Abstract
This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.
Highlights
Département D’informatique et de Mathématique, Université du Québec à Chicoutimi, Departement of Information Systems and Quantitative Methods in Management, École de Gestion, Abstract: This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare
We have reviewed activity recognition methods for healthcare in offline, centralized and distributed cases
When offline activity recognition acts as a sandbox for researcher to compare the performance of different machine learning algorithm and sensor types, real-time centralized methods allow real-life implementation for healthcare applications, but they come with a set of new problems such as efficient communication, online training and classification, real-time activity labeling, and concept drift detection
Summary
Département D’informatique et de Mathématique, Université du Québec à Chicoutimi, Departement of Information Systems and Quantitative Methods in Management, École de Gestion, Abstract: This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise. The initial vision was to increase convenience and comfort for any resident, but soon enough, researchers understood the potential of an environment filled with sensors to remotely monitor patients, and made use of these wireless sensor networks to assist them. The motivation for this review comes from the transition we are reaching in our research work in the use of ambient
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
