OSRP

Open Sensing Research Platform

Complete multi-modal mobile sensing for academic research.
Built for AWS. Open source.

Get Started View on GitHub

OSRP combines screenshots, sensors, and wearables into one comprehensive platform for digital phenotyping and behavioral research.

5-Minute Quick Start

# Install OSRP
pip install osrp

# Initialize study
osrp init my-study

# Deploy to AWS
osrp deploy --aws

# Start analyzing
osrp notebooks

Key Features

📸

Screenshot Capture

Behavioral observation - see what apps participants use and what content they view

📱

Built-in Sensors

Accelerometer, GPS, gyroscope, activity recognition, device state monitoring

⌚

Wearable Integration

Google Fit, Bluetooth heart rate monitors, fitness trackers

📋

Experience Sampling

Context-aware surveys and EMAs delivered at the right moment

☁️

AWS-Native

DynamoDB, S3, Lambda, SageMaker - enterprise-grade infrastructure

📊

Marimo Analysis

Reactive, reproducible notebooks better than Jupyter

Why OSRP?

For Universities

Existing Infrastructure Runs on AWS infrastructure you already have
HIPAA-Compatible Uses HIPAA-eligible AWS services for health research
Open Source Full code ownership and customization (Apache 2.0)

For Researchers

Multi-Modal Data All data streams temporally aligned
Configurable Enable/disable modules per study
Rich Context Behavioral + physiological + environmental
Integrated Analysis Marimo notebooks included
Publication Ready ML pipelines and reproducible workflows

Platform Comparison

OSRP is the only platform with all modalities + AWS + integrated analysis

	AWARE	Screenomics	Centralive	LAMP	Beiwe	OSRP
Screenshots	❌	✅	❌	❌	❌	✅
Sensors	✅	❌	⚠️	✅	✅	✅
Wearables	❌	❌	✅	⚠️	❌	✅
EMA System	⚠️	❌	✅	✅	✅	✅
AWS Native	❌	❌	❌	❌	❌	✅
Analysis Tools	❌	⚠️	⚠️	⚠️	❌	✅✅
Marimo Notebooks	❌	❌	❌	❌	❌	✅
Open Source	✅	✅	❌	✅	✅	✅

Use Cases

🧠 Digital Phenotyping

Depression and anxiety monitoring
Bipolar disorder tracking
Sleep and circadian rhythms
Stress and burnout assessment

📱 Behavioral Research

Social media impact studies
Screen time and wellbeing
App usage patterns
Digital intervention effectiveness

🏃 Multi-Modal Studies

Physical activity and mental health
Sleep quality and performance
Heart rate variability and stress
Location and social behavior

📈 Machine Learning

Stress prediction models
Activity classification
Intervention timing optimization
Relapse prediction

Simple, Powerful API

from osrp import OSRPData

# Initialize
data = OSRPData(region='us-west-2')

# Get complete daily summary
daily = data.get_daily_summary('participant001', date)

# Access individual streams
screenshots = daily['screenshots']
heart_rate = daily['heart_rate']
activity = daily['activity']

# Align multi-modal data
aligned = data.align_multi_modal({
    'screenshots': screenshots,
    'hr': heart_rate,
    'activity': activity
}, freq='5min')

Ready to Get Started?

Join leading research institutions using OSRP for digital phenotyping studies

Get Started Star on GitHub