McLeod Adam, Bochniewicz Elaine M, Lum Peter S, Holley Rahsaan J, Emmer Geoff, Dromerick Alexander W
MITRE Corporation, McLean, VA.
MITRE Corporation, McLean, VA; Department of Biomedical Engineering, Catholic University of America, Washington, DC.
Arch Phys Med Rehabil. 2016 Feb;97(2):224-31. doi: 10.1016/j.apmr.2015.08.435. Epub 2015 Oct 3.
To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking.
Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users.
Rehabilitation hospital training apartment.
Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18).
Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials.
Percent correct classification.
With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly.
We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies.
通过评估使用身体佩戴传感器数据和机器学习模型来区分上肢在社区中的有效抓握和双手活动与行走相关的无关运动的可行性，改善上肢（UE）使用情况的测量。
将机器学习分类模型与上肢假肢使用者手动评分视频表现的标准进行比较。
康复医院训练公寓。
年龄和手优势相似的上肢假肢使用者（n = 5）和对照组（n = 13）的便利样本（N = 18）。
拍摄参与者执行一系列功能活动的视频；一名训练有素的观察者对每一帧进行注释，以表明上肢是针对功能活动的运动还是行走。来自附着在优势手腕上的惯性传感器的同步数据也被类似地分类为表明功能使用或行走。这些数据被用于训练3种分类模型，以根据相关传感器信息预测功能状态与行走状态。模型在4次试验中进行训练：针对上肢截肢者和对照组，以及在个体内和个体间进行。在个体间试验中，还对有无预处理（中心化）的模型性能进行了检查。
正确分类百分比。
除截肢者/个体间试验外，对于所有试验类型，至少有1个模型正确分类了>95%的测试数据。截肢者/个体间试验中表现最佳的模型正确分类了85%的测试示例。
我们已经证明，计算轻量级的分类模型可以使用从腕部佩戴传感器收集的惯性数据，可靠地区分功能使用期间假肢上肢运动与行走相关运动。这种方法有望客观地测量假肢上肢在现实世界中的使用情况，并可能有助于临床试验以及测量对其他上肢疾病治疗的反应。
