IEEE Trans Neural Syst Rehabil Eng. 2013 Nov;21(6):908-16. doi: 10.1109/TNSRE.2013.2239313. Epub 2013 Jan 11.
Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. This paper describes a novel and generic event detection algorithm applicable to signals from tri-axial accelerometers placed on the foot, ankle, shank or waist. Data from healthy subjects undergoing multiple walking trials on flat and inclined, as well as smooth and tactile paving surfaces is acquired for experimentation. The benchmark timings at which heel strike and toe off occur, are determined using kinematic data recorded from a motion capture system. The algorithm extracts features from each of the acceleration signals using a continuous wavelet transform over a wide range of scales. A locality preserving embedding method is then applied to reduce the high dimensionality caused by the multiple scales while preserving salient features for classification. A simple Gaussian mixture model is then trained to classify each of the time samples into heel strike, toe off or no event categories. Results show good detection and temporal accuracies for different sensor locations and different walking terrains.
加速度计在人体生物力学中是一种广泛使用的传感模式，因为它具有便携性、非侵入性和准确性。然而，在与生物力学事件相关的信号可变性和解释方面存在困难。在行走中，脚跟触地和脚趾离地是主要的步态事件，在这些事件中，稳健和准确的检测对于与步态相关的应用至关重要。本文描述了一种新颖的通用事件检测算法，适用于放置在脚部、脚踝、小腿或腰部的三轴加速度计的信号。为了进行实验，从在平坦和倾斜以及光滑和触觉路面上进行多次行走试验的健康受试者那里获取数据。使用从运动捕捉系统记录的运动数据确定脚跟触地和脚趾离地发生的基准定时。该算法使用连续小波变换从每个加速度信号中提取特征，跨越广泛的尺度。然后应用局部保持嵌入方法来减少由多个尺度引起的高维性，同时保留用于分类的显著特征。然后训练一个简单的高斯混合模型，将每个时间样本分类为脚跟触地、脚趾离地或无事件类别。结果表明，不同传感器位置和不同行走地形的检测和时间精度都很好。
