Mohammad Tavakkoli, Ehsan Nazerfard, Maryam Amirmazlaghani
{"title":"利用卷积神经网络进行小波域人类活动识别","authors":"Mohammad Tavakkoli, Ehsan Nazerfard, Maryam Amirmazlaghani","doi":"10.3233/ais-230174","DOIUrl":null,"url":null,"abstract":"Human activity recognition (HAR) is a crucial area of research in human-computer interaction. Despite previous efforts in this field, there is still a need for more accurate and robust methods that can handle time-series data from different sensors. In this study, we propose a novel method that generates an image using wavelet transform to extract time-frequency features of the recorded signal. Our method employs convolutional neural networks (CNNs) for feature extraction and activity recognition, and a new loss function that produces denser representations for samples, improving the model’s generalization on unseen samples. To evaluate the effectiveness of our proposed method, we conducted experiments on multiple publicly available data sets. Our results demonstrate that our method outperforms previous methods in terms of activity classification accuracy. Specifically, our method achieves higher accuracy rates and demonstrates improved robustness in real-world settings. Overall, our proposed method addresses the research gap of accurate and robust activity recognition from time-series data recorded from different sensors. Our findings have the potential to improve the accuracy and robustness of human activity recognition systems in real-world applications.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"12 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wavelet-domain human activity recognition utilizing convolutional neural networks\",\"authors\":\"Mohammad Tavakkoli, Ehsan Nazerfard, Maryam Amirmazlaghani\",\"doi\":\"10.3233/ais-230174\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Human activity recognition (HAR) is a crucial area of research in human-computer interaction. Despite previous efforts in this field, there is still a need for more accurate and robust methods that can handle time-series data from different sensors. In this study, we propose a novel method that generates an image using wavelet transform to extract time-frequency features of the recorded signal. Our method employs convolutional neural networks (CNNs) for feature extraction and activity recognition, and a new loss function that produces denser representations for samples, improving the model’s generalization on unseen samples. To evaluate the effectiveness of our proposed method, we conducted experiments on multiple publicly available data sets. Our results demonstrate that our method outperforms previous methods in terms of activity classification accuracy. Specifically, our method achieves higher accuracy rates and demonstrates improved robustness in real-world settings. Overall, our proposed method addresses the research gap of accurate and robust activity recognition from time-series data recorded from different sensors. Our findings have the potential to improve the accuracy and robustness of human activity recognition systems in real-world applications.\",\"PeriodicalId\":49316,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Smart Environments\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Smart Environments\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/ais-230174\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Smart Environments","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/ais-230174","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Wavelet-domain human activity recognition utilizing convolutional neural networks
Human activity recognition (HAR) is a crucial area of research in human-computer interaction. Despite previous efforts in this field, there is still a need for more accurate and robust methods that can handle time-series data from different sensors. In this study, we propose a novel method that generates an image using wavelet transform to extract time-frequency features of the recorded signal. Our method employs convolutional neural networks (CNNs) for feature extraction and activity recognition, and a new loss function that produces denser representations for samples, improving the model’s generalization on unseen samples. To evaluate the effectiveness of our proposed method, we conducted experiments on multiple publicly available data sets. Our results demonstrate that our method outperforms previous methods in terms of activity classification accuracy. Specifically, our method achieves higher accuracy rates and demonstrates improved robustness in real-world settings. Overall, our proposed method addresses the research gap of accurate and robust activity recognition from time-series data recorded from different sensors. Our findings have the potential to improve the accuracy and robustness of human activity recognition systems in real-world applications.
期刊介绍:
The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively. The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.