Biometric-Authenticated Wearable Health Monitoring System for Remote Patient Care and Sentiment-Based Analysis

Smart overview of the Invention

The patent application describes a biometric-authenticated wearable health monitoring system designed for remote patient care. It integrates fingerprint, facial, and voice recognition to ensure secure association of physiological data with individual users. The system monitors vital signs such as heart rate, oxygen saturation, and movement, transmitting data to electronic medical/health record (EMR/EHR) systems in real time while maintaining privacy compliance. Through machine learning, it assesses disease risk, detects mobility decline, and performs sentiment-based analysis of speech and behavior, enhancing therapy compliance tracking across wellness programs.

Technological Integration

The system leverages advanced technologies, including large language models (LLMs), to enhance contextual understanding and sentiment interpretation from unstructured speech or text inputs. This integration allows for real-time alerts regarding health anomalies or non-compliance, facilitating timely clinical interventions. The platform combines biometric authentication, predictive analytics, and seamless EMR/EHR integration to advance personalized and secure care, addressing the limitations of existing systems in data security and integration.

Applications and Benefits

Designed for chronic disease management, elderly care, mental health rehabilitation, and occupational therapy, the system provides real-time healthcare insights and compliance monitoring. It supports dynamic patient-specific insights across physical and cognitive domains, strengthening clinical decision-making. The modular workflows allow for scalable architecture, adaptable across various healthcare applications, ensuring accurate and authenticated data crucial for clinical decision-making.

Background and Challenges

Traditional methods of fitness data management are inefficient, requiring manual entry and consultation of multiple records. While digital solutions offer better organization, they remain labor-intensive. Wearable technology has improved data capture, yet translating raw data into actionable insights remains challenging. Sentiment-aware analysis using natural language processing (NLP) and artificial intelligence models provides an opportunity for more accurate mental health assessments, which current systems lack.

Addressing Limitations

• Insecure and Misattributed Data: The system addresses data misattribution by employing robust biometric authentication, crucial for accurate clinical decision-making.
• Fragmented Systems: It ensures seamless integration with healthcare provider platforms and EMR/EHR systems, reducing manual data transfer errors.
• Real-Time Monitoring: Provides continuous vital tracking and real-time alerts for chronic health patients, enhancing immediate medical intervention capabilities.
• Mental Health Rehabilitation: Enables secure monitoring and attribution of therapy adherence, addressing challenges in mental health rehabilitation programs.