Classification of gait patterns in patients with unilateral anterior cruciate ligament deficiency based on phase space reconstruction, Euclidean distance and neural networks

Zeng, Wei; Ismail, Shiek Abdullah; Pappas, Evangelos

doi:10.1007/s00500-019-04017-z

Cited by 9 publications

(4 citation statements)

References 57 publications

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“…This is frequently employed in clinical research to assist in understanding gait irregularities and their association with a particular underlying medical condition for better diagnosis and prognosis.This is done using machine leanring techniques involving different deep learning algorithms (Zeng 2020). For this, a variety of technologies that are integrated into specialised devices are being used, which include wearable devices, force platforms embedded in walkways, Inertial Measurement Unit (IMU) sensors, and computer-interfaced video cameras to measure patient motion (M Asif 2022).…”

Section: Gait Analysismentioning

confidence: 99%

Pose detection using deep learning for gait assessment

Peace

Abinayaa

Ebenezer

et al. 2023

Multidiscip. Sci. J.

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This approach of gait analysis helps in solving the problems associated with the health of aged people by concentrating on the frailty and senility syndromes that affect elderly individuals. Deterioration of cognitive and motor abilities is a common result of ageing and has an effect on elderly people's quality of life. Some studies have connected these changes in gait patterns to the decline in cognitive and motor function. As a result, gait analysis is a useful tool for diagnosing senility and frailty diseases. By transferring the pose estimation data to a correct statistical analysis, gait analysis can be carried out utilizing machine learning and computer vision approach, allowing physiotherapists to analyse and treat patients accordingly. The main problem observed is that physiotherapists will analyse stroke patients' walking patterns on their own. As a result, this approach solves the problem by providing an accurate result of the observation. As everything is computerised, proper treatment can be provided with accurate data. A physiotherapist could also use machine learning classification techniques to analyse and classify the observed disorder and correct it. Our study mainly focuses on using machine learning approach instead of traditional sensor based or consultation just by vision. This will serve as a newer and better proposition of the same.

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Section: Gait Analysismentioning

confidence: 99%

Pose detection using deep learning for gait assessment

Peace

Abinayaa

Ebenezer

et al. 2023

Multidiscip. Sci. J.

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“…Machine learning (ML) is widely used in many fields such as medical diagnosis (Begg and Kamruzzaman, 2006;Farah et al, 2019), pattern recognition (Shim and Lee, 2015;Souza and Stemmer, 2018), image processing (Leightley et al, 2017;Wei et al, 2018), classification (Van Gestel et al, 2011;Senanayake et al, 2014), predictive analysis (Yoo et al, 2013;Pla et al, 2017;Xiong et al, 2019), monitoring (Van Gestel et al, 2011;Yoo et al, 2013;Senanayake et al, 2014;Xiong et al, 2019;Zeng et al, 2020), and is therefore suitable for gait research. Nonetheless, ML techniques have been used in many gait applications, such as diagnosing gait disorders (Alaqtash et al, 2011a;Devanne et al, 2016;Leightley et al, 2017), predicting early intervention related to fall-related risks due to disability or aging (Begg et al, 2005;Begg and Kamruzzaman, 2006;Paulo et al, 2019), determining motor recovery tasks (Costa et al, 2016b;Goh et al, 2018), or planning rehabilitation or therapeutic interventions (Liu et al, 2016;Thongsook et al, 2019).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Bao,

Gao,

Wang

et al. 2023

Front. Robot. AI

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Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person’s gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992–2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

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“…Berruto et al [ 17 ] counted the fluctuation range of the acceleration of the patient’s legs with one ACL reconstruction in a pivot-shift test and demonstrated a significant difference between the ACLD-affected and contralateral sides. Zeng et al [ 18 ] used kinematic data extracted by a motion capture system as features for neural network training. Kokkotics et al [ 19 ] used different machine learning methods to identify patients with ACLD and ACL reconstruction from kinematics and dynamics data.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Multivariable Linear Regression Analysis to Identify Patients with Anterior Cruciate Ligament Deficiency Using a Composite Index of the Knee Flexion and Muscle Force

Huang

Ren

et al. 2023

Bioengineering

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Patients with anterior cruciate ligament (ACL) deficiency (ACLD) tend to have altered lower extremity kinematics and dynamics. Clinical diagnosis of ACLD requires more objective and convenient evaluation criteria. Twenty-five patients with ACLD before ACL reconstruction and nine healthy volunteers were recruited. Five experimental jogging data sets of each participant were collected and calculated using a musculoskeletal model. The resulting knee flexion and muscle force data were analyzed using a t-test for characteristic points, which were the time points in the gait cycle when the most significant difference between the two groups was observed. The data of the characteristic points were processed with principal component analysis to generate a composite index for multivariable linear regression. The accuracy rate of the regression model in diagnosing patients with ACLD was 81.4%. This study demonstrates that the multivariable linear regression model and composite index can be used to diagnose patients with ACLD. The composite index and characteristic points can be clinically objective and can be used to extract effective information quickly and conveniently.

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Classification of gait patterns in patients with unilateral anterior cruciate ligament deficiency based on phase space reconstruction, Euclidean distance and neural networks

Cited by 9 publications

References 57 publications

Pose detection using deep learning for gait assessment

Pose detection using deep learning for gait assessment

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Leveraging Multivariable Linear Regression Analysis to Identify Patients with Anterior Cruciate Ligament Deficiency Using a Composite Index of the Knee Flexion and Muscle Force

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