Parkinson’s Detection Using Voice Features and Spiral Drawings

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Abstract

Parkinson's is a dynamic neurodegenerative disease that presents multiple symptoms that advance over time. Our project proposes an innovative Parkinson's discovery machine learning model that combines both voice examination and spiral drawings assessments to capture numerous angles of the disease's symptomatology. Our approach looks for developing a comprehensive Parkinson’s detection model over different stages and symptoms of the disease. By integrating voice analysis techniques to discern subtle changes in speech patterns and spiral drawing assessments to evaluate motor function, our method aims to provide a more holistic assessment of PD symptoms. By leveraging the complementary strengths of voice analysis and spiral drawing assessments, our proposed PD detection project aims to overcome the limitations of existing approaches and provide clinicians with a more comprehensive model for early detection, diagnosis and monitoring of Parkinson's Disease. Ultimately, this initiative strives to enhance patient outcomes, improve treatment efficacy, and advance our understanding of PD progression.

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Most cited references 12

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Using Convolutional Neural Network to Design and Predict the Forces and Kinematic Performance and External Rotation Moment of the Hip Joint in the Pelvis

Seyed Masoud Ghoreishi Mokri, Newsha Valadbeygi, Irina G. Stelnikova (2024)

In order to improve the dynamic and kinematic adaptability of the hip joint, this paper presented a control attitude and kinematics and torque of the hip joint with power based neural network control. The CNN neural network uses input data only from the limb designed by the medical software, and is trained by different natural and artificially altered step patterns of healthy individuals. This type of network has been used for deep learning to realize adaptive speed control, dynamic and motion attitude, as well as prediction of force and torque performance. Detailed movement and torque tests were performed using MIMICS and ANATOMY AND PHYSIOLOGY software, and the obtained data were checked and varied by a healthy person, and finally, the test results showed that the neural network control system was able to control the selection. It has a variable and high speed with proper adaptation in various conditions. Finally, MATLAB software was used to design and predict the data of the problem, and favorable results were obtained.

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Services Integration in Tanzania e-Government Systems

YAKUBA Yusuph Kibira, MUSTAFA Mohsin, DEO Shao (2024)

The interoperability of information systems for public organizations is a significant opportunity for improved delivery of e-government services. However, the lack of e-government services integration is one of the issues preventing services from effectively reaching citizens in many developing nations, especially in this era of information technology advancement. Consequently, this paper aims to provide background information and a framework for comprehending the relevance of Tanzania's integration of e-government services. To understand the current state of the art and the prospects for system integration in government procedures, a thorough government institution Information Systems analysis was done to understand the magnitude of the problem. The survey revealed that the absence of electronic data exchange between public information systems leads to information system silos, which hinder efficiency and synergy in the provision of electronic services. A framework for e-government service integration is proposed utilizing a design science research approach to explain the possibilities of service integration in the public sector. The framework suggests harmonizing public institutions in e-government project plans, communicating e-government systems through a unified network, and establishing the e-government service catalog.

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Predicting the Performance and Adaptation of Artificial Elbow Due to Effective Forces using Deep Learning

Seyed Masoud Ghoreishi Mokri, Newsha Valadbeygi, Khafaji Mohammed Balyasimovich (2024)

Measuring power transmission in organs poses a significant challenge for researchers in the field, with various methods being explored, including the use of artificial intelligence algorithms. This study focused on developing a new neural network model to predict force transmission and performance in an artificial elbow. Rather than evaluating natural joints, the study simulated a prosthetic model using medical software. Empirical data was collected using MIMICS software to estimate power properties and transmission methods, which were then used to train a neural network in MATLAB. The neural network demonstrated strong performance, particularly with the use of CNN architecture. The model's accuracy was validated by comparing results with experimental data from Anatomy and Physiology Comparison software, showing that the neural network provided precise results.

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Author and article information

Journal

Title: International Journal of Innovative Science and Research Technology (IJISRT)

Abbreviated Title: International Journal of Innovative Science and Research Technology (IJISRT)

Publisher: International Journal of Innovative Science and Research Technology

ISSN (Electronic): 2456-2165

Publication date (Electronic): April 29 2024

Pages: 1159-1163

Article

DOI: 10.38124/ijisrt/IJISRT24APR1575

SO-VID: abe8067b-f948-4029-8031-ac8963c30098

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