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.

scholarly journals A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6037 ◽  
Author(s):  
Damien Bouchabou ◽  
Sao Mai Nguyen ◽  
Christophe Lohr ◽  
Benoit LeDuc ◽  
Ioannis Kanellos
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Smart Homes ◽  
The Elderly ◽  
Human Activity Recognition ◽  
Challenges And Opportunities ◽  
The Cost ◽  
Home Assistance

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. However, new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges, and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field.

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 Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches

2019 ◽  
Vol 8 (6S4) ◽  
pp. 531-540
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Independent Living ◽  
Ambient Assisted Living ◽  
The Elderly ◽  
Human Activity Recognition ◽  
Living Environment ◽  
Learning Approaches

The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.

scholarly journals Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Mohammad Iqbal ◽  
Chandrawati Putri Wulandari ◽  
Wawan Yunanto ◽  
Ghaluh Indah Permata Sari
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Activity Patterns ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Sequential Patterns ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Human Activity Patterns

Discovering rare human activity patterns—from triggered motion sensors deliver peculiar information to notify people about hazard situations. This study aims to recognize rare human activities using mining non-zero-rare sequential patterns technique. In particular, this study mines the triggered motion sensor sequences to obtain non-zero-rare human activity patterns—the patterns which most occur in the motion sensor sequences and the occurrence numbers are less than the pre-defined occurrence threshold. This study proposes an algorithm to mine non-zero-rare pattern on human activity recognition called Mining Multi-class Non-Zero-Rare Sequential Patterns (MMRSP).  The experimental result showed that non-zero-rare human activity patterns succeed to capture the unusual activity. Furthermore, the MMRSP performed well according to the precision value of rare activities.

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
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