had ability in differentiating PWP from healthy controls with highest average accuracy, sensitivity, specificity and ROC of above 88%.
Over the past fifteen years, quantitative monitoring of human motor control and movement disorders has been an emerging field of research. Recent studies state the fact that Malaysia has been experiencing improved health, longer life expectancy, and low mortality as well as declining fertility like other developing countries. As the population grows older, the prevalence of neurodegenerative diseases also increases exponentially. Parkinson disease (PD) is one of the most common chronic progressive neurodegenerative diseases that are related to movement disorders. After years of research and development solutions for detecting and assessing the symptoms severity in PD are quite limited. With current ongoing advance development sensor technology, development of various uni-modal approaches: technological tools to quantify PD symptom severity had drawn significance attention worldwide. The objective of this review is to compare some available technological tools for monitoring the severity of motor fluctuations in patients with Parkinson (PWP).
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