A robust algorithm for gait cycle segmentation

Jiang, Shuo; Wang, Xingchen; Kyrarini, Maria; Gräser, Axel

doi:10.23919/eusipco.2017.8081163

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…Peak detection has been used to identify gait events through the inference of cyclic motion and reducing reliance on physical meanings of the signal by searching for the assumed cyclic pattern rather than a given threshold value or calculated joint angle (Jiang, Wang, Kyrarini, & Gräser, 2017). Peak detection from cross-correlated data has been used for gait event detection in accelerometry data (Yoneyama, Kurihara, Watanabe, & Mitoma, 2013).…”

Section: Discussionmentioning

confidence: 99%

seg1d: A Python package for Automated segmentation of one-dimensional (1D) data

Schwartz¹,

Pataky²,

Donnelly³

2020

JOSS

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Section: Discussionmentioning

confidence: 99%

seg1d: A Python package for Automated segmentation of one-dimensional (1D) data

Schwartz¹,

Pataky²,

Donnelly³

2020

JOSS

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“…In the Alg.3, they divided the signal into several segments based on the gait cycles. The methodology of this approach is based on [15,64]. Gait cycle segmentation was accomplished based on [64].…”

Section: Preprocessingmentioning

confidence: 99%

“…The methodology of this approach is based on [15,64]. Gait cycle segmentation was accomplished based on [64]. They computed MI(t) (Equation 1), where (Ax, Ay, Az) are the 3D accelerometer data; and…”

Section: Preprocessingmentioning

confidence: 99%

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Ahad

Ngo

Antar

et al. 2020

Sensors

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Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

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“…Peak-detection with thresholding is the most common approach for gait segmentation, especially for studies that use IMU data [ 40 , 44 , 45 ]. Although popular due to their simplicity, thresholding approaches are often challenged when handling noisy data or outliers [ 14 , 19 , 44 ]. Ultimately, however, it is hope that these types of sensors will become widely adopted in the community, leading to a much greater variability of usage scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, matched filtering [ 45 , 50 ] has been shown to effectively exploit the periodicity of the signal for gait segmentation while adding minimal complexity to the segmentation algorithm. Such approaches offer advantages over amplitude-based thresholding techniques, but have been shown to experience issues when the temporal structure varies, such as with varying stride durations [ 44 ]. Variable stride durations, however, are common in real-life walking conditions and in clinical settings, such as during rehabilitation from spinal cord injury or therapy for Parkinson ’s disease [ 14 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait

Gill

Seth

Scheme

2018

Sensors

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Individuals with mobility impairments related to age, injury, or disease, often require the help of an assistive device (AD) such as a cane to ambulate, increase safety, and improve overall stability. Instrumenting these devices has been proposed as a non-invasive way to proactively monitor an individual’s reliance on the AD while also obtaining information about behaviors and changes in gait. A critical first step in the analysis of these data, however, is the accurate processing and segmentation of the sensor data to extract relevant gait information. In this paper, we present a highly accurate multi-sensor-based gait segmentation algorithm that is robust to a variety of walking conditions using an AD. A matched filtering approach based on loading information is used in conjunction with an angular rate reversal and peak detection technique, to identify important gait events. The algorithm is tested over a variety of terrains using a hybrid sensorized cane, capable of measuring loading, mobility, and stability information. The reliability and accuracy of the proposed multi-sensor matched filter (MSMF) algorithm is compared with variations of the commonly employed gyroscope peak detection (GPD) algorithm. Results of an experiment with a group of 30 healthy participants walking over various terrains demonstrated the ability of the proposed segmentation algorithm to reliably and accurately segment gait events.

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A robust algorithm for gait cycle segmentation

Cited by 11 publications

References 9 publications

seg1d: A Python package for Automated segmentation of one-dimensional (1D) data

seg1d: A Python package for Automated segmentation of one-dimensional (1D) data

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait

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