Fanton Michael, Harari Yaar, Giffhorn Matthew, Lynott Allie, Alshan Eli, Mendley Jonathan, Czerwiec Madeline, Macaluso Rebecca, Ideses Ianir, Oks Eduard, Jayaraman Arun
Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, USA.
Department of Physical Medicine and Rehabilitation, Northwestern University, Evanston, IL, USA.
NPJ Digit Med. 2022 Sep 6;5(1):134. doi: 10.1038/s41746-022-00684-9.
Movement health is understanding our body's ability to perform movements during activities of daily living such as lifting, reaching, and bending. The benefits of improved movement health have long been recognized and are wide-ranging from improving athletic performance to helping ease of performing simple tasks, but only recently has this concept been put into practice by clinicians and quantitatively studied by researchers. With digital health and movement monitoring becoming more ubiquitous in society, smartphone applications represent a promising avenue for quantifying, monitoring, and improving the movement health of an individual. In this paper, we validate Halo Movement, a movement health assessment which utilizes the front-facing camera of a smartphone and applies computer vision and machine learning algorithms to quantify movement health and its sub-criteria of mobility, stability, and posture through a sequence of five exercises/activities. On a diverse cohort of 150 participants of various ages, body types, and ability levels, we find moderate to strong statistically significant correlations between the Halo Movement assessment overall score, metrics from sensor-based 3D motion capture, and scores from a sequence of 13 standardized functional movement tests. Further, the smartphone assessment is able to differentiate regular healthy individuals from professional movement athletes (e.g., dancers, cheerleaders) and from movement impaired participants, with higher resolution than that of existing functional movement screening tools and thus may be more appropriate than the existing tests for quantifying functional movement in able-bodied individuals. These results support using Halo Movement's overall score as a valid assessment of movement health.
运动健康是指了解我们身体在日常生活活动（如举重、伸手和弯腰）中执行动作的能力。改善运动健康的益处早已得到认可，范围广泛，从提高运动表现到帮助轻松完成简单任务，但直到最近，这一概念才被临床医生付诸实践，并由研究人员进行定量研究。随着数字健康和运动监测在社会中变得越来越普遍，智能手机应用程序成为量化、监测和改善个人运动健康的一个有前途的途径。在本文中，我们验证了光环运动（Halo Movement），这是一种运动健康评估方法，它利用智能手机的前置摄像头，并应用计算机视觉和机器学习算法，通过一系列五个练习/活动来量化运动健康及其移动性、稳定性和姿势等子标准。在150名年龄、体型和能力水平各异的参与者组成的多样化队列中，我们发现光环运动评估总分、基于传感器的3D运动捕捉指标以及一系列13项标准化功能运动测试的分数之间存在中度到高度的统计学显著相关性。此外，智能手机评估能够区分普通健康个体与专业运动运动员（如舞者、啦啦队员）以及运动功能受损的参与者，其分辨率高于现有的功能运动筛查工具，因此可能比现有测试更适合量化健全个体的功能运动。这些结果支持将光环运动的总分作为运动健康的有效评估。
