Deep Learning based Gait Abnormality Detection using Wearable Sensor System

Potluri, Sasanka; Ravuri, Srinivas; Diedrich, Christian; Schega, Lutz

doi:10.1109/embc.2019.8856454

Cited by 31 publications

(19 citation statements)

References 17 publications

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“…The use of machine learning algorithms has shown its usefulness in the search for an individual gait pattern and its evolution over different time scales [28]. This gait pattern recognition can be used to successfully solve tasks for classifying gait disorders [29][30][31][32] or for extracting gait characteristics [33]. Deep learning methods have also been used to characterise the gait phase the subject is in, thus resulting in IC/FC detection from multiple accelerometers [34], 3D markers [35,36] or instrumented shoes [37].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Template-Based Approach for the Study of Locomotion

Dot

Quijoux

Oudre

et al. 2020

Sensors

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The automatic detection of gait events (i.e., Initial Contact (IC) and Final Contact (FC)) is crucial for the characterisation of gait from Inertial Measurements Units. In this article, we present a method for detecting steps (i.e., IC and FC) from signals of gait sequences of individuals recorded with a gyrometer. The proposed approach combines the use of a dictionary of templates and a Dynamic Time Warping (DTW) measure of fit to retrieve these templates into input signals. Several strategies for choosing and learning the adequate templates from annotated data are also described. The method is tested on thirteen healthy subjects and compared to gold standard. Depending of the template choice, the proposed algorithm achieves average errors from 0.01 to 0.03 s for the detection of IC, FC and step duration. Results demonstrate that the use of DTW allows achieving these performances with only one single template. DTW is a convenient tool to perform pattern recognition on gait gyrometer signals. This study paves the way for new step detection methods: it shows that using one single template associated with non-linear deformations may be sufficient to model the gait of healthy subjects.

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Section: Introductionmentioning

confidence: 99%

Non-Linear Template-Based Approach for the Study of Locomotion

Dot

Quijoux

Oudre

et al. 2020

Sensors

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“…In previous studies, anomalous gait data were acquired by attaching weights to particular body parts of a person without gait abnormalities [16] or by imitating anomalous gait kinematics according to the prescribed protocols [15,46,47]. In our study, to reflect the incidence of abnormal gait in the population, a large number of subjects representing the age and gender distributions of the community in Seongnam, South Korea were recruited over a 5-year period.…”

Section: Discussionmentioning

confidence: 99%

Anomalous Gait Feature Classification From 3-D Motion Capture Data

Jeon

Lee

Koo

2022

IEEE J. Biomed. Health Inform.

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The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to develop a machine learning method for automatically classifying five anomalous gait features, i.e., toe-out, genu varum, pes planus, hindfoot valgus, and forward head posture features, from threedimensional data on gait kinematics. Gait data and gait feature labels of 488 subjects were acquired. The orientations of the human body segments during a gait cycle were mapped to a low-dimensional latent gait vector using a variational autoencoder. A two-layer neural network was trained to classify five gait features using logistic regression and calculate an anomalous gait feature vector (AGFV). The proposed network showed balanced accuracies of 82.8% for a toe-out, 85.9% for hindfoot valgus, 80.2% for pes planus, 73.2% for genu varum, and 92.9% for forward head posture when the AGFV was rounded to the nearest zero or 1. Multiple anomalous gait features were detectable using the proposed method, which has a practical advantage over current gait indices, including the gait deviation index with a single value. The overall results confirmed the feasibility of using the proposed method for screening subjects with anomalous gait features using three-dimensional motion capture data.

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“…al. measured the significant gait parameters using wearable plantar pressure and inertial sensors to investigate differences between normal and abnormal walking patterns identified by a long short-term memory (LSTM) model to predict the risk of fall [13]. Similar research by Gao et.…”

Section: Introductionmentioning

confidence: 89%

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Rana

Dey

Ghavami

et al. 2021

IEEE Trans. Instrum. Meas.

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Gait disorder diagnosis and rehabilitation is one area where human perception and observation are highly integrated. Predominantly, gait evaluation, comprises technological devices for gait analysis such as, dedicated force sensors, cameras, and wearable sensor based solutions, however they are limited by insufficient gait parameter recognition, post processing, installation costs, mobility, and skin irritation issues. Thus, the proposed study concentrates on the creation of a widely deployable, noncontact and non-intrusive gait recognition method from impulse radio ultra wideband (IR-UWB) sensing phenomenon, where a standalone IR-UWB system can detect gait problems with less human intervention. A 3D human motion model for gait identification from IR-UWB has been proposed with embracing spherical trigonometry and vector algebra to determine knee angles. Subsequently, normal and abnormal walking subjects were involved in this study. Abnormal gait subjects belong to the spastic gait category only. The prototype has been tested in both the anechoic and multipath environments. The outcomes have been corroborated with a simultaneously deployed Kinect Xbox sensor and supported by statistical graphical approach Bland and Altman (B&A) analysis.

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Deep Learning based Gait Abnormality Detection using Wearable Sensor System

Cited by 31 publications

References 17 publications

Non-Linear Template-Based Approach for the Study of Locomotion

Non-Linear Template-Based Approach for the Study of Locomotion

Anomalous Gait Feature Classification From 3-D Motion Capture Data

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

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