Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions i...Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.展开更多
Five-year survival rate for patients with all cancers combined, in China, is only 30.9%, which is much lower than those in developed countries. The three main reasons for the low cancer curative rates in China include...Five-year survival rate for patients with all cancers combined, in China, is only 30.9%, which is much lower than those in developed countries. The three main reasons for the low cancer curative rates in China include differences in the spectrum of cancer types, in early detection rates, and in the percentage of cancer patients receiving standardized treatment between China and developed countries.The most important mechanism for improving the curative rate is to improve early detection rates of major cancers in China using novel and affordable technologies that can be operated at home by the patients themselves.This attempt could be helpful in setting up a practical example for other developing countries with limited medical resources and a limited number of healthcare practitioners.展开更多
文摘Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.
基金supported by grants from the National Natural Science Foundation of China(No.81472386,No.81672872)the National High Technology Research and Development Program of China(863 Program)(No.2012AA02A501)+1 种基金the Science and Technology Planning Project of Guangdong Province,China(No.2014B020212017,No.20148050504004 and No.2015B050501005)the Provincial Natural Science Foundation of Guangdong,China(No.2016A030311011)
文摘Five-year survival rate for patients with all cancers combined, in China, is only 30.9%, which is much lower than those in developed countries. The three main reasons for the low cancer curative rates in China include differences in the spectrum of cancer types, in early detection rates, and in the percentage of cancer patients receiving standardized treatment between China and developed countries.The most important mechanism for improving the curative rate is to improve early detection rates of major cancers in China using novel and affordable technologies that can be operated at home by the patients themselves.This attempt could be helpful in setting up a practical example for other developing countries with limited medical resources and a limited number of healthcare practitioners.
