Abnormal gesture perception for cognitive disorder

Research AI

1. Abnormal Gesture Perception for Cognitive Disorder Using AL/ML Anupam Gupta Roy Dishant Kar Rohan Agarwal Abhijeet Vishwakarma Team Introduction

2. Early diagnosis and continuous monitoring of cognitive disorders such as Alzheimer's disease, Parkinson's disease, and other forms of dementia are critical for effective treatment and patient care. However, traditional diagnostic methods are often invasive, expensive, and time-consuming, requiring complex medical procedures and frequent clinical visits. Targeted Audience : Healthcare providers Healthcare institutions Caregivers Researchers & Patients Patients Problem Statement

3. Project Overview: Develop an AI/ML-based system to non-invasively detect and analyze abnormal gestures for early diagnosis and continuous monitoring of cognitive disorders like Alzheimer's, Parkinson's, and dementia. The system will use sensor data from cameras and wearables to identify subtle motor changes, enhancing diagnostic accuracy and facilitating timely interventions. Objective: The objective of this project is to create a reliable, non-invasive AI/ML-based system capable of detecting and analyzing abnormal gestures to facilitate early diagnosis and continuous monitoring of cognitive disorders such as Alzheimer's, Parkinson's, and dementia. This system aims to improve diagnostic accuracy, enable timely interventions, and enhance overall patient care and outcomes. Project Overview and Objectives

4. Methodology : The Diagonosis starts with a MOCA Test with a system that we created that tracks hand gestures to check your motor controls accuracy throughout the test which at the end evaluates the potential chances of you being pron to any sort of cognitive disorders that may happen to you in the later stage of life . Objective: The objective of this project is to Early diagnosis and continuous monitoring of cognitive disorders such as Alzheimer's disease, Parkinson's disease, and other forms of dementia are critical for effective treatment and patient care. However, traditional diagnostic methods are often invasive, expensive, and time-consuming, requiring complex medical procedures and frequent clinical visits. Methodology and Implementation

5. Long-Term Monitoring: Extend the system's capabilities for long-term monitoring to track disease progression and assess treatment effectiveness over time. Multi-Sensor Integration Develop decision support tools for healthcare providers based on the system's analysis to assist in treatment planning and patient management. Long-Term Monitoring Future Works: Clinical Decision Support Multi-Sensor Integration Long-Term Monitoring Result and Future Work