Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool

Alireza Khanshan; Pieter Van Gorp; Panos Markopoulos

doi:10.1007/978-3-031-34586-9_21

Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool

Alireza Khanshan, Pieter Van Gorp, Panos Markopoulos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

2 Downloads (Pure)

Abstract

We introduce Experiencer, a newly developed Experience Sampling Method (ESM) software for commodity-level smartwatches. We designed this software mainly to address the compliance-related challenges, such as dropouts of study participants, that generations of ESM software solutions have faced. Dropouts are often caused by the inconvenient frequency and timing of the ESM prompts. This can partly be mitigated by utilizing physiological smartwatch sensors to learn which prompting moments are both convenient to the study participant and also relevant to the ESM study designer. Experiencer enables researchers to configure context-sensitive sampling protocols, providing access to raw sensor data, within the boundaries of European privacy legislation. In this paper, we describe the technical capabilities of our software, compare its features with the state-of-the-art, and showcase its application in studies that used Experiencer.

Original language	English
Title of host publication	Pervasive Computing Technologies for Healthcare
Subtitle of host publication	16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings
Editors	Athanasios Tsanas, Andreas Triantafyllidis
Place of Publication	Cham
Publisher	Springer
Chapter	21
Pages	315-331
Number of pages	17
ISBN (Electronic)	978-3-031-34586-9
ISBN (Print)	978-3-031-34585-2
DOIs	https://doi.org/10.1007/978-3-031-34586-9_21
Publication status	Published - 11 Jun 2023
Event	16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022 - Thessaloniki, Greece Duration: 12 Dec 2022 → 14 Dec 2022 https://pervasivehealth.eai-conferences.org/2022/

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volume	488
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022
Abbreviated title	PervasiveHealth 2022
Country/Territory	Greece
City	Thessaloniki
Period	12/12/22 → 14/12/22
Internet address	https://pervasivehealth.eai-conferences.org/2022/

Keywords

Experience Sampling Method (ESM)
Wearable ESM
mHealth
Ubiquitous Computing
Smartwatch application
Wearables
Software Framework
Experience Sampling Method

Access to Document

10.1007/978-3-031-34586-9_21

Cite this

Khanshan, A., Van Gorp, P., & Markopoulos, P. (2023). Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool. In A. Tsanas, & A. Triantafyllidis (Eds.), Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings (pp. 315-331). (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Vol. 488). Springer. https://doi.org/10.1007/978-3-031-34586-9_21

Khanshan, Alireza ; Van Gorp, Pieter ; Markopoulos, Panos. / Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool. Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings. editor / Athanasios Tsanas ; Andreas Triantafyllidis. Cham : Springer, 2023. pp. 315-331 (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering).

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abstract = "We introduce Experiencer, a newly developed Experience Sampling Method (ESM) software for commodity-level smartwatches. We designed this software mainly to address the compliance-related challenges, such as dropouts of study participants, that generations of ESM software solutions have faced. Dropouts are often caused by the inconvenient frequency and timing of the ESM prompts. This can partly be mitigated by utilizing physiological smartwatch sensors to learn which prompting moments are both convenient to the study participant and also relevant to the ESM study designer. Experiencer enables researchers to configure context-sensitive sampling protocols, providing access to raw sensor data, within the boundaries of European privacy legislation. In this paper, we describe the technical capabilities of our software, compare its features with the state-of-the-art, and showcase its application in studies that used Experiencer.",

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Khanshan, A , Van Gorp, P & Markopoulos, P 2023, Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool. in A Tsanas & A Triantafyllidis (eds), Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 488, Springer, Cham, pp. 315-331, 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022, Thessaloniki, Greece, 12/12/22. https://doi.org/10.1007/978-3-031-34586-9_21

Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool. / Khanshan, Alireza ; Van Gorp, Pieter ; Markopoulos, Panos.
Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings. ed. / Athanasios Tsanas; Andreas Triantafyllidis. Cham: Springer, 2023. p. 315-331 (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Vol. 488).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - We introduce Experiencer, a newly developed Experience Sampling Method (ESM) software for commodity-level smartwatches. We designed this software mainly to address the compliance-related challenges, such as dropouts of study participants, that generations of ESM software solutions have faced. Dropouts are often caused by the inconvenient frequency and timing of the ESM prompts. This can partly be mitigated by utilizing physiological smartwatch sensors to learn which prompting moments are both convenient to the study participant and also relevant to the ESM study designer. Experiencer enables researchers to configure context-sensitive sampling protocols, providing access to raw sensor data, within the boundaries of European privacy legislation. In this paper, we describe the technical capabilities of our software, compare its features with the state-of-the-art, and showcase its application in studies that used Experiencer.

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ER -

Khanshan A , Van Gorp P , Markopoulos P. Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool. In Tsanas A, Triantafyllidis A, editors, Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings. Cham: Springer. 2023. p. 315-331. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering). doi: 10.1007/978-3-031-34586-9_21

Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool

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