Abnormal Gait Recognition in Real-Time using Recurrent Neural Networks

Jinnovart, Thanaporn; Cai, Xiongcai; Thonglek, Kundjanasith

doi:10.1109/cdc42340.2020.9304106

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…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

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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.

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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

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“…Some studies have investigated pathological and physiological features in classifying human movement with kinematic data [ 27 , 28 ]. Although there are some advantages of using kinematic data for gait analysis, kinematic data are more difficult to obtain.…”

Section: Discussionmentioning

confidence: 99%

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors

Bawa,

Banitsas,

Abbod

2024

Sensors

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Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used—namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)—to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes.

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“…En esta sección, se revisaron algunos de los trabajos enfocados en el reconocimiento de movimiento o ejercicios usando videos para el área del fitness o bien para rehabilitación aplicando diferentes tipos de redes neuronales artificiales. Jinnovart et al en 2020 [8] Reconocimiento anormal de la marcha en tiempo real mediante redes neuronales recurrentes simples, de memoria a corto plazo a largo plazo (LSTM) y red de unidad recurrente cerrada (GRU). Extraen 135 puntos clave del cuerpo humano usando OpenPose.…”

Section: Trabajos Relacionadosunclassified

Prototipo para el reconocimiento de video en tiempo real de los ejercicios del método Klapp usando redes neuronales recurrentes

Rojas¹,

Jiménez²,

Téllez³

et al. 2022

progmat

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En la actualidad la telerehabilitación ha tomado un gran auge debido a los acontecimientos de la COVID-19, éste trata de la aplicación de un programa bajo la supervisión remota, usando las tecnologías de información y comunicación, eliminando limitantes de las personas para su desplazamiento, minimizando riesgos y aportando mayor cumplimiento a los programas de rehabilitación y reacondicionamiento. El presente trabajo propone el desarrollo de una herramienta para el reconocimiento en tiempo real de ejercicios del método fisioterapéutico Klapp, basándonos en el concepto de telerehabilitación. El objetivo de este proyecto es dar apoyo a los fisioterapeutas para dar seguimiento al tratamiento de rehabilitación o reacondicionamiento. La herramienta permite evaluar la ejecución en tiempo real de cada uno de los ejercicios para informarle al paciente si lo realizó correctamente, y el fisioterapeuta podrá consultar dichos resultados. El desarrollo del proyecto fue gestionado bajo el modelo V como referencia para cada una de las actividades realizadas. La técnica utilizada para el reconocimiento son las redes neuronales recurrentes, pues su arquitectura permite modelar la siguiente predicción basándose en la información pasada y lograr modelar datos en secuencia como videos. Y para la implementación fueron empleadas las herramientas Keras, TensorFlow, Python, librerías de OpenCV y OpenPose, con el cual se obtuvieron los puntos de referencias de los ejercicios basándonos en los ángulos de Cobb. Los resultados obtenidos de reconocimiento de la red recurrente fueron medidos bajo la métrica de exactitud (Accuracy), logrando un 97% considerando 6 ejercicios, los cuales comprenden los ejercicios del método Klapp.

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Abnormal Gait Recognition in Real-Time using Recurrent Neural Networks

Cited by 9 publications

References 21 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

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors

Prototipo para el reconocimiento de video en tiempo real de los ejercicios del método Klapp usando redes neuronales recurrentes

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