Activity Recognition as a Service for Smart Home: Ambient Assisted Living Application via Sensing Home

2017 ◽  
Author(s):  
Xiaohu Fan ◽  
Qubo Xie ◽  
Xuebin Li ◽  
Hao Huang ◽  
Jian Wang ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Smart Home ◽  
Ambient Assisted Living

scholarly journals Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 768
Author(s):  
Caetano Mazzoni Ranieri ◽  
Scott MacLeod ◽  
Mauro Dragone ◽  
Patricia Amancio Vargas ◽  
Roseli Aparecida Francelin Romero 
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Daily Living ◽  
Smart Home ◽  
Inertial Sensors ◽  
Ambient Assisted Living ◽  
Sensor Data ◽  
University Of Texas ◽  
Demographic Projections ◽  
The University

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.

scholarly journals A non-intrusive context-aware system for ambient assisted living in smart home

2013 ◽  
Vol 3 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Willy Allègre ◽  
Thomas Burger ◽  
Jean-Yves Antoine ◽  
Pascal Berruet ◽  
Jean-Paul Departe
Keyword(s):  
Assisted Living ◽  
Smart Home ◽  
Ambient Assisted Living ◽  
Context Aware ◽  
Context Aware System

Activity Recognition for Ambient Assisted Living Using Off-the-shelf Motion Sensing Input Devices

2019 ◽  
Author(s):  
Elena Cebanov ◽  
Ciprian Dobre ◽  
Alexandru Gradinaru ◽  
Radu-Ioan Ciobanu ◽  
Valeriu-Daniel Stanciu
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Motion Sensing ◽  
Input Devices

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.

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