用单个便宜的惯性传感器实现假肢步态实时检测，精准又便宜。
IMU-based Real-Time Crutch Gait Phase and Step Detections in Lower-Limb Exoskeletons
- 在手杖上装一个惯性传感器，不用改假肢就能判断步态阶段。
- 94%准确率检测拐杖步数，延迟低，适合实时控制。
- 模型能跨人群通用，训练时只用健康人数据，也能用在瘫痪患者。
下肢外骨骼和假肢需要精确、实时的步态阶段与步数检测以确保运动同步与用户安全。传统方法常依赖复杂的力感硬件，引入控制延迟。本文提出一种极简框架，仅使用集成于拐杖手柄的单个低成本惯性测量单元（IMU），避免机械改造。设计五阶段分类系统，包含标准步态阶段与非移动辅助状态，防止误动作。在个人电脑与嵌入式系统上对比三种深度学习架构，通过有限状态机（FSM）增强数据受限下的生物力学一致性，提升性能。时间卷积网络（TCN）表现最优，成功率达最高且延迟最低。值得注意的是，模型在未参与训练的瘫痪用户上仍有效。该系统实现94%的拐杖步检测成功率，为外骨骼实时控制提供高性能、低成本解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。