運動学的手法とLSTMモデルによる踵接地・つま先離地イベントの検出
Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models
588名の健常者の4363歩行周期を対象に、7つの運動学的手法とLSTMモデルで踵接地・つま先離地を検出し、Zeni法とLSTMが同等の精度を示したことを明らかにした。
著者: Longbin Zhang, Zhizhang Li, Xinyi Fu, Yi Xie, Xiaoyue Yan, Suiyuan Wang, Te Zhang, Hui Zhang, Kailun Yang, Tsung-Lin Wu, Prayook Jatesiktat, Ananda Sidarta, Wei Tech Ang
分類: cs.RO
原文アブストラクト
Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where precise identification of stance and swing phases is essential. This study evaluated the performance of seven kinematics-based methods and a Long Short-Term Memory (LSTM) model for detecting heel strike and toe-off events across 4363 gait cycles from 588 able-bodied subjects. The results indicated that while the Zeni et al. method achieved the highest accuracy among kinematics-based approaches, other methods exhibited systematic biases or required dataset-specific tuning. The LSTM model performed comparably to Zeni et al., providing a data-driven alternative without systematic bias. These findings highlight the potential of deep learning-based approaches for gait event detection while emphasizing the need for further validation in clinical populations and across diverse gait conditions. Future research will explore the generalizability of these methods in pathological populations, such as individuals with post-stroke conditions and knee osteoarthritis, as well as their robustness across varied gait conditions and data collection settings to enhance their applicability in rehabilitation and exoskeleton control.
