Feature extraction from ear-worn sensor data for gait analysis

Ling Li, L. Atallah, Benny P. L. Lo, Guang-Zhong Yang
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引用次数: 4

Abstract

Gait analysis has a significant role in assessing human's walking pattern. It is generally used in sports science for understanding body mechanics, and it is also used to monitor patients' neuro-disorder related gait abnormalities. Traditional marker-based systems are well known for tracking gait parameters for gait analysis, however, it requires long set up time therefore very difficult to be applied in everyday realtime monitoring. Nowadays, there is ever growing of interest in developing portable devices and their supporting software with novel algorithms for gait pattern analysis. The aim of this research is to investigate the possibilities of novel gait pattern detection algorithms for accelerometer-based sensors. In particular, we have used e-AR sensor, an ear-worn sensor which registers body motion via its embedded 3-D accelerom-eter. Gait data was given semantic annotation using pressure mat as well as real-time video recording. Important time stamps within a gait cycle, which are essential for extracting meaningful gait parameters, were identified. Furthermore, advanced signal processing algorithm was applied to perform automatic feature extraction by signal decomposition and reconstruction. Analysis on real-word data has demonstrated the potential for an accelerometer-based sensor system and its ability to extract of meaningful gait parameters.
用于步态分析的耳戴式传感器数据特征提取
步态分析在评估人类的行走方式中具有重要的作用。它通常用于运动科学,用于了解身体力学,也用于监测患者与神经障碍相关的步态异常。传统的基于标记的系统以跟踪步态参数进行步态分析而闻名,但其设置时间较长,难以应用于日常的实时监测。目前,人们对开发具有新型步态模式分析算法的便携式设备及其配套软件越来越感兴趣。本研究的目的是探讨基于加速度计传感器的新型步态模式检测算法的可能性。特别是,我们使用了e-AR传感器,这是一种耳戴式传感器,通过其内置的3d加速度计记录身体运动。采用压力垫对步态数据进行语义标注和实时视频记录。识别出步态周期内重要的时间戳,这是提取有意义的步态参数所必需的。在此基础上,采用先进的信号处理算法,通过信号分解和重构实现特征的自动提取。对实时数据的分析已经证明了基于加速度计的传感器系统的潜力及其提取有意义的步态参数的能力。
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