Research Article
Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool
@INPROCEEDINGS{10.1007/978-3-031-34586-9_21, author={Alireza Khanshan and Pieter Van Gorp and Panos Markopoulos}, title={Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool}, proceedings={Pervasive Computing Technologies for Healthcare. 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2023}, month={6}, keywords={Experience Sampling Method Wearable ESM mHealth Ubiquitous Computing Smartwatch application Wearables Software Framework}, doi={10.1007/978-3-031-34586-9_21} }- Alireza Khanshan
Pieter Van Gorp
Panos Markopoulos
Year: 2023
Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-031-34586-9_21
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.
