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.

Parkinson's disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers - K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level - with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal's information.

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%.

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