Stress Annotations from Older Adults - Exploring the Foundations for Mobile ML-Based Health Assistance
Stress Annotations from Older Adults - Exploring the Foundations for Mobile ML-Based Health Assistance
复制标题
老年人的压力注释 - 探索基于移动 ML 的健康援助的基础
DOI：
10.1145/3329189.3329197
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
E. André
中科院分区：
文献类型：
--
作者：
Michael Dietz;Ilhan Aslan;Dominik Schiller;S. Flutura;A. Steinert;Robert Klebbe;E. André
内置传感器的新型移动和可穿戴技术的数量正在增加，这些传感器可以量化我们生活的方方面面。因此，机器学习（ML）模型及其应用的发展出现了新的数据源和机会。在本文中，我们报告了对16名年龄在66岁至81岁之间（50%为女性）的老年人进行的为期四周的实地研究，他们被要求以不同的方式提供与压力相关的体验样本，包括纸质日记和借助可穿戴设备（即微软Band 2）收集的数据。我们提供了参与者压力注释行为的见解，报告了对记录数据的详细分析以及对老年人压力情况注释的结果含义，讨论了移动注释技术如何从与传统方法的协同作用中受益，并论证了为什么我们认为适当的注释技术是从未来强大的机器学习模型中受益的基础。
The number of new mobile and wearable technologies with built-in sensors for quantifying every aspect of our lives is increasing. Consequently, new data sources and opportunities arise for the development of machine learning (ML) models and their applications. In this paper, we report on a four weeks field study with 16 older adults, aged between 66 and 81 years (50% female), who were asked to provide stress-related experience samples in different modalities, including paper-based diaries and data collected with the help of a wearable (i.e., a Microsoft Band 2). We provide insights into participants' stress annotation behavior, report on a detailed analysis of the recorded data and the resulting implications regarding the annotation of stressful situations by older adults, discuss how mobile annotation technology can benefit from the synergies with traditional methods and argue why we believe that appropriate annotation techniques are the basis to benefit individually from future powerful machine learning models.