Background: Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. Methods: After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. Results: A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. Conclusion: Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.

中文翻译：

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使用可穿戴传感器评估疲劳：一项试点研究

背景：疲劳是一个广泛的、多因素的概念，包括身体和精神能量水平降低的感觉。疲劳会在各种情况下强烈影响患者与健康相关的生活质量，但迄今为止，可用于了解疲劳的工具受到严重限制。方法：在使用基于循环神经网络的算法从多传感器可穿戴设备中估算缺失的时间序列数据后，我们比较了有监督和无监督机器学习方法，以深入了解自我报告的非病理性疲劳与多模态传感器数据之间的关系。结果：共分析了 27 名健康受试者和 405 个记录日。记录的数据包括关于身体活动、生命体征和其他生理参数的连续多模式可穿戴传感器时间序列，以及关于疲劳的每日问卷。当使用因果卷积神经网络模型进行多变量传感器数据的无监督表示学习，并使用随机森林作为在受试者报告的物理疲劳标签上训练的分类器（加权精度为 0.70 ± 0.03 和召回率为 0.73 ± 0.03）时，获得了最佳结果. 在传感器数据上使用手动设计的特征来训练我们的随机森林（加权精度为 0.70 ± 0.05，召回率为 0.72 ± 0.01）时，包括身体活动（能量消耗、活动计数和步数）和生命体征（心率、心率变异性和呼吸频率）是衡量的重要参数。此外，与身体指标相比，生命体征作为预测精神疲劳的主要特征贡献最大。这些结果支持疲劳是一个高度多模态概念的观点。对来自传感器数据的聚类分析突出显示了一种数字表型，表明存在以高强度身体活动为特征的疲劳（95% 的观察结果）。精神疲劳遵循类似的趋势，但难以预测。假设更长的个体监测周期，潜在的未来方向可能集中在异常检测上。结论：综上所述，这些结果首次证明多模态数字数据可用于通知、量化和增强主观捕获的非病理性疲劳测量。精神疲劳遵循类似的趋势，但难以预测。假设更长的个体监测周期，潜在的未来方向可能集中在异常检测上。结论：综上所述，这些结果首次证明多模态数字数据可用于通知、量化和增强主观捕获的非病理性疲劳测量。精神疲劳遵循类似的趋势，但难以预测。假设更长的个体监测周期，潜在的未来方向可能集中在异常检测上。结论：综上所述，这些结果首次证明多模态数字数据可用于通知、量化和增强主观捕获的非病理性疲劳测量。