Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method

Lee, Posen; Chen, Tai‐Been; Liu, Chin-Hsuan; Wang, Chi-Yuan; Huang, Guan‐Hua; Lu, Nan‐Han

doi:10.3390/bios12050295

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…The future of research in the area of assistive technologies design lies in the increased use of robotic devices, personalised by 3D printing; Internet of Things wearable sensors for personalised training of limb use, gait and balance, and activities of daily living and health monitoring [17][18][19][20][21]; and even novel brain-computer interfaces [22,23] and associated computational models [24]. Further, more advanced studies may additionally use data from postural and gait analysis [25,26]. We will also draw inspiration as to the direction of further research from the work of [27][28][29].…”

Section: Directions For Further Researchmentioning

confidence: 99%

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

et al. 2023

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It is increasingly important to provide post-stroke patients with rapid access to patient-tailored assistive technologies to increase independence, mobility, and participation. Automating the selection of assistive devices based on artificial intelligence could speed up the process and improve accuracy. It would also relieve the burden on diagnosticians and therapists and speed up the introduction of new ranges by automating databases. This article compares selected machine learning classification methods in the area of post-stroke rehabilitation device selection. The article covers the specifics of the selection, the choice of classification methods, and the identification of the best one, as well as the experimental part, the description of the results, the comparison process, and directions for further research. The novelty lies both in the topic, as the choice of classification method has an impact on the accuracy of classification in the selection of medical materials, and in the manner of the comprehensive approach. The possible contribution is of great scientific and clinical relevance, but above all, it has economic and social importance, enabling post-stroke individuals to return more quickly to the community, learning, and work, and relieving the burden on the health care system.

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Section: Directions For Further Researchmentioning

confidence: 99%

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

et al. 2023

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“…The videos captured by a cellphone camera can be processed using the OpenPose algorithm to create dynamic joint node plots (DJNPs), which depict the movement of joint nodes over time. The DJNPs allow for the analysis of dynamic movements during walking [ 12 ]. Support Vector Machines (SVM) have also been utilized in postural control analysis.…”

Section: Introductionmentioning

confidence: 99%

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach

Lee,

Chen,

Lin

et al. 2024

Bioengineering

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Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system’s robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

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“…Rapid changes in human posture are linked with fall incidence [ 20 , 21 ], whereas specific postures or patterns of human movements are present in various types of PAs [ 15 ]. Recently, several advanced methods based on deep neural networks have been proposed to extract the discriminant patterns related to human postures or movements from images through machine learning [ 22 , 23 , 24 ]. Vision-based approaches are beneficial due to the characteristic of wear-free, unobstructed monitoring, but it is only available in spaces where cameras have been installed.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing

Chan

Ouyang

Chen

et al. 2023

Sensors

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Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types of PAs based on a convolutional neural network and recurrent neural network hybrid. The footwear-based detections using deep-learning technology were demonstrated to be efficient based on the data collected from 32 participants, each performing simulated falls and various types of PAs: fall detection with inertial measures had a higher F1-score than detection using foot pressures; the detections of dynamic PAs (jump, jog, walks) had higher F1-scores while using inertial measures, whereas the detections of static PAs (sit, stand) had higher F1-scores while using foot pressures; the combination of foot pressures and inertial measures was most efficient in detecting fall, static, and dynamic PAs.

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Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method

Cited by 3 publications

References 28 publications

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach

Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing

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