A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Georgia tech gesture toolkit: supporting experiments in gesture recognition
Proceedings of the 5th international conference on Multimodal interfaces
Telling humans and computers apart automatically
Communications of the ACM - Information cities
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
WICON '06 Proceedings of the 2nd annual international workshop on Wireless internet
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones
Proceedings of the 5th international conference on Mobile systems, applications and services
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Proceedings of the 20th annual ACM symposium on User interface software and technology
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Harnessing the wisdom of crowds in wikipedia: quality through coordination
Proceedings of the 2008 ACM conference on Computer supported cooperative work
The history of version control
ACM SIGSOFT Software Engineering Notes
Crowdsourcing graphical perception: using mechanical turk to assess visualization design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MAGIC: a motion gesture design tool
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Are your participants gaming the system?: screening mechanical turk workers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PRISM: platform for remote sensing using smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Gestalt: integrated support for implementation and analysis in machine learning
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
LiveLab: measuring wireless networks and smartphone users in the field
ACM SIGMETRICS Performance Evaluation Review
DoubleFlip: a motion gesture delimiter for mobile interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploiting Social Networks for Large-Scale Human Behavior Modeling
IEEE Pervasive Computing
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Bootstrapping personal gesture shortcuts with the wisdom of the crowd and handwriting recognition
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Accurate measurements of pointing performance from in situ observations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tap, swipe, or move: attentional demands for distracted smartphone input
Proceedings of the International Working Conference on Advanced Visual Interfaces
Medusa: a programming framework for crowd-sensing applications
Proceedings of the 10th international conference on Mobile systems, applications, and services
My App is an Experiment: Experience from User Studies in Mobile App Stores
International Journal of Mobile Human Computer Interaction
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Mobile applications can offer improved user experience through the use of novel modalities and user context. However, these new input dimensions often require recognition-based techniques, with which mobile app developers or designers may not be familiar. Furthermore, the recruiting, data collection and labeling, necessary for using these techniques, are usually time-consuming and expensive. We present CrowdLearner, a framework based on crowdsourcing to automatically generate recognizers using mobile sensor input such as accelerometer or touchscreen readings. CrowdLearner allows a developer to easily create a recognition task, distribute it to the crowd, and monitor its progress as more data becomes available. We deployed CrowdLearner to a crowd of 72 mobile users over a period of 2.5 weeks. We evaluated the system by experimenting with 6 recognition tasks concerning motion gestures, touchscreen gestures, and activity recognition. The experimental results indicated that CrowdLearner enables a developer to quickly acquire a usable recognizer for their specific application by spending a moderate amount of money, often less than $10, in a short period of time, often in the order of 2 hours. Our exploration also revealed challenges and provided insights into the design of future crowdsourcing systems for machine learning tasks.