// Machine Learning
Wearable AI Analytics
Machine-learning models for classifying activity, sleep stages, and stress from wearable sensor data.
Role
Artificial Intelligence Engineer
Duration
Mar 2024 - Aug 2024
Project Preview
Wearable AI Analytics
// Project overview
The Project
This project processes wearable sensor data and uses machine-learning and deep-learning models to classify user activity, sleep stages, and stress levels for real-time coaching applications.
The Challenge
Processing noisy wearable sensor signals.
Classifying multiple health and activity states accurately.
Supporting low-latency inference.
Preparing models for edge deployment.
The Solution
Created preprocessing pipelines for wearable sensor data.
Trained classification models for activity, sleep, and stress.
Optimized models for real-time inference.
Integrated outputs with the coaching assistant workflow.
// Core capabilities
Key Features
Activity classification
Sleep-stage detection
Stress classification
Sensor preprocessing
Real-time inference
Edge deployment
// System architecture
How It Works
Wearable sensor data is collected from the user.
Signals are cleaned, normalized, and transformed into features.
The trained model classifies activity, sleep, or stress state.
Inference results are passed to the coaching platform.
The system generates personalized insights and recommendations.
// Project impact
Results
Enabled real-time classification of wearable activity data.
Supported more personalized coaching recommendations.
Prepared models for efficient edge deployment.