{"title":"基于轻量级卷积神经网络的老年人活动分类","authors":"Hanzhang Ding, Wenzhang Zhu","doi":"10.1145/3581807.3581834","DOIUrl":null,"url":null,"abstract":"Accurate implementation of action classification for the elderly on lightweight convolutional neural networks benefits resource-limited embedded and mobile devices in the healthcare industry. The study proposes a lightweight convolutional neural network model called mD-MobileNet. The micro-Doppler feature spectrograms of 106 elderly people were studied as a dataset. Transfer learning methods were used to train the proposed model, and three lightweight convolutional neural networks (MobileNetV3-Small, ShuffleNetV2, and EfficientNet-B0) were compared using the same training method. All of these models were able to correctly classify various actions. By comparison, mD-MobileNet gave the best classification results. mD-MobileNet’s Top-1 Accuracy reached 96.1% while Marco F1 was 96.30. By comparing the results with Grad-CAM’s visualization and analyzing them in conjunction with its network structure features, it was determined that mD-MobileNet has the best local perception with the least number of model parameters and the highest accuracy rate compared to other models.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"67 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Activity classification of the elderly based on lightweight convolutional neural networks\",\"authors\":\"Hanzhang Ding, Wenzhang Zhu\",\"doi\":\"10.1145/3581807.3581834\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate implementation of action classification for the elderly on lightweight convolutional neural networks benefits resource-limited embedded and mobile devices in the healthcare industry. The study proposes a lightweight convolutional neural network model called mD-MobileNet. The micro-Doppler feature spectrograms of 106 elderly people were studied as a dataset. Transfer learning methods were used to train the proposed model, and three lightweight convolutional neural networks (MobileNetV3-Small, ShuffleNetV2, and EfficientNet-B0) were compared using the same training method. All of these models were able to correctly classify various actions. By comparison, mD-MobileNet gave the best classification results. mD-MobileNet’s Top-1 Accuracy reached 96.1% while Marco F1 was 96.30. By comparing the results with Grad-CAM’s visualization and analyzing them in conjunction with its network structure features, it was determined that mD-MobileNet has the best local perception with the least number of model parameters and the highest accuracy rate compared to other models.\",\"PeriodicalId\":292813,\"journal\":{\"name\":\"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition\",\"volume\":\"67 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3581807.3581834\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3581807.3581834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Activity classification of the elderly based on lightweight convolutional neural networks
Accurate implementation of action classification for the elderly on lightweight convolutional neural networks benefits resource-limited embedded and mobile devices in the healthcare industry. The study proposes a lightweight convolutional neural network model called mD-MobileNet. The micro-Doppler feature spectrograms of 106 elderly people were studied as a dataset. Transfer learning methods were used to train the proposed model, and three lightweight convolutional neural networks (MobileNetV3-Small, ShuffleNetV2, and EfficientNet-B0) were compared using the same training method. All of these models were able to correctly classify various actions. By comparison, mD-MobileNet gave the best classification results. mD-MobileNet’s Top-1 Accuracy reached 96.1% while Marco F1 was 96.30. By comparing the results with Grad-CAM’s visualization and analyzing them in conjunction with its network structure features, it was determined that mD-MobileNet has the best local perception with the least number of model parameters and the highest accuracy rate compared to other models.