Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

中文翻译：

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机器学习和基于传感器的站立测试的使用

由于用于评估脆弱表型的常规筛查工具需要大量资源并且不适合常规应用，因此正在努力通过使用基于传感器的技术来简化和缩短脆弱筛查方案。这项研究探索了将机器学习与脆弱模型相结合是否可以确定识别身体脆弱和三种关键脆弱表型（缓慢，虚弱和疲惫）所需的最少的传感器派生功能。年龄较大的参与者（n = 102，年龄= 76.54±7.72岁）配备了五个可穿戴传感器，并完成了五次从坐到站的测试。结合机器学习技术和脆弱建模，提取了17个传感器派生的特征，并将其用于最佳特征选择。髋关节角速度范围的平均值（缓慢度指标），选择垂直功率范围的平均值（弱点指标）和垂直功率范围的变化系数（疲惫指标）作为最佳特征。具有三个最佳特征的脆弱模型的曲线下面积为85.20％，灵敏度为82.70％，特异性为71.09％。这项研究表明，这三个传感器衍生的特征可以用作身体虚弱和缓慢，虚弱和疲惫表型的数字生物标记。我们的发现可以促进未来基于低成本传感器的技术的设计，以通过远程医疗进行远程身体脆弱性评估。灵敏度为82.70％，特异性为71.09％。这项研究表明，这三个传感器衍生的特征可以用作身体虚弱和缓慢，虚弱和疲惫表型的数字生物标记。我们的发现可以促进未来基于低成本传感器的技术的设计，以通过远程医疗进行远程身体脆弱性评估。灵敏度为82.70％，特异性为71.09％。这项研究表明，这三个传感器衍生的特征可以用作身体虚弱和缓慢，虚弱和疲惫表型的数字生物标记。我们的发现可以促进未来基于低成本传感器的技术的设计，以通过远程医疗进行远程身体脆弱性评估。