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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

7 Citaten (Scopus)

15 Downloads (Pure)

Samenvatting

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.

Originele taal-2	Engels
Titel	Pervasive Computing Technologies for Healthcare
Subtitel	16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings
Redacteuren	Athanasios Tsanas, Andreas Triantafyllidis
Plaats van productie	Cham
Uitgeverij	Springer
Hoofdstuk	21
Pagina's	315-331
Aantal pagina's	17
ISBN van elektronische versie	978-3-031-34586-9
ISBN van geprinte versie	978-3-031-34585-2
DOI's	https://doi.org/10.1007/978-3-031-34586-9_21
Status	Gepubliceerd - 11 jun. 2023
Evenement	16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022 - Thessaloniki, Griekenland Duur: 12 dec. 2022 → 14 dec. 2022 https://pervasivehealth.eai-conferences.org/2022/

Publicatie series

Naam	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volume	488
ISSN van geprinte versie	1867-8211
ISSN van elektronische versie	1867-822X

Congres

Congres	16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022
Verkorte titel	PervasiveHealth 2022
Land/Regio	Griekenland
Stad	Thessaloniki
Periode	12/12/22 → 14/12/22
Internet adres	https://pervasivehealth.eai-conferences.org/2022/

Toegang tot document

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

Andere bestanden en links

Link naar publicatie in Scopus

Citeer dit

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

@inproceedings{72bb7fb9b5e244bba0283734e0efe485,

title = "Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool",

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.",

keywords = "Experience Sampling Method (ESM), Wearable ESM, mHealth, Ubiquitous Computing, Smartwatch application, Wearables, Software Framework, Experience Sampling Method",

author = "Alireza Khanshan and \{Van Gorp\}, Pieter and Panos Markopoulos",

year = "2023",

month = jun,

day = "11",

doi = "10.1007/978-3-031-34586-9\_21",

language = "English",

isbn = "978-3-031-34585-2",

series = "Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering",

publisher = "Springer",

pages = "315--331",

editor = "Athanasios Tsanas and Andreas Triantafyllidis",

booktitle = "Pervasive Computing Technologies for Healthcare",

address = "Germany",

note = "16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022, PervasiveHealth 2022 ; Conference date: 12-12-2022 Through 14-12-2022",

url = "https://pervasivehealth.eai-conferences.org/2022/",

}

Khanshan, A, Van Gorp, P & Markopoulos, P 2023, Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool. in A Tsanas & A Triantafyllidis (uitgave), 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, blz. 315-331, 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022, Thessaloniki, Griekenland, 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. uitgave / Athanasios Tsanas; Andreas Triantafyllidis. Cham: Springer, 2023. blz. 315-331 (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Vol. 488).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool

AU - Khanshan, Alireza

AU - Van Gorp, Pieter

AU - Markopoulos, Panos

PY - 2023/6/11

Y1 - 2023/6/11

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.

AB - 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.

KW - Experience Sampling Method (ESM)

KW - Wearable ESM

KW - mHealth

KW - Ubiquitous Computing

KW - Smartwatch application

KW - Wearables

KW - Software Framework

KW - Experience Sampling Method

UR - https://www.scopus.com/pages/publications/85164101342

U2 - 10.1007/978-3-031-34586-9_21

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

M3 - Conference contribution

SN - 978-3-031-34585-2

T3 - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

SP - 315

EP - 331

BT - Pervasive Computing Technologies for Healthcare

A2 - Tsanas, Athanasios

A2 - Triantafyllidis, Andreas

PB - Springer

CY - Cham

T2 - 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022

Y2 - 12 December 2022 through 14 December 2022

ER -

Khanshan A, Van Gorp P , Markopoulos P. Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool. In Tsanas A, Triantafyllidis A, redacteurs, Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings. Cham: Springer. 2023. blz. 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

Samenvatting

Publicatie series

Congres

Toegang tot document

Andere bestanden en links

Vingerafdruk

Citeer dit