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

Authors :
Khanshan, Alireza
Van Gorp, Pieter
Markopoulos, Panos
Tsanas, Athanasios
Triantafyllidis, Andreas
Future Everyday
Information Systems IE&IS
School of Med. Physics and Eng. Eindhoven
EAISI Health
EAISI High Tech Systems
Source :
Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings, 315-331, STARTPAGE=315;ENDPAGE=331;TITLE=Pervasive Computing Technologies for Healthcare
Publication Year :
2023
Publisher :
Springer, 2023.

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.

Details

Language :
English
Database :
OpenAIRE
Journal :
Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12–14, 2022, Proceedings, 315-331, STARTPAGE=315;ENDPAGE=331;TITLE=Pervasive Computing Technologies for Healthcare
Accession number :
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