Lower limb exoskeletons and prostheses require precise, real time gait phase and step detections to ensure synchronized motion and user safety. Conventional methods often rely on complex force sensing hardware that introduces control latency. This paper presents a minimalist framework utilizing a single, low cost Inertial-Measurement Unit (IMU) integrated into the crutch hand grip, eliminating the need for mechanical modifications. We propose a five phase classification system, including standard gait phases and a non locomotor auxiliary state, to prevent undesired motion. Three deep learning architectures were benchmarked on both a PC and an embedded system. To improve performance under data constrained conditions, models were augmented with a Finite State Machine (FSM) to enforce biomechanical consistency. The Temporal Convolutional Network (TCN) emerged as the superior architecture, yielding the highest success rates and lowest latency. Notably, the model generalized to a paralyzed user despite being trained exclusively on healthy participants. Achieving a 94% success rate in detecting crutch steps, this system provides a high performance, cost effective solution for real time exoskeleton control.

翻译：下肢外骨骼与假肢需要精确、实时的步态相位与步态检测，以确保动作同步与使用者安全。传统方法通常依赖复杂的力传感硬件，这会引入控制延迟。本文提出了一种极简框架，利用单个低成本惯性测量单元（IMU）集成于拐杖手柄中，无需进行机械改造。我们提出了一种包含标准步态相位及一个非运动辅助状态在内的五相位分类系统，以防止非期望运动。在个人电脑与嵌入式系统上对三种深度学习架构进行了基准测试。为提升数据受限条件下的性能，模型通过有限状态机（FSM）进行增强，以强制保证生物力学一致性。时序卷积网络（TCN）表现最优，实现了最高的成功率与最低的延迟。值得注意的是，该模型尽管仅使用健康参与者的数据进行训练，却能泛化至瘫痪用户。该系统在检测拐杖步态方面达到了94%的成功率，为实时外骨骼控制提供了一种高性能、高性价比的解决方案。