No abstract

Parkinson's disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson's disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson's gait properties without affecting the common behavioral patterns during the clinical practices. Therefore, the Accelerometer Sensor-based Parkinson's Disease Identification System (ASPDIS) is introduced with a kernel-based support vector machine classifier model to make an early prediction of the disease. consequently, the proposed classifier can easily predict various severity levels of Parkinson's disease from the sensor data. The performance of the proposed classifier is compared against the existing models such as random forest, decision tree, and k-nearest neighbor classifiers respectively. As per the experimental observation, the proposed classifier has more capability to differentiate Parkinson's from non-Parkinson patients depending upon the severity levels. Also, it is found that the model has outperformed the existing classifiers concerning prediction time and accuracy respectively.

By building sensor-based alert systems, physical therapists can not only decrease the after-fall repercussions but even save lives.Older people are prone to several diseases, and falling is a regular occurrence for them during this period.Various fall detection systems have recently been developed, with computer vision-based approaches being one of the most promising and effective. Here, the sensor-based data has been analysed for a patient's human fall symptoms. This data has been pre-processed using Gaussian filtering with kernel neural network in which the data has been normalized and trained based on neural network. The trained normalized data has been segmented using encoded Stacked Deconvolutional Network (EnSt-DeConvNet). We found that the suggested method predicts such fall symptoms with the highest accuracy from sensor data. Other algorithms' accuracy results, on the other hand, are also fairly close. Experiments reveal that the suggested technique, when compared to other generally utilized techniques based on multiple cameras fall dataset, produced reliable findings and that our dataset, which consists of more training samples, produced even better results. Experimental results showaccuracy of 96%, Precision of 94%, Recall of 88% and F-1 score of 82%, computational time of 69%.