Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis

Abstract

PostprintThis is the accepted version of a paper published in IEEE transactions on neural systems and rehabilitation engineering. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans' natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

“…Continuous wavelet transform (CWT) is a widely used time-frequency analysis tool that effectively captures the general characteristics of the signal under observation [27]. Furthermore, CWT with a proper mother wavelet has shown robustness in detecting gait events [20,27,28,29]. The Morlet mother wavelet was adopted in this study to investigate the time-frequency relationship between the gait event and gait cycle [20].…”

“…Furthermore, CWT with a proper mother wavelet has shown robustness in detecting gait events [20,27,28,29]. The Morlet mother wavelet was adopted in this study to investigate the time-frequency relationship between the gait event and gait cycle [20]. The fundamental knowledge of the Morlet wavelet function and CWT are described as follows: ${\mathrm{sans-serif\psi }}_0(\mathsf{\eta })=\frac{1}{\sqrt[4]{\mathrm{sans-serif\pi }}}{e}^{i{w}_0\mathsf{\eta }}{e}^{-\frac{{\mathrm{sans-serif\eta }}^2}{2}}$…”

“…From Figure 4, we can also see that the time-frequency relationship between the individual gait events (HS and TO) and their corresponding gait cycleTable is represented clearly. Therefore, it would be possible to use the time-frequency characteristics of acceleration signals to capture gait events during walking as reported in a previous study [20]. Hence, we applied the time-frequency analysis technique to the data in the observation window to detect gait events and also obtained the desired threshold parameters for the real-time heuristic based algorithm.…”

“…However, the power consumption rate of a typical gyroscope sensor is relatively large (usually several mA), which limits its long-term use in everyday life. Accelerometers are another type of widely used wearable inertial sensors with relatively low power consumption (usually a few μA), and have been shown to provide reliable measures of gait parameters [20]. Several algorithms have been proposed to detect TO and HS events from accelerometer signals in previous studies, among which the threshold-based algorithms have been proven to be reliable for gait event detection and can be used to trigger functional electrical stimulation during level ground walking [1,2].…”

“…Kotiadis et al indicated that poor detection rates were often achieved with existing methods in the terrains of upstairs and downstairs compared to level ground walking [2]. Recently, Khandelwal et al proposed a wavelet based time-frequency analysis method to detect gait events with acceleration signals recorded from real world walking environments [20]. Although their algorithm could achieve gait event detection with relatively good accuracy and robustness, it is an offline-based method that may be inapplicable in a number of real-time applications, such as the triggering of functional electrical stimulation based devices and active prostheses.…”