Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions.
Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions.
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DOI：
10.1145/2750858.2807537
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Kumar S
中科院分区：
文献类型：
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作者：
Sharmin M;Raij A;Epstien D;Nahum-Shani I;Beck JG;Vhaduri S;Preston K;Kumar S
我们调查压力时间序列传感器数据可视化的需求、挑战和机遇，为及时适应性干预措施 (JITAI) 的设计提供信息。我们确定了七个关键挑战：海量且多样的数据、识别压力源的复杂性、空间的可扩展性、压力与时间之间的多方面关系、多粒度表示的需求、人际差异以及由于新颖性而对 JITAI 设计要求的理解有限。我们基于一百万分钟的传感器数据 (n=70) 提出了四种新的可视化效果。我们与压力研究人员 (n=6) 一起评估我们的可视化，以初步了解其在 JITAI 设计中的可用性和实用性。我们的结果表明，时空可视化有助于识别和解释人与人之间和人内压力模式的变异性，而情境可视化可以帮助做出有关干预时间、内容和方式的决策。有趣的是，粒度表示被认为提供了丰富的信息，但容易产生噪音。抽象表示是设计 JITAI 的首选起点。
We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.