Digital Signal Processing and System Theory, Institute of Electrical and Information Engineering, Kiel University, Kaiserstraße 2, 24143, Kiel, Germany.
Neurogeriatrics, Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Straße 3, Haus D, 24105, Kiel, Germany.
J Neuroeng Rehabil. 2021 Feb 6;18(1):28. doi: 10.1186/s12984-021-00828-0.
Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson's disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions.
Participants (older adults, people with Parkinson's disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories.
The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%), slalom walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%), and turning (IC: recall [Formula: see text] 85%, precision [Formula: see text] 95%, F1 score [Formula: see text]91%; FC: recall [Formula: see text] 84%, precision [Formula: see text] 95%, F1 score [Formula: see text]89%).
Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.
个体步态事件的识别对于临床步态分析至关重要，因为它可用于诊断目的或跟踪帕金森病等神经疾病的进展。先前的研究表明，步态事件可以从安装在小腿上的惯性测量单元（IMU）中检测到，但是检测性能通常仅从直线行走中进行评估。为了在日常生活中使用，需要在曲线行走和转弯以及单任务和双任务条件下评估检测性能。
参与者（老年人、帕金森病患者或中风患者）进行了三种不同的行走试验：（1）直线行走，（2）障碍行走，（3）斯特鲁普行走试验。使用光学运动捕捉系统作为参考系统。在鞋的脚跟和脚趾区域贴上标记，参与者在两侧小腿上安装 IMU。小腿 IMU 的角速度用于检测初始足触（IC）和最终足触（FC）的实例，并与从标记轨迹获得的参考值进行比较。
在不同人群的直线行走过程中，初始接触和最终接触的检测方法均显示出较高的召回率、精度和 F1 得分（IC：召回率 [公式：见文本]100%，精度 [公式：见文本]100%，F1 得分 [公式：见文本]100%；FC：召回率 [公式：见文本]100%，精度 [公式：见文本]100%，F1 得分 [公式：见文本]100%），障碍行走（IC：召回率 [公式：见文本]100%，精度 [公式：见文本]99%，F1 得分 [公式：见文本]100%；FC：召回率 [公式：见文本]100%，精度 [公式：见文本]99%，F1 得分 [公式：见文本]100%）和转弯（IC：召回率 [公式：见文本]85%，精度 [公式：见文本]95%，F1 得分 [公式：见文本]91%；FC：召回率 [公式：见文本]84%，精度 [公式：见文本]95%，F1 得分 [公式：见文本]89%）。
小腿安装的 IMU 可用于检测直线行走、障碍行走和转弯过程中的步态事件。然而，在转弯过程中观察到更多的假事件，并且在转弯过程中错过了更多的事件。为了在日常生活中使用，我们建议在从识别的步态事件中提取时间步态参数之前，识别转弯。
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