Abstract

Introduction: In recent times, biosensors have become advanced tools in medical diagnostics, utilizing artificial intelligence (AI) to detect specific biological disorders promptly. AI in the form of OpenPose models with wearable biosensors improves patients' daily routines and physiological changes in Alzheimer patient diagnostics. AI-based patient routine analysis covers complex patterns and offers fast processing with higher accuracy for physiological changes in Alzheimer patients. This model advances the detection of health condition changes, such as understanding patient routines, suggesting disease progression, and improving healthcare outcomes.

Methods: Modern healthcare technology has evolved, leading to the creation of a wearable biosensor that monitors human organs for real-time data analysis. It measures vital signs, physical activity, and sleep patterns to analyse physiological changes in patients. Wearable biosensors are cost-effective devices for detecting biomarkers associated with behavioral and cognitive changes in patients. This model allows caretakers of patients and healthcare professionals to track patients’ health and activities in real time, enabling early activity changes in Alzheimer patient diagnostics.

Results: Utilizing the full potential of AI models like OpenPose, trained on biomarker datasets, this wearable biosensor effectively handles diverse datasets, achieving a precision of 93.27% in Alzheimer patient activity analysis. This study successfully found changes in daily activity, like irregular sleep patterns, abnormal vital signs, and little physical activity. These systems also improve the accuracy of cognitive decline by allowing real-time monitoring of medical component response, which makes them more useful in healthcare diagnostics.

Conclusion: The integration of biosensors with OpenPose is transforming Alzheimer patient diagnostics, offering more accurate and effective solutions. This wearable biosensor-based AI model provides real-time health monitoring, personalized medical care, and early activity change detection and supports further medical component development. In the future, this biosensor-based AI will play an important role in improving global healthcare facilities and patient monitoring, leading to efficient outcomes.