U.S. Army Research Institute of Environmental Medicine, USA.
U.S. Army Research Institute of Environmental Medicine, USA.
J Sci Med Sport. 2023 Jun;26 Suppl 1:S3-S8. doi: 10.1016/j.jsams.2023.01.009. Epub 2023 Feb 3.
Measures of human motion provide a rich source of health and physiological status information. This paper provides examples of motion-based biomarkers in the form of patterns of movement, quantified physical activity, and characteristic gaits that can now be assessed with practical measurement technologies and rapidly evolving physiological models and algorithms, with research advances fed by the increasing access to motion data and associated contextual information. Quantification of physical activity has progressed from step counts to good estimates of energy expenditure, useful to weight management and to activity-based health outcomes. Activity types and intensity durations are important to health outcomes and can be accurately classified even from carried smart phone data. Specific gaits may predict injury risk, including some re-trainable injurious running or modifiable load carriage gaits. Mood status is reflected in specific types of human movement, with slumped posture and shuffling gait signaling depression. Increased variability in body sway combined with contextual information may signify heat strain, physical fatigue associated with heavy load carriage, or specific neuropsychological conditions. Movement disorders might be identified earlier and chronic diseases such as Parkinson's can be better medically managed with automatically quantified information from wearable systems. Increased path tortuosity suggests head injury and dementia. Rapidly emerging wear-and-forget systems involving global positioning system and inertial navigation, triaxial accelerometry, smart shoes, and functional fiber-based clothing are making it easier to make important health and performance outcome associations, and further refine predictive models and algorithms that will improve quality of life, protect health, and enhance performance.
人体运动测量提供了丰富的健康和生理状态信息来源。本文以运动模式、量化的身体活动和特征步态为例,介绍了基于运动的生物标志物,这些标志物现在可以通过实用的测量技术和快速发展的生理模型和算法进行评估,研究进展得益于越来越多的运动数据和相关背景信息的获取。身体活动的量化已经从步数发展到了对能量消耗的准确估计,这对体重管理和基于活动的健康结果很有用。活动类型和强度持续时间对健康结果很重要,即使从携带的智能手机数据中也可以准确分类。特定的步态可能预示着受伤风险,包括一些可重新训练的受伤跑步或可修改的负重步态。情绪状态反映在特定类型的人体运动中, slumped 姿势和拖着脚走的步态表明抑郁。身体摆动的变异性增加,结合背景信息,可能表明热应激、与负重有关的身体疲劳或特定的神经心理状况。运动障碍可能更早被识别,佩戴式系统自动量化的信息可更好地管理帕金森病等慢性疾病。路径扭曲度增加表明头部受伤和痴呆。涉及全球定位系统和惯性导航、三轴加速度计、智能鞋和功能性纤维服装的快速新兴免维护系统,使得更容易进行重要的健康和性能结果关联,并进一步改进预测模型和算法,提高生活质量,保护健康,提高性能。