Wearable Health Device Data Analysis Full Syllabus

Module 1: Introduction to Wearable Devices

• What are wearable health devices?
• Categories: Smartwatches, fitness bands, biosensors, ECG patches
• Applications: Fitness tracking, sleep monitoring, chronic care
• Key companies: Apple, Fitbit, Garmin, WHOOP, Oura

Module 2: Understanding Sensor Data

• Types of sensors:
  • Accelerometers
  • Gyroscopes
  • Optical sensors (PPG)
  • ECG, EMG sensors
  • Skin temperature & SpO2
• Sampling rate, data resolution & accuracy
• Signal-to-noise ratio and filtering

Module 3: Data Acquisition & Storage

• How data is collected: BLE, APIs, SDKs
• Real-time vs batch data logging
• Popular APIs: Fitbit API, Apple HealthKit, Google Fit, Garmin SDK
• Data formats: JSON, CSV, FHIR

Module 4: Data Preprocessing & Cleaning

• Noise removal & filtering techniques
• Missing data handling
• Data normalization
• Segmenting and labeling activity data

Module 5: Exploratory Data Analysis (EDA)

• Visualizing time-series health data
• Correlation analysis (e.g., HR vs sleep)
• Feature extraction: HRV, step count, breathing rate, stress index
• Anomaly detection basics

Module 6: Machine Learning for Wearable Data

• Classification: Activity recognition (walk/run/sleep)
• Regression: Predicting calorie burn or fatigue level
• Clustering: Sleep pattern grouping
• Real-time health monitoring (e.g., AFib detection)
• Libraries: Scikit-learn, XGBoost

Module 7: Deep Learning on Sensor Data

• CNNs for time-series and ECG signals
• LSTMs & GRUs for sequence prediction (e.g., HR trends)
• Autoencoders for anomaly detection
• Models deployment on-device (TinyML, TensorFlow Lite)

Module 8: Health Monitoring Use Cases

• Sleep stage detection (light/deep/REM)
• Stress prediction using HRV
• Fall detection in elderly
• Continuous glucose monitoring (CGM)
• Cardiac arrhythmia detection

Module 9: Real-Time Data Processing & Edge Computing

• Streaming data pipelines (MQTT, Kafka)
• Edge AI models for wearables
• Latency reduction techniques
• Alert system setup for emergencies (e.g., sudden drop in SpO2)

Module 10: Data Visualization & Dashboards

• Using tools like Streamlit, Dash, or Power BI
• Personalized health reports generation
• Real-time vitals tracking dashboard
• Integration with mobile/web apps

Module 11: Ethics, Privacy & Regulatory Compliance

• GDPR & HIPAA compliance in wearables
• Data encryption, secure APIs
• Ethical challenges in personalized tracking
• Consent management in health tech

Module 12: Tools, Platforms & Datasets

• Tools: Python, TensorFlow, PyTorch, Jupyter, OpenSignals
• Platforms: Fitbit Studio, Google Fit, HealthKit, Edge Impulse
• Public Datasets:
  • WESAD (Wearable Stress & Affect Detection)
  • PAMAP2 (Physical Activity Monitoring)
  • MHEALTH Dataset
  • Sleep-EDF dataset