SeVA: Senior Virtual Assistant for Healthcare

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Overview

SeVA is an advanced healthcare support system designed to empower seniors with proactive health monitoring and personalized care recommendations. By leveraging real-time data analysis, advanced signal processing, and AI-driven insights, SeVA bridges the gap between conventional healthcare systems and senior-specific needs. The system seamlessly integrates wearable devices, IoT sensors, and patient data to provide daily, weekly, and monthly insights into health trends, detect anomalies, and deliver actionable feedback for improved well-being and preventive care.

SeVA Framework

Fig: SeVA Framework

System Architecture and Workflow

1. Data Collection Phase

  • Input: Data from wearable devices, health tracking apps, and IoT-enabled health sensors, capturing metrics like heart rate, respiratory rate, temperature, and activity levels.
  • Processing: Incoming data is preprocessed, cleaned, and transformed into structured datasets for feature computation.
  • Output: Ready-to-analyze health datasets, timestamped for real-time and periodic analysis.

2. Feature Computation Phase

  • Core Algorithms: Incorporates statistical analysis, signal processing, information theory, and Poincaré-based features to extract meaningful insights.
  • Processing:
    • Statistical Features: Mean, standard deviation, skewness, kurtosis, and autocorrelation to understand variability and patterns.
    • Poincaré Features: SD1, SD2, and SD1/SD2 ratio to analyze variability in physiological data.
    • Information Theory Features: Shannon entropy, Rényi entropy, and Tsallis entropy to measure complexity and irregularity in data signals.
  • Output: Comprehensive health feature profiles across daily, weekly, and monthly intervals.
Anomaly Detection in a patient’s data (Original, Statistical, Information Theory Features)
Anomaly Detection in a patient’s data (Original, Statistical, Information Theory Features)

Fig: Anomaly Detection in a patient’s data (Original, Statistical, Information Theory Features)

3. Anomaly Detection Phase

  • Key Components:
    • Threshold-Based Analysis: Detects anomalies using z-scores and dynamic thresholds tailored to each health feature.
    • Multi-Feature Context: Considers multiple health parameters simultaneously for detecting subtle but significant changes.
  • Output: Alerts for potential health risks based on deviations from baseline trends.

4. Insights & Recommendations Phase

  • Key Features:
    • Proactive Alerts: Notifies caregivers or users of potential health concerns (e.g., abnormal heart rate or irregular breathing).
    • Personalized Recommendations: Provides tailored advice based on detected anomalies, including exercise routines, diet adjustments, or medical consultations.
    • Long-Term Trends: Visualizes health data over time to identify patterns and inform preventive measures.
  • Output: Interactive dashboards and visualizations for caregivers, healthcare providers, and seniors.

Key Features

  • Real-Time Health Monitoring: Continuously tracks physiological metrics with minute-level granularity.
  • Advanced Feature Extraction: Employs statistical, signal processing, and entropy-based techniques for in-depth health analysis.
  • Anomaly Detection: Identifies upper and lower anomalies to signal potential health risks.
  • Personalized Insights: Delivers actionable recommendations for improving health and well-being.
  • Activity Analysis: Filters data by activity (e.g., sleeping, exercising) to provide context-specific insights.

Tools and Technologies

  • Feature Extraction: Skewness, Kurtosis, Shannon Entropy, Poincaré analysis.
  • Visualization: Matplotlib, Pandas for time-series plotting and analysis.
  • Programming & Platforms: Python, NumPy, SciPy, Google Drive integration for data handling.
  • Anomaly Detection: Z-score-based thresholds for identifying deviations.
  • Data Collection: Wearable devices, IoT-enabled sensors, health tracking apps.

Outcomes and Impact

Performance Improvements:

  1. Enhanced Anomaly Detection:
    • Improved sensitivity to physiological changes by integrating multi-feature analysis.
  2. Scalable Framework:
    • Modular design allows integration with additional metrics and devices.

Healthcare Benefits:

  • Preventive Care: Early detection of health anomalies reduces risks of complications.
  • Empowering Seniors: Enhances self-care capabilities with personalized insights.
  • Caregiver Support: Provides actionable data to caregivers, reducing monitoring burden.

SeVA is a transformative system in senior healthcare, combining AI-powered analytics and personalized care strategies. Its end-to-end workflow ensures continuous monitoring, timely alerts, and actionable insights, fostering a healthier and more independent lifestyle for seniors.

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