CS328 Mobile Health Sensing and Analytics · Companion Visualizations

Sensor Data Smoothing & Filtering

Time-domain smoothing

• Smoothing Filters by Hand
• Time-Domain Smoothing Filters
• Window Size vs Step Peaks

From time to frequency

• Building a Signal from Sinusoids
• Aliasing and Nyquist
• Signals and Noise in Frequency
• Time vs Frequency Domain Smoothing

Frequency-domain filtering

• Filter Shapes
• lfilter versus filtfilt

Denoising pipelines

• Denoising an ECG Recording
• Denoising a PPG Recording
• Denoising a Speech Recording
• Denoising a Walking Signal

Counting Steps & Reps

Counting steps

• Collapsing Three Axes into One
• Walking Signal by Placement
• Filtering Out the Noise
• Which Peaks Are Steps
• Verifying Counted Steps

Counting exercise repetitions

• Acceleration vs Rotation
• Wrist Signals During Exercise
• Rep Counting Pipeline
• No Single Setting Fits All
• Same Exercise, Different People
• Best Sensor for Each Activity
• No Single Sensor Fits All

The Activity Recognition Pipeline

• Can Peak Features Tell Activities Apart?
• The Pipeline at a Glance
• What the Data Actually Looks Like
• Pre-processing with a Band-Pass Filter
• Segmenting the Data into Windows
• From Windows to a Feature Table

Feature Extraction

Time and frequency domain

• Sliding-Window Time Features
• Spectrum and Dominant Frequency

Sequence similarity

• Dynamic Time Warping

Decision Trees

Choosing a split

• Information Gain on Cardiac Data
• Threshold Search on Features

Growing and using a tree

• Growing a Decision Tree
• Tree Depth and Overfitting
• Walking the Cardiac Risk Tree

Beyond Decision Trees

Ensembles

• Majority Voting
• Bagging and Bootstrap Samples
• Random Forests
• Boosting

Tuning and Evaluating Classifiers

Validation

• Holdout and Cross-Validation
• Tuning with GridSearchCV

Metrics

• Confusion Matrix and Metrics

Case Studies in Activity Recognition

Activity recognition

• Activity Windows and Features
• Subject Split and Tuning

Exercise recognition

• Feature Families for Exercises
• Sensors, Direction and 39 Classes

Voice-Based Health Analytics

Representing speech

• Framing and the Spectrogram
• The MFCC Pipeline

Voice features

• Pitch and Intensity Contours
• Jitter, Shimmer and HNR

Sensing Heart Rhythm with PPG

From PPG to heart rate

• Band-Pass Filtering the PPG
• Peaks to Heart Rate

Motion and variability

• Peak Detection through Motion
• Heart Rate Variability

Measuring Sleep Patterns

A night of data

• Epochs and the Hypnogram
• Overnight Motion and Heart Rate

Actigraphy

• Per-Epoch Motion Features
• Wake/Sleep Classifier