Evaluation of gait parameters for gait phase detection during walking

Senanayake, C.M.; Senanayake, S. M. N. Arosha

doi:10.1109/mfi.2010.5604472

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…Similar kind of work is reported in [19], in which author deployed hip, ankle, and knee angle to segment gait cycle. Another work in [20] relies on 3D information.…”

Section: Literature Surveymentioning

confidence: 64%

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Fuzzy Logic-Based Gait Phase Detection Using Passive Markers

Prakash

Gupta

Kumar

et al. 2016

Advances in Intelligent Systems and Computing

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With the advancement in technology, gait analysis plays a vital role in sports, science, rehabilitation, geriatric care, and medical diagnostics. Identification of accurate gait phase is of paramount importance. The objective of this paper is to put forward a novel approach via passive marker-based optical approach that automatically recognizes gait subphases using fuzzy logic approach from hip and knee angle parameters extracted at RAMAN lab at MNIT, Jaipur. In addition to stance phase and swing phase, the approach is capable of detecting all the subphases such as initial swing, mid swing, and terminal swing, loading response, mid stance, terminal stance and preswing. The prototype of the system provides an effective and accurate gait phase that could be used for understanding patients' gait pathology and in control strategies for active lower extremity prosthetics and orthotics. It is an automated, easy to use, and very cost-efficient yet reliable model.

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“…Similar kind of work is reported in [19], in which author deployed hip, ankle, and knee angle to segment gait cycle. Another work in [20] relies on 3D information.…”

Section: Literature Surveymentioning

confidence: 64%

“…At a given stance of time, kinematic parameters such as hip, ankle, and knee angle can be put to use to detect the gait phase [22]. One possible approach for gait segmentation is by setting threshold for discrete event analysis.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Fuzzy Logic-Based Gait Phase Detection Using Passive Markers

Prakash

Gupta

Kumar

et al. 2016

Advances in Intelligent Systems and Computing

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“…The curve characteristic correlated with the subphase detection of the swing-phase. 26 This hypothesis can be affirmed in the future.…”

Section: Future Workmentioning

confidence: 86%

Quantitative method for gait pattern detection based on fiber Bragg grating sensors

Ding

Tong

2017

J. Biomed. Opt

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This paper presents a method that uses fiber Bragg grating (FBG) sensors to distinguish the temporal gait patterns in gait cycles. Unlike most conventional methods that focus on electronic sensors to collect those physical quantities (i.e., strains, forces, pressure, displacements, velocity, and accelerations), the proposed method utilizes the backreflected peak wavelength from FBG sensors to describe the motion characteristics in human walking. Specifically, the FBG sensors are sensitive to external strain with the result that their backreflected peak wavelength will be shifted according to the extent of the influence of external strain. Therefore, when subjects walk in different gait patterns, the strains on FBG sensors will be different such that the magnitude of the backreflected peak wavelength varies. To test the reliability of the FBG sensor platform for gait pattern detection, the gold standard method using force-sensitive resistors (FSRs) for defining gait patterns is introduced as a reference platform. The reliability of the FBG sensor platform is determined by comparing the detection results between the FBG sensors and FSRs platforms. The experimental results show that the FBG sensor platform is reliable in gait pattern detection and gains high reliability when compared with the reference platform.

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“…For example, vision based gait recognition can be a useful tool in forensic analysis of crime where no fingerprint is available [1], [2]. In addition, gait recognition has become one of the most reliable applications in the area of physical therapy, biometrics, rehabilitation, sports, science, and geriatric care [3]- [5]. Clinicians are also able to utilize gait segmentation concept in their routine clinical practice to evaluate a patient's status, treatment, and rehabilitation for complex musculoskeletal and neurological disorders [2], [6].…”

Section: Introductionmentioning

confidence: 99%

Triangle and Trapezoid Area Features for Gait Authentication

Sikandar

Azhani

Samsudin

et al. 2018

2018 9th IEEE Control and System Graduate Research Colloquium (ICSGRC)

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This paper presents two gait authentication features based on geometric shape for gait analysis. Specifically, triangle and trapezoid based features are proposed for gait authentication. The features are based on the geometric pattern extracted from a particular gait cycle of a gait model. These features use four points from hip-knee-toe joints and construct a triangle and a right trapezoid. The area of the triangle and trapezoid are calculated using geometric formula as well as image processing methods. Later two areas are compared to validate the model free approach. The results show that, the proposed feature can be used as the features in model free gait analysis.

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Evaluation of gait parameters for gait phase detection during walking

Cited by 14 publications

References 16 publications

Fuzzy Logic-Based Gait Phase Detection Using Passive Markers

Fuzzy Logic-Based Gait Phase Detection Using Passive Markers

Quantitative method for gait pattern detection based on fiber Bragg grating sensors

Triangle and Trapezoid Area Features for Gait Authentication

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