Parkinson’s disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson’s disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson’s disease patients is the unavailability of reliable procedures for diagnosing Parkinson’s disease. In the literature, we observed different methods for diagnosing Parkinson’s disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson’s disease is a difficult task because the important features that can help in detecting Parkinson’s disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson’s disease and develop a reliable model which can diagnose Parkinson’s disease at its early stages. Early diagnostic systems for the detection of Parkinson’s disease are needed to diagnose Parkinson’s disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson’s disease rating scale, known as the Total Unified Parkinson’s Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson’s disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson’s disease in its early stages.

Objectives: The aim of this study was (i) to design and develop a portable BCA device for measuring body composition parameters such as body weight, body fat (BF) %, total body water (TBW), fat-free mass (FFM), muscle mass (MM), and bone mass (BM); (ii) to validate the developed portable BCA with the Tanita MC 980 MA BCA device. Methods: For this current study, two hundred healthy and obese subjects, whose ages ranged from 8 to 12 years (8.4 ± 1.7), were considered. Results: The highest percentage difference between the two study groups was found to be in BFat (50.39%), followed by body mass index (BMI) (41.73 kg), FFM (38.32 kg), and MM (37.89 kg), and this was found to be statistically significant. The results obtained from the designed prototype of the body composition analyzer were validated using Tanita MC 980MA BCA. The overall error% was calculated as ±3% for measuring the different body composition parameters. Conclusions: Due to its low standard error and high overall accuracy, the BCA prototype demonstrates the potential to be a dependable instrument for evaluating and tracking the body composition of children.