![]() Domnic, Deep ensemble network using distance maps and body part features for skeleton based action recognition, Pattern Recognit. Escobedo Cardenas E.J., Chavez G.C., Multimodal hand gesture recognition combining temporal and pose information based on cnn descriptors and histogram of cumulative magnitudes, J.Plötz, Deep, convolutional, and recurrent models for human activity recognition using wearables, in: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016, pp. Chen K., Zhang D., Yao L., Guo B., Yu Z., Liu Y., Deep learning for sensor-based human activity recognition: overview, challenges and opportunities, 2020, arXiv preprint arXiv:2001.07416.smart Approaches for Human Action Recognition. del R., Roggen D., The opportunity challenge: A benchmark database for on-body sensor-based activity recognition, Pattern Recognit. Chavarriaga R., Sagha H., Calatroni A., Digumarti S.T., Tröster G., Millán J.Lu J., Zheng X., Sheng M., Jin J., Yu S., Efficient human activity recognition using a single wearable sensor, IEEE Internet Things J.Ding W., Jing X., Yan Z., Yang L.T., A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion, Inf.Deng X., Jiang Y., Yang L.T., Lin M., Yi L., Wang M., Data fusion based coverage optimization in heterogeneous sensor networks: A survey, Inf.On the other hand, experiments of applying our model to two other tasks show that our model effectively supports other recognition tasks related to human activity and improves performance on the datasets of these tasks. Experimental results on fourteen datasets demonstrate that the proposed approach significantly outperforms other state-of-the-art methods. ![]() What is more, the knowledge learned through our approach can be seen as a priori applicable to improve the performance for other general reasoning tasks. This approach learns not only sample features and sample distribution characteristics via meta-learning-based graph prototypical model, but also the embeddings derived from priority attention mechanism that mines and utilizes relations between sample features and sample distribution characteristics. To address these issues, we propose a meta-learning-based graph prototypical model with priority attention mechanism for sensor-based human activity recognition. Although many successful methods have been proposed, there are three challenges to overcome: (1) deep model’s performance overly depends on the data size (2) deep model cannot explicitly capture abundant sample distribution characteristics (3) deep model cannot jointly consider sample features, sample distribution characteristics, and the relationship between the two. Sensor-based human activity recognition is a hotspot and starts to employ deep learning approaches to supersede traditional shallow learning that rely on hand-crafted features. These sensors are used not only to collect data but, more importantly, to assist in tracking and analyzing the daily human activities. ![]() With the rapid growth of the Internet of Things (IoT), smart systems and applications are equipped with an increasing number of wearable sensors and mobile devices.
0 Comments
Leave a Reply. |