Using Deep Learning Methods to Predict Walking Intensity from Plantar Pressure Images

Chen, Hsing‐Chung; Sunardi, Sunardi; Jan, Yih‐Kuen; Liau, Ben-Yi; Lin, Chen‐Yuan; Tsai, Jen Yung; Li, Cheng Tsung; Lung, Chi-Wen

doi:10.1007/978-3-030-80713-9_35

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Additionally, regarding foot deformity classification, a similar labeling method was applied in the research of the author Chae J. et al, in which their results were then used to create machine learning algorithms [ 20 ]. Another recent example of a machine learning approach in orthopedics was the prediction of the risk of diabetic foot ulcers from plantar pressure images, with a procedure similar to ours [ 24 ]. Furthermore, the estimation of various walking intensities based on wearable plantar pressure sensors was performed using an artificial neural network, another machine learning method.…”

Section: Discussionmentioning

confidence: 99%

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities

et al. 2023

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Background and Objectives: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an optimal level of objectivity and reliability. The acquired results would enable an individual approach to the treatment of patients with foot deformities. Thus, the goal of this research study was the development of a new, objective model for recognizing and classifying foot deformities with the application of machine learning, by labeling baropodometric analysis data using computer vision methods. Materials and Methods: In this work, data from 91 students of the Faculty of Medicine and the Faculty of Sports and Physical Education, University of Novi Sad were used. Measurements were determined by using a baropodometric platform, and the labelling process was carried out in the Python programming language, using functions from the OpenCV library. Segmentation techniques, geometric transformations, contour detection and morphological image processing were performed on the images, in order to calculate the arch index, a parameter that gives information about the type of the foot deformity. Discussion: The foot over which the entire labeling method was applied had an arch index value of 0.27, which indicates the accuracy of the method and is in accordance with the literature. On the other hand, the method presented in our study needs further improvement and optimization, since the results of the segmentation techniques can vary when the images are not consistent. Conclusions: The labeling method presented in this work provides the basis for further optimization and development of a foot deformity classification system.

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Section: Discussionmentioning

confidence: 99%

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities

et al. 2023

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“…In the fourth step, a ratio of 80% of the dataset utilized for model learning was used for model training data, while 20% was used for model validation. In many disciplines, using machine learning or deep learning models to solve issues typically involves dividing information into ratios [5]. Furthermore, each category received 200 testing data, for 1,000 testing data spread throughout 5 classes.…”

Section: Methodsmentioning

confidence: 99%

“…Before being used in model training, preprocessing data is a crucial stage in its preparation and transformation, when the range of the data samples varies, normalization is a popular data processing technique where numerical column values are altered to have a uniform scale [5]. It is essential to scale the data into a value range of -1 to 1 in normalization process before using it to reconstruct the dataset into two dimensions since the power quality distribution one-dimensional dataset value has a broad range, specifically between -7,185 and 11,997.…”

Section: Data Preparationmentioning

confidence: 99%

Regulated 2D Grayscale Image for Finding Power Quality Abnormalities in Actual Data

Chen

Syamsudin

Berutu

2022

J. Phys.: Conf. Ser.

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It is possible to preserve power quality by classifying and identifying abnormalities. Prior studies focused on enhancing the PQD classification performance in one-dimensional (1D) CNNs. Recently, various image conversion methods have been established to facilitate CNN for PQD classification. PQD is a 1D signal that needs to be converted to a 2D image through data pre-processing since 2D images may include more PQD information than 1D signals. However, the PQD data used for the power quality classifier is synthetic PQD produced using mathematical models with parameter modifications in accordance with IEEE Std. 1159, which places limitations on prior research. This study uses data from the Amrita Honeywell Hackathon 2021 to examine how the response-based 2D deep CNN power quality classifier responds to actual field power quality disruptions. The results of the study show that a 2D deep CNN with regulated 2D grayscale pictures based on a process-regulated 2D image matrix can classify real data power quality disturbances with accuracy, precision, recall, and F1-score of 98.80%, 98.99%, and 98.60%, respectively. Additionally, 2D images can potentially contain more PQD data than 1D signals, enhancing identification performance on actual data.

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“…Zhang et al [37] focused on gait recognition using a combination of pressure signals and acceleration signals to make up for the lack of data provided by a single sensor and transmitted the data to a computer for signal processing, and building a k nearest neighbor (kNN) model to test gait pattern recognition effect. Chen et al [38] explored DL algorithms including ResNet50, InceptionV3, and MobileNet to identify differences in the response of walking speed to plantar pressure. Jun et al [39] performed pathological gaits classification, feeding the sequential skeleton and average foot pressure data into a recurrent neural network (RNN) based encoding layers and CNN-based encoding layers, respectively.…”

Section: A Velostat-based Applicationmentioning

confidence: 99%

Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

Yuan

2021

2021 IEEE Sensors

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With the emphasis on healthcare, early childhood education, and fitness, non-invasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied due to its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introduces a smart pressure e-mat (SPeM) system based on a piezoresistive material Velostat for human monitoring applications, including sleeping postures, sports, and yoga recognition. After a subsystem scans e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks (DNNs) are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and five dynamic activities inspired by Nintendo Switch Ring Fit Adventure (RFA) are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies on both applications, which demonstrates the high accuracy and generalization ability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties.

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Using Deep Learning Methods to Predict Walking Intensity from Plantar Pressure Images

Cited by 10 publications

References 13 publications

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities

Regulated 2D Grayscale Image for Finding Power Quality Abnormalities in Actual Data

Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

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