Brunyé Tad T, McIntyre James, Hughes Gregory I, Miller Eric L
U.S. Army DEVCOM Soldier Center, Natick, MA 01760, USA.
Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA 02155, USA.
Sensors (Basel). 2024 Nov 25;24(23):7530. doi: 10.3390/s24237530.
In occupational domains such as sports, healthcare, driving, and military, both individuals and small groups are expected to perform challenging tasks under adverse conditions that induce transient cognitive states such as stress, workload, and uncertainty. Wearable and standoff 6DOF sensing technologies are advancing rapidly, including increasingly miniaturized yet robust inertial measurement units (IMUs) and portable marker-less infrared optical motion tracking. These sensing technologies may offer opportunities to track overt physical behavior and classify cognitive states relevant to human performance in diverse human-machine domains. We describe progress in research attempting to distinguish cognitive states by tracking movement behavior in both individuals and small groups, examining potential applications in sports, healthcare, driving, and the military. In the context of military training and operations, there are no generally accepted methods for classifying transient mental states such as uncertainty from movement-related data, despite its importance for shaping decision-making and behavior. To fill this gap, an example data set is presented including optical motion capture of rifle trajectories during a dynamic marksmanship task that elicits variable uncertainty; using machine learning, we demonstrate that features of weapon trajectories capturing the complexity of motion are valuable for classifying low versus high uncertainty states. We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human-machine interfaces to support a wide range of cognitive functions.
在体育、医疗保健、驾驶和军事等职业领域，个人和小群体都需要在不利条件下执行具有挑战性的任务，这些条件会引发如压力、工作量和不确定性等短暂的认知状态。可穿戴和远距离6自由度传感技术正在迅速发展，包括日益小型化但坚固的惯性测量单元（IMU）和便携式无标记红外光学运动跟踪技术。这些传感技术可能提供机会来跟踪明显的身体行为，并对不同人机领域中与人类表现相关的认知状态进行分类。我们描述了在研究方面取得的进展，该研究试图通过跟踪个人和小群体的运动行为来区分认知状态，并研究其在体育、医疗保健、驾驶和军事领域的潜在应用。在军事训练和行动的背景下，尽管从与运动相关的数据中分类诸如不确定性等短暂心理状态对塑造决策和行为很重要，但目前尚无普遍接受的方法。为了填补这一空白，我们展示了一个示例数据集，其中包括在动态射击任务中步枪轨迹的光学运动捕捉，该任务会引发不同程度的不确定性；通过机器学习，我们证明捕捉运动复杂性的武器轨迹特征对于分类低不确定性状态和高不确定性状态很有价值。我们认为，利用人类运动行为指标可以揭示机会，以补充相对昂贵且不太便携的神经生理传感技术，并实现特定领域的人机接口，以支持广泛的认知功能。
