IEEE Trans Neural Syst Rehabil Eng. 2022;30:2589-2599. doi: 10.1109/TNSRE.2022.3204781. Epub 2022 Sep 15.
Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.
运动功能评估对脑卒中康复至关重要。传统的评估方法具有主观性，严重依赖治疗师的经验。鉴于脑卒中严重程度水平与日常生活活动（ADL）表现之间存在很强的相关性，我们探索了通过三种典型的 ADL（刷牙、洗脸和喝水）自动评估上肢 Brunnstrom 恢复阶段（BRS）的可能性。多模态数据（加速度、角速度、表面肌电图）同步采集自 5 个上肢佩戴传感器模块。BRS 评估系统的性能已知会随不同的系统参数（例如，传感器模块数量、特征类型和分类器）而变化。我们系统地从不同的数据分段策略（5 个窗口长度和 4 个重叠）、42 种特征、12 种特征优化技术和 9 种分类器中搜索最佳参数，采用留一受试者交叉验证。为了实现可靠和低成本的监测，我们进一步探索了是否可以使用相对较少的传感器模块获得令人满意的结果。结果，该方法仅使用 3 个经过优化的传感器模块（窗口长度：7s，重叠：50%）、提取的特征（简单平方积分、斜率符号变化、修正绝对值 1 和修正绝对值 2）、特征优化方法（主成分分析）和逻辑回归分类器，即可正确识别所有 27 名参与者的阶段。根据文献，这是第一项全面优化 BRS 分类框架各阶段传感器配置和参数的研究。该方法可作为自动 BRS 分类的筛选工具，并有望在国内进一步应用。
