Abnormal Gait Recognition Using 3D Joint information of Multiple Kinects System and RNN-LSTM

Lee, Deok-Won; Jun, Kooksung; Lee, Sanghyub; Ko, Joong-Kwang; Kim, Mun Sang

doi:10.1109/embc.2019.8857607

Cited by 20 publications

(15 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total number of 26 articles were reviewed. Most of the articles stated the number of participants in the study except in [26,28,37,42,43,45]. e majority of the studies used Kinect v2 as the main tool for capturing skeletal data for gait abnormality assessment, while a few articles used the older Kinect v1.…”

Section: Resultsmentioning

confidence: 99%

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

Bawa

Banitsas

Abbod

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.

show abstract

Section: Resultsmentioning

confidence: 99%

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

Bawa

Banitsas

Abbod

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…In the context of movement-related diseases, ML/DL techniques have been used, together with data provided by wearable or vision-based sensors, to support gait assessment with the aim of diagnosis and/or evaluation of disease progression [10,11,[45][46][47][48][49][50][51][52][53][54][55][56][57]. The main focus of most contributions is the detection of abnormal gait based on information extracted from gait data obtained with accelerometers, gyroscopes and/or pressure sensors [45,46,56,57], or with RGB-D cameras [10,[47][48][49][50][51][52][53][54][55].…”

Section: Related Workmentioning

confidence: 99%

“…Beyond the classic ML algorithms, deep learning (DL) has been used -either with wearable or vision-based sensors -for gait recognition with convolutional neural networks [58] or the identification [53,54,56] or classification [52,55,57] of abnormal gait, with most using long short-term memory networks. Reported accuracy values vary between 82% [53] and 98.5% [58].…”

Section: Related Workmentioning

confidence: 99%

Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning

Vilas-Boas

Rocha

Cardoso

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.

show abstract

“…The results can aid in the design of other pathological gait classification schemes. This paper is an extended version of a preliminary conference report that we presented at the 41st International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019 [15]. In the previous work, an LSTM-based classifier was used to classify the same pathological gaits, and different joint groups were fed to the classifier.…”

Section: Trendelenburg Gaitmentioning

confidence: 99%

Pathological Gait Classification Using Kinect v2 and Gated Recurrent Neural Networks

Jun

Lee

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

With the development of depth sensors and skeleton tracking algorithms, many skeletonbased pathological gait classification methods have recently been proposed. However, these methods classify only simple gait patterns, and there is no approach to classify complicated gait patterns. In this paper, we classify 1 normal and 5 pathological gaits (antalgic, stiff-legged, lurching, steppage, and Trendelenburg gaits) by using a gated recurrent unit (GRU)-based classifier and 3D skeleton data. We collected skeleton datasets for 1 normal and 5 pathological gaits by using a multiperspective Kinect system. We developed the GRU classifier to classify the pathological gaits and compared its performance with that of other machine learning-based classifiers. Furthermore, we considered various joint groups to identify important and irrelevant joints for pathological gait classification and to improve the performance of the GRU classifier. When all skeleton data are used, the GRU classifier achieves a classification accuracy of 90.13%. A long short-term memory (LSTM)-based classifier achieves the next highest accuracy of 87.25%. The classification accuracy of the GRU classifier depends on the joint groups considered, and the classification accuracy increases to 93.67% when only leg joints are considered. This study indicates that various pathological gaits can be classified by using skeleton data and the GRU classifier. The proposed method can be used to support medical and clinical decisions. Furthermore, the results for various joint groups can be used to develop other methods in cases where only specific joint data are available because of environmental limitations.

show abstract

Abnormal Gait Recognition Using 3D Joint information of Multiple Kinects System and RNN-LSTM

Cited by 20 publications

References 10 publications

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning

Pathological Gait Classification Using Kinect v2 and Gated Recurrent Neural Networks

Contact Info

Product

Resources

About