%0 Journal Article %1 8668435 %A Ghosh, Surjya %A Ganguly, Niloy %A Mitra, Bivas %A De, Pradipta %D 2021 %J IEEE Transactions on Affective Computing %K leibnizailab myown %N 4 %P 913-927 %R 10.1109/TAFFC.2019.2905561 %T Designing an Experience Sampling Method for Smartphone Based Emotion Detection %V 12 %X 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 - <italic>probing frequency</italic>, <italic>timely self-report collection</italic>, and <italic>notifying at opportune moment</italic> to ensure high <italic>response quality</italic>. 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 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.

@article{8668435, abstract = {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 - <italic>probing frequency</italic>, <italic>timely self-report collection</italic>, and <italic>notifying at opportune moment</italic> to ensure high <italic>response quality</italic>. 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 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.}, added-at = {2022-02-24T21:47:51.000+0100}, author = {Ghosh, Surjya and Ganguly, Niloy and Mitra, Bivas and De, Pradipta}, biburl = {https://www.bibsonomy.org/bibtex/23aba4eac1f96a2b260ec179c2f479669/niloy}, doi = {10.1109/TAFFC.2019.2905561}, interhash = {4959a46af8e5f36582e9edf7ff972838}, intrahash = {3aba4eac1f96a2b260ec179c2f479669}, issn = {1949-3045}, journal = {IEEE Transactions on Affective Computing}, keywords = {leibnizailab myown}, month = oct, number = 4, pages = {913-927}, timestamp = {2022-02-25T09:00:03.000+0100}, title = {Designing an Experience Sampling Method for Smartphone Based Emotion Detection}, volume = 12, year = 2021 }