Recognizing locomotion modes is a crucial step in controlling lower-limb exoskeletons/orthoses. Our study proposed a fuzzy-logic-based locomotion mode/transition recognition approach that uses the onrobot inertial sensors for a hip joint exoskeleton (active pelvic orthosis). The method outputs the recognition decisions at each extreme point of the hip joint angles purely relying on the integrated inertial sensors. Compared with the related studies, our approach enables calibrations and recognition without additional sensors on the feet. We validated the method by measuring four locomotion modes and eight locomotion transitions on three able-bodied subjects wearing an active pelvic orthosis (APO). The average recognition accuracy was 92.46% for intrasubject crossvalidation and 93.16% for intersubject crossvalidation. The average time delay during the transitions was 1897.9 ms (28.95% one gait cycle). The results were at the same level as the related studies. On the other side, the study is limited in the small sample size of the subjects, and the results are preliminary. Future efforts will be paid on more extensive evaluations in practical applications.

中文翻译：

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髋关节外骨骼惯性信号的运动模式识别

识别运动模式是控制下肢外骨骼/矫形器的关键步骤。我们的研究提出了一种基于模糊逻辑的运动模式/转换识别方法，该方法将 onrobot 惯性传感器用于髋关节外骨骼（主动骨盆矫形器）。该方法在髋关节角度的每个极值点输出识别决策，完全依赖于集成的惯性传感器。与相关研究相比，我们的方法无需在脚上安装额外的传感器即可进行校准和识别。我们通过测量佩戴主动骨盆矫形器 (APO) 的三名身体健全的受试者的四种运动模式和八种运动转换来验证该方法。受试者内交叉验证的平均识别准确率为 92.46%，受试者间交叉验证的平均识别准确率为 93.16%。转换期间的平均时间延迟为 1897.9 毫秒（一个步态周期为 28.95%）。结果与相关研究处于同一水平。另一方面，该研究受限于受试者的小样本量，结果是初步的。未来将努力在实际应用中进行更广泛的评估。