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All Journal International Journal of Electrical and Computer Engineering Journal of Electronics, Electromedical Engineering, and Medical Informatics
Chaudhari, Sachin Vasant
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Review of gait recognition systems: approaches and challenges Mandlik, Sachin B.; Labade, Rekha; Chaudhari, Sachin Vasant; Agarkar, Balasaheb Shrirangao
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp349-355

Abstract

Gait recognition (GR) has emerged as a significant biometric identification technique, leveraging an individual's walking pattern for various applications such as surveillance, forensic analysis, and person identification. Despite its non-intrusive nature, GR systems face challenges due to their sensitivity to pose variations, limiting functionality in real-world scenarios where people exhibit diverse walking styles and body orientations. This review paper aims to comprehensively discuss GR systems, focusing on approaches and challenges in designing accurate and robust systems capable of handling bodily variations. GR's prominence spans across domains including surveillance, security, healthcare, and human-computer interaction, positioning it as a versatile biometric modality complementary to the traditional methods like fingerprint and face recognition. The review offers an in-depth analysis of GR systems, detailing silhouette-based, model-based, and deep-learning approaches. Silhouette-based methods capture gait information by analyzing the outline and locomotion of a person’s silhouette, while model-based approaches utilize skeletal models to describe gait patterns. The paper elucidates the challenges and limitations of GR systems, encompassing factors such as walking conditions, clothing, viewpoint, and environmental influences. Additionally, it explores potential future directions in GR research, highlighting the technology’s ongoing evolution and integration into diverse applications. As a valuable resource, this review serves researchers, practitioners, and policymakers by providing insights into the current state of GR systems and avenues for further research and development. It underscores the importance of addressing challenges to enhance GR’s accuracy and robustness, ensuring its continued relevance in biometric identification across various domains.
Deep Learning Approach for Segmenting Nuchal Translucency Region in Fetal Ultrasound Images for Detecting Down Syndrome using GoogLeNet and AlexNet Aher, Sandip Rajendra; Agarkar, Balasaheb Shrirangrao; Chaudhari, Sachin Vasant
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.729

Abstract

Down syndrome (DS) is a chromosomal disorder linked to intellectual impairment and developmental delays in babies. The primary prenatal indicator for detecting DS during the initial stages of gestation is the thickness of nuchal translucency (NT). This paper introduces a GoogLeNet model based on convolutional neural networks (CNN) for the semantic segmentation of the NT region from ultrasound fetal images, facilitating rapid and cost-effective diagnosis in the early stages of the gestational period. A transfer learning methodology with AlexNet is employed to train the NT regions for the detection of DS. The Inception module of GoogLeNet enables the model to simultaneously capture characteristics at various sizes of images. The capacity to extract both intricate and broad characteristics can improve the model’s performance in precisely identifying the NT area. This will function as an exceptional tool for physicians in screening of DS, enhancing the detection rate and providing a substantial opinion for early diagnosis. The proposed deep learning approach attained an accuracy of 96.18% and Jaccard index of 0.967 for NT region segmentation utilizing GoogLeNet. A confusion matrix was used to evaluate the image classification by AlexNet model's effectiveness, and the results showed an overall accuracy of 97.84%, ROC-AUC of 98.45%, recall of 99.64%, precision of 96.04%, and F1 score of 97.80%. The proposed deep learning method produced remarkable outcomes and can be applied to the identification of DS in medical field. This method identifies individuals at increased risk for this condition and enables termination in the early stages of pregnancy.
Co-Authors Agarkar, Balasaheb Shrirangao Agarkar, Balasaheb Shrirangrao Aher, Sandip Rajendra Labade, Rekha Mandlik, Sachin B.