@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
}