Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach.
Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach.
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DOI：
10.3389/fphys.2021.738973
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发表时间：
2021
影响因子：
4
通讯作者：
Miller EL
中科院分区：
文献类型：
--
作者：
Brunyé TT;Yau K;Okano K;Elliott G;Olenich S;Giles GE;Navarro E;Elkin-Frankston S;Young AL;Miller EL
用于测量数字和化学生理的可穿戴技术正在渗透到消费市场，并有可能可靠地对与人类表现相关的状态进行分类，包括压力、睡眠不足和体力消耗。基于可穿戴设备高效、准确地对生理状态进行分类的能力正在提高。然而，人的行为在个人内部和个人之间的固有变异性使得预测被识别的状态如何影响与军事行动和其他高风险领域相关的人的表现结果具有挑战性。我们描述了一种计算建模方法来应对这一挑战，试图将从包括可穿戴设备在内的各种来源获得的用户状态转换为跨认知和物理领域的相关和可操作的见解。三个状态预测因子被考虑：压力水平、睡眠状态和体力消耗程度；这些自变量被用来预测三个人类表现结果：反应时间、执行功能和知觉-运动控制。在给定状态预测器的情况下，该方法提供了性能变量的完整的条件概率模型。该模型的构建利用不同的原始数据源，使用参数建模和最大似然估计来估计感兴趣的六个独立和相依变量中每一个的边际概率密度函数。变量之间的联合分布使用自适应套索方法，基于现有研究的荟萃分析得出的条件关系(效应大小)的强度和方向性。模型优化过程收敛于保持原始边缘分布的完整性以及条件关系的方向性和稳健性的解。所描述的建模框架为人类表现预测提供了灵活和可扩展的解决方案，提供了利用感兴趣的额外的独立和依赖变量、摄取新的原始数据以及扩展到自变量之间的双向和三向交互的有效扩展。继续的工作包括将模型扩展到多个自变量和因变量，通过可穿戴设备进行实时模型模拟，个性化和小群体预测，以及实验室和现场验证。
Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.
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影响因子：
34.7
作者：
Arnsten, Amy F. T.
通讯作者：
Arnsten, Amy F. T.
影响因子：
4.9
作者：
Bartel, P;Offermeier, W;Becker, P
通讯作者：
Becker, P
影响因子：
3.7
作者：
Bock, Otmar;Haeger, Mathias;Voelcker-Rehage, Claudia
通讯作者：
Voelcker-Rehage, Claudia
影响因子：
2.9
作者：
Acheson, Ashley;Richards, Jerry B.;de Wit, Harriet
通讯作者：
de Wit, Harriet
影响因子：
4.2
作者：
Boyd, Danah;Crawford, Kate
通讯作者：
Crawford, Kate