{"title":"使用迁移学习的人工智能驱动的可信任、可解释的跌倒检测系统","authors":"Aryan Nikul Patel , Ramalingam Murugan , Praveen Kumar Reddy Maddikunta , Gokul Yenduri , Rutvij H. Jhaveri , Yaodong Zhu , Thippa Reddy Gadekallu","doi":"10.1016/j.imavis.2024.105164","DOIUrl":null,"url":null,"abstract":"<div><p>Accidental falls pose a significant public health challenge, especially among vulnerable populations. To address this issue, comprehensive research on fall detection and rescue systems is essential. Vision-based technologies, with their promising potential, offer an effective means to detect falls. This research paper presents a cutting-edge fall detection methodology aimed at enhancing individual safety and well-being. The proposed methodology utilizes deep neural networks, leveraging their capabilities to drive advancements in fall detection. To overcome data limitations and computational efficiency concerns, this study employ transfer learning by fine-tuning pre-trained models on large-scale image datasets for fall detection. This approach significantly enhances model performance, enabling better generalization and accuracy, especially in real-time applications with constrained resources. Notably, the methodology achieved an impressive test accuracy of 98.15%. Additionally, the incorporation of Explainable Artificial Intelligence (XAI) techniques is used to ensure transparent and trustworthy decision-making in fall detection using deep learning models, especially in critical healthcare contexts for vulnerable individuals. XAI provides valuable insights into complex model architectures and parameters, enabling a deeper understanding of fall identification patterns. To evaluate the effectiveness of this approach, a rigorous experimentation was conducted using a diverse dataset containing real-world fall and non-fall scenarios. The results demonstrate substantial improvements in both accuracy and interpretability, confirming the superiority of this method over conventional fall detection approaches.</p></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-powered trustable and explainable fall detection system using transfer learning\",\"authors\":\"Aryan Nikul Patel , Ramalingam Murugan , Praveen Kumar Reddy Maddikunta , Gokul Yenduri , Rutvij H. Jhaveri , Yaodong Zhu , Thippa Reddy Gadekallu\",\"doi\":\"10.1016/j.imavis.2024.105164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accidental falls pose a significant public health challenge, especially among vulnerable populations. To address this issue, comprehensive research on fall detection and rescue systems is essential. Vision-based technologies, with their promising potential, offer an effective means to detect falls. This research paper presents a cutting-edge fall detection methodology aimed at enhancing individual safety and well-being. The proposed methodology utilizes deep neural networks, leveraging their capabilities to drive advancements in fall detection. To overcome data limitations and computational efficiency concerns, this study employ transfer learning by fine-tuning pre-trained models on large-scale image datasets for fall detection. This approach significantly enhances model performance, enabling better generalization and accuracy, especially in real-time applications with constrained resources. Notably, the methodology achieved an impressive test accuracy of 98.15%. Additionally, the incorporation of Explainable Artificial Intelligence (XAI) techniques is used to ensure transparent and trustworthy decision-making in fall detection using deep learning models, especially in critical healthcare contexts for vulnerable individuals. XAI provides valuable insights into complex model architectures and parameters, enabling a deeper understanding of fall identification patterns. To evaluate the effectiveness of this approach, a rigorous experimentation was conducted using a diverse dataset containing real-world fall and non-fall scenarios. The results demonstrate substantial improvements in both accuracy and interpretability, confirming the superiority of this method over conventional fall detection approaches.</p></div>\",\"PeriodicalId\":50374,\"journal\":{\"name\":\"Image and Vision Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Image and Vision Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0262885624002695\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885624002695","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
AI-powered trustable and explainable fall detection system using transfer learning
Accidental falls pose a significant public health challenge, especially among vulnerable populations. To address this issue, comprehensive research on fall detection and rescue systems is essential. Vision-based technologies, with their promising potential, offer an effective means to detect falls. This research paper presents a cutting-edge fall detection methodology aimed at enhancing individual safety and well-being. The proposed methodology utilizes deep neural networks, leveraging their capabilities to drive advancements in fall detection. To overcome data limitations and computational efficiency concerns, this study employ transfer learning by fine-tuning pre-trained models on large-scale image datasets for fall detection. This approach significantly enhances model performance, enabling better generalization and accuracy, especially in real-time applications with constrained resources. Notably, the methodology achieved an impressive test accuracy of 98.15%. Additionally, the incorporation of Explainable Artificial Intelligence (XAI) techniques is used to ensure transparent and trustworthy decision-making in fall detection using deep learning models, especially in critical healthcare contexts for vulnerable individuals. XAI provides valuable insights into complex model architectures and parameters, enabling a deeper understanding of fall identification patterns. To evaluate the effectiveness of this approach, a rigorous experimentation was conducted using a diverse dataset containing real-world fall and non-fall scenarios. The results demonstrate substantial improvements in both accuracy and interpretability, confirming the superiority of this method over conventional fall detection approaches.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.