Designing an experience sampling method for smartphone based emotion detection

Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider - probing frequency, timely self-report collection, and notifying at opportune moment to ensure high response quality. We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64%, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9% while detecting inopportune moments with an average accuracy of 89%. These design choices improve the response quality, as manifested by 96% valid response collection and a maximum improvement of 24% in emotion classification accuracy.

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