< p > <大胆>背景:< /大胆>手臂使用指标来源于腕带式运动传感器被广泛用于量化上肢表现在现实条件下的个体与中风在汽车复苏。实际使用的计算指标,如手臂使用时间和一侧的偏好,依赖于准确的识别功能动作。因此,分类上肢活动功能<斜体> < /斜体>和<斜体>非功能性< /斜体>类是至关重要的。加速度阈值通常用来区分这些类。然而,这些方法都受到高国米和个体内变异的运动模式。在这项研究中,我们开发和验证一个机器学习分类器的任务,而使用传统和最佳阈值的方法。< / p > < p > <大胆>方法:< /大胆>个人中风后在家里视频环境执行semi-naturalistic日常任务而戴腕带式惯性测量单元。分类后的数据标签帧功能上肢运动的定义,不包括全身运动,和测序为1 s时代。数量计算长短,和最优阈值功能运动是由接受者操作特征曲线分析组和个人水平。逻辑回归分类器训练在同一标签使用时间和频域特性。 Performance measures were compared between all classification methods.Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75–82%) to conventional thresholds (58–66%) across unilateral and bilateral activities.
Conclusion: This work compares the validity of methods classifying stroke survivors’ real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.