Lonini Luca, Gupta Aakash, Deems-Dluhy Susan, Hoppe-Ludwig Shenan, Kording Konrad, Jayaraman Arun
Shirley Ryan Ability Lab, Max Näder Lab, Chicago, IL, United States.
Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States.
JMIR Rehabil Assist Technol. 2017 Aug 10;4(2):e8. doi: 10.2196/rehab.7317.
Wearable sensors gather data that machine-learning models can convert into an identification of physical activities, a clinically relevant outcome measure. However, when individuals with disabilities upgrade to a new walking assistive device, their gait patterns can change, which could affect the accuracy of activity recognition.
The objective of this study was to assess whether we need to train an activity recognition model with labeled data from activities performed with the new assistive device, rather than data from the original device or from healthy individuals.
Data were collected from 11 healthy controls as well as from 11 age-matched individuals with disabilities who used a standard stance control knee-ankle-foot orthosis (KAFO), and then a computer-controlled adaptive KAFO (Ottobock C-Brace). All subjects performed a structured set of functional activities while wearing an accelerometer on their waist, and random forest classifiers were used as activity classification models. We examined both global models, which are trained on other subjects (healthy or disabled individuals), and personal models, which are trained and tested on the same subject.
Median accuracies of global and personal models trained with data from the new KAFO were significantly higher (61% and 76%, respectively) than those of models that use data from the original KAFO (55% and 66%, respectively) (Wilcoxon signed-rank test, P=.006 and P=.01). These models also massively outperformed a global model trained on healthy subjects, which only achieved a median accuracy of 53%. Device-specific models conferred a major advantage for activity recognition.
Our results suggest that when patients use a new assistive device, labeled data from activities performed with the specific device are needed for maximal precision activity recognition. Personal device-specific models yield the highest accuracy in such scenarios, whereas models trained on healthy individuals perform poorly and should not be used in patient populations.
可穿戴传感器收集的数据能被机器学习模型转化为身体活动识别，这是一种具有临床相关性的结果指标。然而，当残疾个体升级使用新的步行辅助设备时，他们的步态模式可能会改变，这可能会影响活动识别的准确性。
本研究的目的是评估我们是否需要使用来自新辅助设备执行活动的标记数据来训练活动识别模型，而不是使用来自原始设备或健康个体的数据。
从11名健康对照者以及11名年龄匹配的残疾个体收集数据，这些残疾个体使用标准的站立控制膝踝足矫形器（KAFO），然后使用计算机控制的自适应KAFO（奥托博克C型支具）。所有受试者在腰部佩戴加速度计的同时进行一组结构化的功能活动，并使用随机森林分类器作为活动分类模型。我们研究了在其他受试者（健康或残疾个体）上训练的全局模型以及在同一受试者上训练和测试的个人模型。
使用新KAFO数据训练的全局模型和个人模型的中位准确率（分别为61%和76%）显著高于使用原始KAFO数据的模型（分别为55%和66%）（Wilcoxon符号秩检验，P = 0.006和P = 0.01）。这些模型也大大优于在健康受试者上训练的全局模型，后者的中位准确率仅为53%。特定设备模型在活动识别方面具有主要优势。
我们的结果表明，当患者使用新的辅助设备时，需要来自特定设备执行活动的标记数据以实现最高精度的活动识别。在这种情况下，个人特定设备模型产生的准确率最高，而在健康个体上训练的模型表现不佳，不应在患者群体中使用。
