Titlebook: Human Activity Recognition Challenge; Md Atiqur Rahman Ahad,Paula Lago,Sozo Inoue Book 2021 The Editor(s) (if applicable) and The Author(s

先鋒派

assent

打算

Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN,lize the advantages of multi-modal data, we propose a joint training CNN model that combines the acceleration and skeleton data together. Finally, the proposed CNN model achieves an average accuracy of 82.8% for macro-activity recognition on Cooking Activity Dataset.

ACTIN

Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activie recipe to refine the original micro-activity predictions. After the post-processing step using a Hidden Markov Model, we achieved the competition score of 95% on the training data with cross-validation.

一致性

Book 2021s) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia)..

松果

978-981-15-8271-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

bourgeois

高調(diào)

https://doi.org/10.1007/978-981-15-8269-1Activity Recognition; Cooking Challenge; Sensor; Computer Vision; Machine Learning; Deep Learning; Healthc

Hot-Flash

Nerve-Block