Analytical Review on Ontological Human Activity Recognition Approaches

2017 ◽  
Vol 13 (2) ◽  
pp. 58-78 ◽  
Author(s):  
Samaneh Zolfaghari ◽  
Mohammad Reza Keyvanpour ◽  
Raziyeh Zall
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Pervasive Computing ◽  
Daily Living ◽  
Smart Homes ◽  
Human Activity Recognition ◽  
Computing Technology ◽  
Monitoring Tool ◽  
Vast Number ◽  
Analytical Review

New advancements in pervasive computing technology have turned smart homes into a daily living monitoring tool increasingly used for elderly. Recently, using knowledge driven approaches such as ontology to introduce semantic smart homes has received attention due to their flexibility, reasoning and knowledge representation. Due to the vast number of ontological human activity recognition methods, the proposed ontological human activity recognition framework can be effective in analyzing and evaluating different methods in different applications and dealing with various challenges. Also, due to numerous challenges involved in different aspects of ontology-based human activity recognition in smart homes, this paper offers a classification for challenges in human activity recognition in ontology based systems. Then the proposed ontological human activity recognition framework is evaluated based on the proposed classification and ontology-based techniques which are thought to solve some of the challenges are examined and analyzed.

scholarly journals UCAmI Cup. Analyzing the UJA Human Activity Recognition Dataset of Activities of Daily Living

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1267 ◽  
Author(s):  
Macarena Espinilla ◽  
Javier Medina ◽  
Chris Nugent
Keyword(s):  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Human Activity Recognition ◽  
Proximity Data ◽  
Location Data ◽  
Smart Watch ◽  
Real World Applications ◽  
Tools And Techniques

Many real-world applications, which are focused on addressing the needs of a human, require information pertaining to the activities being performed. The UCAmI Cup is an event held within the context of the International Conference on Ubiquitous Computing and Ambient Intelligence, where delegates are given the opportunity to use their tools and techniques to analyse a previously unseen human activity recognition dataset and to compare their results with others working in the same domain. In this paper, the human activity recognition dataset used relates to activities of daily living generated in the UJAmI Smart Lab, University of Jaén. The dataset chosen for the first edition of the UCAmI Cup represents 246 activities performed over a period of ten days carried out by a single inhabitant. The dataset includes four data sources: (i) event streams from 30 binary sensors, (ii) intelligent floor location data, (iii) proximity data between a smart watch worn by the inhabitant and 15 Bluetooth Low Energy beacons and (iv) acceleration of the smart watch. In this first edition of the UCAmI Cup, 26 participants from 10 different countries contacted the organizers to obtain the dataset.‬‬‬‬‬

scholarly journals DCR: A new distributed model for human activity recognition in smart homes

2020 ◽  
Vol 140 ◽  
pp. 112849
Author(s):  
Amina Jarraya ◽  
Amel Bouzeghoub ◽  
Amel Borgi ◽  
Khedija Arour
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Smart Homes ◽  
Human Activity Recognition ◽  
Distributed Model

scholarly journals A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4474 ◽  
Author(s):  
Du ◽  
Lim ◽  
Tan
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Power Saving ◽  
Smart Homes ◽  
The Elderly ◽  
Human Mind ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Home Based ◽  
Passive Rfid

Smart Homes are generally considered the final solution for living problem, especially for the health care of the elderly and disabled, power saving, etc. Human activity recognition in smart homes is the key to achieving home automation, which enables the smart services to automatically run according to the human mind. Recent research has made a lot of progress in this field; however, most of them can only recognize default activities, which is probably not needed by smart homes services. In addition, low scalability makes such research infeasible to be used outside the laboratory. In this study, we unwrap this issue and propose a novel framework to not only recognize human activity but also predict it. The framework contains three stages: recognition after the activity, recognition in progress, and activity prediction in advance. Furthermore, using passive RFID tags, the hardware cost of our framework is sufficiently low to popularize the framework. In addition, the experimental result demonstrates that our framework can realize good performance in both activity recognition and prediction with high scalability.

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