Towards designing an intelligent experience sampling method for emotion detection

Experience Sampling Method (ESM) is widely used in idiographic approaches to collect within-person patterns. Planning a suitable survey schedule while designing an ESM based experiment is challenging as it must balance between survey fatigue of users, and the timeliness and accuracy of the responses provided by users. Even with the proliferation of ESM experiments, survey scheduling typically remains confined to use of fixed schedules, where periodic probes are sent to user, or event-based schedules, where depending on the number of events, large number of probes may interrupt user frequently. We propose a novel survey scheduling scheme, Low-Interference High-fidelity (LIHF) ESM schedule, which is designed to reduce interference while retaining fidelity of user response. We integrated LIHF into an ESM application, called TapSense, that is used to infer user's emotion from typing characteristics on smartphone keypad. Conducting a 2-week field study involving 9 users, using proposed metrics we show that using LIHF there is 26% reduction in survey fatigue, 50% improvement in triggering survey probes in timely manner, and 8% improvement in predicting emotion states based on typing patterns compared to typical ESM scheduling techniques.

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