IEEE J Biomed Health Inform. 2019 Nov;23(6):2464-2474. doi: 10.1109/JBHI.2019.2893945. Epub 2019 Jan 18.
Physical activity recognition using wearable sensors has achieved good performance in discriminating heterogeneous activities for health monitoring, but there has been less investigation of sedentary activities, e.g., desk work, which is often physically homogenous, to improve health in office environments. In this study, we explored head movement as a new sensing modality for physical and mental activity analysis. A new algorithm which segments gyroscope signals into atomic head movement events is proposed. Instead of recognizing activities in terms of predefined categories, we recognized four dimensions of task load: cognitive, perceptual, communicative, and physical, analogous to current manual workload assessment methods like NASA-TLX. We collected head movement data from 24 participants who wore a tri-axial inertial sensor at head while performing multiple tasks with varying load levels at office. An average of 70% accuracy was achieved for recognizing cognitive load levels, and more than 80% for the other three load types. The proposed event features outperformed a set of 181 features from previous physical activity recognition studies. We also demonstrated that these atomic event features are diagnostic of different load types in cross-load type classification, showing the promise of physical and mental load monitoring for health.
使用可穿戴传感器进行身体活动识别在区分健康监测中的异构活动方面表现出色，但对于经常在身体上同质的静止活动（例如办公工作）的研究较少，这些活动可以改善办公环境中的健康状况。在这项研究中，我们探索了头部运动作为身体和精神活动分析的新传感模式。提出了一种将陀螺仪信号分割成原子头部运动事件的新算法。我们不是根据预定义的类别来识别活动，而是根据类似于当前手动工作量评估方法（如 NASA-TLX）的认知、感知、交流和身体四个维度来识别任务负荷。我们从 24 名参与者那里收集了头部运动数据，这些参与者在办公室中执行不同负荷水平的多项任务时，头部佩戴三轴惯性传感器。对于认知负荷水平的识别，平均准确率达到了 70%，对于其他三种负荷类型，准确率超过了 80%。与之前的身体活动识别研究中的一组 181 个特征相比，所提出的事件特征表现更优。我们还证明，这些原子事件特征在跨负荷类型分类中对于不同的负荷类型具有诊断意义，为健康的身体和精神负荷监测提供了前景。
