Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health.
Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health.
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DOI：
10.1109/access.2020.3042451
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Najafi B
中科院分区：
文献类型：
--
作者：
Zahiri M;Wang C;Gardea M;Nguyen H;Shahbazi M;Sharafkhaneh A;Ruiz IT;Nguyen CK;Bryant MS;Najafi B
远程筛查身体虚弱 (PF) 可能有助于对临床优先考虑前往临床中心进行预防性护理的慢性阻塞性肺疾病 (COPD) 患者进行分类。然而，传统的 PF 评估工具对于远程患者监测应用的可行性有限。为了提高 PF 评估的安全性，我们之前开发并验证了一种快速、安全的 PF 筛查工具，称为 Frailty Meter (FM)。 FM 的工作原理是使用腕戴式传感器量化 20 秒重复性肘部屈曲/伸展任务期间的虚弱、缓慢、僵硬和疲惫，并生成范围从 0 到 1 的虚弱指数 (FI)；数值越高表明虚弱程度逐渐加重。然而，腕式传感器的使用限制了其在远程医疗和远程患者监护中的应用。在这项研究中，我们开发了一种基于深度学习图像处理的无传感器 FM，它可以轻松集成到移动健康中，并能够远程评估身体虚弱程度。无传感器 FM 从平板电脑摄像头记录的 20 秒肘部屈曲和伸展视频中提取前臂运动的运动学特征，然后计算衰弱表型和 FI。为了测试无传感器 FM 的有效性，招募了 11 名入住远程医疗肺康复诊所的 COPD 患者和 10 名健康的年轻志愿者（对照）。所有参与者都完成了测试，表明可行性很高。在基于传感器的 FM 和无传感器 FM 之间观察到强相关性 (0.72 < r < 0.99)，以提取所有虚弱表型和 FI。在根据年龄和体重指数 (BMI) 进行调整后，无传感器 FM 能够将 COPD 组与对照组区分开来 (p<0.050)，其中观察到的虚弱效应值最大 (Cohen 效应值 d=2.24)、虚弱指数 (d=1.70) 和缓慢 (d=1.70)。这些试点结果表明了这种无传感器 FM 对 COPD 患者 PF 远程评估的可行性和概念有效性。
Remote screening physical frailty (PF) may assist in triaging patients with chronic obstructive pulmonary disease (COPD) who are in clinical priorities to visit a clinical center for preventive care. Conventional PF assessment tools have however limited feasibility for remote patient monitoring applications. To improve the safety of PF assessment, we previously developed and validated a quick and safe PF screening tool called Frailty Meter (FM). FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion/extension task using a wrist-worn sensor and generates a frailty index (FI) ranging from zero to one; higher values indicate progressively greater severity of frailty. However, the use of wrist-sensor limits its applications in telemedicine and remote patient monitoring. In this study, we developed a sensor-less FM based on deep learning-based image processing, which can be easily integrated into mobile health and enables remote assessment of physical frailty. The sensor-less FM extracts kinematic features of the forearm motion from the video of 20-second elbow flexion and extension recorded by a tablet camera, and then calculates frailty phenotypes and FI. To test the validity of sensor-less FM, 11 COPD patients admitted to a Telehealth pulmonary rehabilitation clinic and 10 healthy young volunteers (controls) were recruited. All participants completed the test indicating high feasibility. Strong correlations (0.72 < r < 0.99) were observed between the sensor-based FM and sensor-less FM to extract all frailty phenotypes and FI. After adjusting with age and body mass index(BMI), sensor-less FM enables distinguishing COPD group from controls (p<0.050) with the largest effect sizes observed for weakness (Cohen’s effect size d=2.24), frailty index (d=1.70), and slowness (d=1.70). These pilot findings suggest feasibility and proof of concept validity of this sensor-less FM toward remote assessment of PF in COPD patients.
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