Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

被引:41
作者
Diraco, Giovanni [1 ]
Rescio, Gabriele [1 ]
Siciliano, Pietro [1 ]
Leone, Alessandro [1 ]
机构
[1] Natl Res Council Italy, Inst Microelect & Microsyst, I-73100 Lecce, Italy
关键词
review; human action recognition; smart living; multimodality; real-time processing; interoperability; resource-constrained processing; sensing technology; machine learning; deep learning; signal processing; smart home; smart environment; smart city; smart community; ambient assisted living; MACHINE; NETWORK; SENSORS; CONTEXT; MODEL;
D O I
10.3390/s23115281
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.
引用
收藏
页数:33
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