Adaptive Segmentation and Sequence Learning of human activities from skeleton data
作者：
Highlights：
• A novel method for segmentation and sequence learning of human activity sequences.
• Exploiting temporal accumulated motion energy of human actions in activity sequences.
• Comparison of the ASSL approach with different sequence learning methods.
摘要
•A novel method for segmentation and sequence learning of human activity sequences.•Exploiting temporal accumulated motion energy of human actions in activity sequences.•Comparison of the ASSL approach with different sequence learning methods.
论文关键词：Human activity recognition,Sequence learning,Long Short-Term Memory,Activity segmentation,Mean-shift clustering,Key pose
论文评审过程：Received 9 March 2020, Revised 14 June 2020, Accepted 1 August 2020, Available online 9 August 2020, Version of Record 13 August 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113836
