CrowdLearner: rapidly creating mobile recognizers using crowdsourcing.

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.