Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany.
Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany.
Gait Posture. 2020 Sep;81:102-108. doi: 10.1016/j.gaitpost.2020.06.019. Epub 2020 Jun 16.
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.
Does a gait event detection algorithm for various locomotion tasks provide comparable estimation accuracies as existing task-specific algorithms?
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.
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.
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.
在基于移动传感器的步态分析中，稳健地识别初始接触 (IC) 和足趾离地 (TO) 事件是一项至关重要的任务。在结合了合适的数据处理算法的情况下，附着在小腿上的陀螺仪可以稳健且准确地完成步态事件检测任务。然而，很少有研究考虑适用于不同运动任务的步态检测算法。
适用于各种运动任务的步态事件检测算法是否能提供与特定任务算法相当的估计精度？
13 名男性志愿者在右小腿上安装了一个陀螺仪，参与了由直线运动和变向运动组成的 9 项不同的运动任务。基于小腿矢状面角速度，开发了一种用于 IC 和 TO 事件的基于规则的算法。该算法与垂直地面反力确定的事件进行了比较。使用绝对平均误差 (AME)、相对绝对平均误差 (RAME) 和 Bland-Altman 分析来评估其准确性。
IC 的平均 AME 和 RAME 分别为 11±3ms 和 3.07±1.33%，TO 的平均 AME 和 RAME 分别为 29±11ms 和 7.27±2.92%。步行运动的改变，如转弯和不同类型的跑步，略微降低了 IC 和 TO 检测的准确性。与以前的方法相比，该方法在 IC 和 TO 检测方面都显示出了更高或相当的准确性。
研究表明，所提出的算法能够通过位于小腿上的单个陀螺仪检测各种运动任务的步态事件。因此，该算法可应用于包含各种运动（例如足球）的活动中。最终，这扩展了基于移动传感器的步态分析的应用。
