Background

The robust identification of initial contact (IC) and toe-off (TO) events is a vital task in mobile sensor-based gait analysis. Shank attached gyroscopes in combination with suitable algorithms for data processing can robustly and accurately complete this task for gait event detection. However, little research has considered gait detection algorithms that are applicable to different locomotion tasks.

Research question

Does a gait event detection algorithm for various locomotion tasks provide comparable estimation accuracies as existing task-specific algorithms?

Methods

Thirteen males, equipped with a gyroscope attached to the right shank, volunteered to perform nine different locomotion tasks consisting of linear movements and movements with a change of direction. A rule-based algorithm for IC and TO events was developed based on the shank sagittal plane angular velocity. The algorithm was evaluated against events determined by vertical ground reaction force. Absolute mean error (AME), relative absolute mean error (RAME) and Bland–Altman analysis was used to assess its accuracy.

Results

The average AME and RAME were 11 ± 3 ms and 3.07 ± 1.33 %, respectively, for IC and 29 ± 11 ms and 7.27 ± 2.92 %, respectively, for TO. Alterations of the walking movement, such as turns and types of running, slightly reduced the accuracy of IC and TO detection. In comparison to previous methods, increased or comparable accuracies for both IC and TO detection are shown.

Significance

The study shows that the proposed algorithm is capable of detecting gait events for a variety of locomotion tasks by means of a single gyroscope located on the shank. In consequence, the algorithm can be applied to activities, which consist of various movements (e.g., soccer). Ultimately, this extends the use of mobile sensor-based gait analysis.

中文翻译：

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使用一种新型的基于陀螺仪的算法，可以自动完成各种运动任务的步态事件检测。

背景

在基于移动传感器的步态分析中，可靠地识别初始接触（IC）和脚趾离开（TO）事件是一项至关重要的任务。附有柄的陀螺仪与合适的算法进行数据处理相结合，可以稳健而准确地完成步态事件检测任务。但是，很少有研究考虑适用于不同运动任务的步态检测算法。

研究问题

与现有任务特定算法相比，用于各种运动任务的步态事件检测算法是否具有可比的估计精度？

方法

13名男性，其右柄上装有陀螺仪，自愿执行9种不同的运动任务，包括线性运动和方向改变的运动。基于柄矢状平面角速度，开发了基于规则的IC和TO事件算法。针对垂直地面反作用力确定的事件对算法进行了评估。绝对平均误差（AME），相对绝对平均误差（RAME）和Bland–Altman分析用于评估其准确性。

结果

IC的平均AME和RAME分别为11±3 ms和3.07±1.33％，TO的平均分别为29±11 ms和7.27±2.92％。步行运动的改变（例如转弯和跑步类型）会稍微降低IC和TO检测的准确性。与以前的方法相比，IC和TO检测的准确度有所提高。

意义

研究表明，提出的算法能够通过位于柄上的单个陀螺仪检测各种运动任务的步态事件。结果，该算法可以应用于由各种运动（例如，足球）组成的活动。最终，这扩展了基于移动传感器的步态分析的使用。