Detect cane events in real-life walking situations is needed to assess indirectly human gait without using wearable systems which may be undesirable, uncomfortable, or difficult to wear, especially for patients who are undergoing rehabilitation. This article fosters two reliable kinematic data-based approaches—a machine learning classifier and an adaptive rule-based finite-state machine (FSM)—to detect four cane events that can operate at diverse gait speeds and on diverse real-life terrains in real-time. A comparative analysis was performed to identify the most suitable machine learning classifier and the most relevant subset of features. The FSM only uses the cane’s angular velocity and acceleration to facilitate its integration for daily and repeated use. Repeated measures from two groups of seven healthy subjects each were acquired to validate both approaches. The first group (23.29 ± 1.16 years) performed trials under controlled situations in a treadmill at different speeds (from 1.0 to 1.5 km/h) and slopes (from 0 to 10%). Heterogeneous gait patterns were further collected from the second group (24.14 ± 0.83 years) during forward walking on flat, rough, and inclined surfaces and climbing staircases. The CNN-LSTM when using the first 32 features ranked by the Relief-F method was more accurate than the FSM. The CNN-LSTM detects cane events accurately with an accuracy higher than 99% under controlled and real-life situations, except for the maximum support moment (MSM) (accuracy > 85.53%). The FSM detects most of the cane events accurately (accuracy > 90.63%). Misdetection was more pronounced in MSM (43.75% < accuracy < 84.91%). The lower computational load, together with high performances, makes these two approaches suitable for gait assessment in the rehabilitation field.

需要检测现实生活中的步行情况下的拐杖事件，以间接评估人的步态，而无需使用可穿戴系统，这可能是不希望的，不舒适的或难以穿戴的，特别是对于正在接受康复治疗的患者。本文提出了两种可靠的基于运动学数据的方法-机器学习分类器和基于自适应规则的有限状态机（FSM）-来检测四个拐杖事件，这些事件可以在不同的步态速度和实际的不同地形上进行操作-时间。进行了比较分析，以确定最合适的机器学习分类器和最相关的功能子集。FSM仅使用拐杖的角速度和加速度来促进其整合，以便日常使用和重复使用。从两组分别由七个健康受试者组成的小组中获取了重复测量值，以验证这两种方法。第一组（23.29±1.16岁）在受控情况下在跑步机上以不同的速度（从1.0到1.5 km / h）和坡度（从0到10％）进行了试验。在平坦，粗糙和倾斜的表面以及攀爬楼梯上向前行走时，从第二组（24.14±0.83年）中进一步收集了不同的步态模式。使用Relief-F方法排名的前32个功能时，CNN-LSTM比FSM更准确。CNN-LSTM在最大的控制力矩（MSM）之外（准确度> 85.53％），能够在受控和现实情况下准确地检测出甘蔗事件，其准确性高于99％。FSM能够准确检测到大多数甘蔗事件（准确性> 90.63％）。错误检测在MSM中更为明显（43.75％<准确度<84.91％）。较低的计算量以及较高的性能，使这两种方法都适合在康复领域进行步态评估。