A Comprehensive review on Artificial Intelligence Based Identification of Neurodegenerative Diseases through Gait Analysis

Abstract

Neurodegenerative diseases, such as Parkinson's disease, Alzheimer's disease, and multiple sclerosis, significantly impact the quality of life and functional abilities of affected individuals. Early and accurate diagnosis is crucial for effective treatment and management of these conditions. This paper presents a novel approach to identifying neurodegenerative diseases using artificial intelligence (AI) through the analysis of gait patterns. By leveraging advanced machine learning algorithms and computer vision techniques, our system analyzes gait characteristics to detect subtle abnormalities indicative of neurodegenerative disorders. The proposed method involves the collection of gait data using wearable sensors and video recordings, followed by feature extraction and classification using AI models. Experimental results demonstrate the effectiveness of our approach in differentiating between healthy individuals and those with neurodegenerative diseases with high accuracy. This AI-based identification system offers a non-invasive, cost-effective, and efficient tool for early diagnosis, potentially enabling timely interventions and improved patient outcomes. The study highlights the promise of integrating AI in healthcare, particularly in the domain of neurodegenerative disease diagnostics.