同时识别步行模式与步态相位，提升截肢假肢控制流畅性。
Simultaneous Locomotion Mode Classification and Continuous Gait Phase Estimation for Transtibial Prostheses
- 基于人体与假肢数据，联合预测运动模式与步态相位。
- 模式识别准确率达99.1%~99.3%，步态相位误差低于4%。
- 算法高效（每步2.91微秒），适合日常多模式活动识别。
识别和理解人类运动是实现可穿戴机器人（如截肢假肢）流畅控制的关键步骤。特别是对预期运动模式的分类和步态相位的估计至关重要。本文提出一种新颖、可解释且计算高效的算法，可同步预测运动模式与步态相位。利用健全人（AB）和截肢假肢（PR）数据，测试了七种运动模式：慢速、中速、快速平地行走（0.6、0.8、1.0 m/s）、5度斜坡上下行及20厘米台阶上下行。在AB与PR条件下，总体分类准确率分别达到99.1%和99.3%；所有数据下平均步态相位误差小于4%。利用数据结构特性，算法计算效率达每时间步2.91 μs，时间复杂度为O(N·M)，其中M为运动模式数，N为每步周期样本数。该效率与高精度可支持约700种运动模式（基于Open-Source Leg Prosthesis平台），覆盖日常生活的多样化活动需求。
原文摘要 · Abstract (English)
Recognizing and identifying human locomotion is a critical step to ensuring fluent control of wearable robots, such as transtibial prostheses. In particular, classifying the intended locomotion mode and estimating the gait phase are key. In this work, a novel, interpretable, and computationally efficient algorithm is presented for simultaneously predicting locomotion mode and gait phase. Using able-bodied (AB) and transtibial prosthesis (PR) data, seven locomotion modes are tested including slow, medium, and fast level walking (0.6, 0.8, and 1.0 m/s), ramp ascent/descent (5 degrees), and stair ascent/descent (20 cm height). Overall classification accuracy was 99.1$\%$ and 99.3$\%$ for the AB and PR conditions, respectively. The average gait phase error across all data was less than 4$\%$. Exploiting the structure of the data, computational efficiency reached 2.91 $μ$s per time step. The time complexity of this algorithm scales as $O(N\cdot M)$ with the number of locomotion modes $M$ and samples per gait cycle $N$. This efficiency and high accuracy could accommodate a much larger set of locomotion modes ($\sim$ 700 on Open-Source Leg Prosthesis) to handle the wide range of activities pursued by individuals during daily living.
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