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CrowdSense

CrowdSense is a free continuous sensing tool, based on our open source library SensingKit

CrowdSense is a free continuous sensing tool, based on our open source library SensingKit

CrowdSense

by Kleomenis Katevas
CrowdSense
CrowdSense
CrowdSense

What is it about?

CrowdSense is a free continuous sensing tool, based on our open source library SensingKit. It provides an easy way for researchers to capture sensor data using iOS devices. Taking user privacy seriously, all data are fully anonymised and the user’s explicit permission is required prior to enabling the sensors. Recorded data can be either shared withing the app, or accessed using iTunes through File Sharing.

CrowdSense

App Details

Version
1.3.4
Rating
(1)
Size
3Mb
Genre
Utilities Education
Last updated
September 21, 2018
Release date
August 29, 2015
More info

App Store Description

CrowdSense is a free continuous sensing tool, based on our open source library SensingKit. It provides an easy way for researchers to capture sensor data using iOS devices. Taking user privacy seriously, all data are fully anonymised and the user’s explicit permission is required prior to enabling the sensors. Recorded data can be either shared withing the app, or accessed using iTunes through File Sharing.

CrowdSense supports the following sensors:
- Accelerometer
- Gyroscope
- Magnetometer
- Device Motion (Gravity, User acceleration, Magnetic Field, Rotation, Attitude)
- Motion Activity
- Pedometer
- Altimeter
- Location
- Heading
- iBeacon™ Proximity
- Eddystone™ Proximity
- Battery
- Microphone

For more details about SensingKit library, please refer to https://www.sensingkit.org, or read the following publications:

Kleomenis Katevas, Hamed Haddadi and Laurissa Tokarchuk, "SensingKit: Evaluating the Sensor Power Consumption in iOS devices", 12th International Conference on Intelligent Environments (IE'16), September 2016, London, UK.

Kleomenis Katevas, Hamed Haddadi, Laurissa Tokarchuk, "SensingKit – A Multi-Platform Mobile Sensing Framework for Large‑Scale Experiments", Extended abstract, ACM MobiCom 2014, Maui, Hawaii, September 2014.

This application is part of a Ph.D. research, conducted in Queen Mary University of London. For more information, please visit https://minoskt.github.io.

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