Framework for selecting and benchmarking mobile devices in psychophysiological research.
Framework for selecting and benchmarking mobile devices in psychophysiological research.
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DOI：
10.3758/s13428-020-01438-9
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发表时间：
2021-04
影响因子：
5.4
通讯作者：
Quigley KS
中科院分区：
文献类型：
--
作者：
Kleckner IR;Feldman MJ;Goodwin MS;Quigley KS
商业上可用的消费电子产品（智能手表和可穿戴生物传感器）越来越多地使得能够在实验室环境内外获取外围生理和身体活动数据。然而，很少有文献可用于选择和评估这些新型器械的科学用途的适用性。为了克服这一局限性，本文提供了一个框架，以帮助研究人员选择和评估可穿戴技术用于实证研究。我们的七步框架包括：（1）识别感兴趣的信号;（2）描述预期用例;（3）识别研究特定的语用需求;（4）选择评估设备;（5）建立评估程序;（6）对结果数据进行定性和定量分析;以及如果需要，（7）进行功效分析以确定更严格地比较器件性能所需的样本大小。我们通过比较来自各种商业无线传感器（Affectiva Q，Empatica E3，Empatica E4，Actiwave Cardio，Shimmer）的皮肤电，心血管和加速度计数据来说明框架的应用程序相对于经过充分验证的有线Mindware实验室系统。我们的评估是在两项研究（N=10，N=11）涉及心理测量的声音，标准化的任务，包括体力活动和影响感应。在将我们的框架应用于这些数据之后，我们得出结论，只有一些用于生理测量的商用消费设备能够无线测量具有足够质量的科学用例的外围生理和身体活动数据。因此，该框架似乎有利于建议在研究中部署之前对移动的生理设备进行更系统、透明和严格的评价的步骤。
Commercially available consumer electronics (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired Mindware laboratory system. Our evaluations are performed in two studies (N=10, N=11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to these data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.
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