CrowdSense

CrowdSense is a free continuous sensing app that enables researchers to collect rich mobile sensor data using iOS devices. Built on the open-source SensingKit framework, the app makes it easy to run mobile sensing studies and capture high-quality sensor streams for research and experimentation. CrowdSense prioritizes transparency and user control by requiring explicit permission before any sensor is activated, ensuring that all data collection is clear, secure, and fully under the user’s consent.

The app supports a wide range of device sensors, allowing researchers to capture diverse signals for behavioral, environmental, and mobility studies. Supported sensors include accelerometer, gyroscope, magnetometer, device motion (gravity, user acceleration, magnetic field, rotation, and attitude), motion activity, pedometer, altimeter, location, heading, iBeacon proximity, Eddystone proximity, battery status, microphone, screen brightness, and network connection.

Data recorded with CrowdSense can be easily accessed and exported for analysis. Researchers can share collected data directly from within the app or export it securely via USB using iTunes File Sharing, providing flexible workflows for integrating sensor datasets into research pipelines.

CrowdSense was developed as part of a PhD research project at Queen Mary University of London and is designed to support large-scale mobile sensing experiments using the SensingKit framework. More information about SensingKit is available at https://www.sensingkit.org, and additional details about the research behind the project can be found at https://minoskt.github.io.

Relevant publications include: “Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing” (SenSys-ML 2019), “Detecting Group Formations using iBeacon Technology” (HASCA 2016), “SensingKit: Evaluating the Sensor Power Consumption in iOS Devices” (IE 2016), “Walking in Sync: Two is Company, Three’s a Crowd” (WPA 2015), and “Poster: SensingKit — A Multi-Platform Mobile Sensing Framework for Large-Scale Experiments” (MobiCom 2014).