This study evaluates the capability of a single inertial sensor based joint angles estimation during four different walking patterns in an outdoor setting. The sensor was placed on the upper part of the tibia, which was chosen due to its large range of motion and minimal foot-ground impact. A Bi-LSTM (bidirectional long short-term memory) data-driven approach was used for joint angle estimation. The results showed smaller errors in intra-subject angle estimation compared to inter-subject, with an average MAE (mean absolute error) of 2.11° to 3.65°. The study suggests that deep learning approaches can effectively process data from a single IMU (inertial measurement unit) for accurate human motion monitoring, reducing the need for multiple sensors. Despite using only one sensor and four different walking patterns (zigzag, sideways, backward, and ramp walking), our method achieved similar results to previous studies that used single-motion activities. This study, conducted outdoors without instructing participants, is a step closer to real-world application, potentially providing insights into lower body biomechanics in physiotherapy, mobility improvement progress after surgery, and aiding in the development of personalized exoskeletons robots.

中文翻译：

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使用优化复用采样方案的亚奈奎斯特相干成像 [提示与技巧]


本研究评估了在户外环境中四种不同步行模式下基于单个惯性传感器的关节角度估计的能力。传感器被放置在胫骨的上部，之所以选择它是因为它的运动范围大且脚部地面影响最小。 Bi-LSTM（双向长短期记忆）数据驱动方法用于关节角度估计。结果显示，与受试者间相比，受试者内角度估计的误差更小，平均 MAE（平均绝对误差）为 2.11° 至 3.65°。该研究表明，深度学习方法可以有效地处理来自单个 IMU（惯性测量单元）的数据，以实现精确的人体运动监测，从而减少对多个传感器的需求。尽管仅使用一个传感器和四种不同的行走模式（之字形、侧向、向后和坡道行走），我们的方法取得了与之前使用单运动活动的研究相似的结果。这项研究是在没有指导参与者的情况下在户外进行的，离现实世界的应用又近了一步，有可能为物理治疗中的下半身生物力学、手术后的活动能力改善进展提供见解，并有助于个性化外骨骼机器人的开发。