A knowledge-driven approach for activity recognition in smart homes based on activity profiling

2020 ◽  
Vol 107 ◽  
pp. 924-941 ◽  
Author(s):  
Majdi Rawashdeh ◽  
Mohammed GH. Al Zamil ◽  
Samer Samarah ◽  
M. Shamim Hossain ◽  
Ghulam Muhammad
Keyword(s):  
Activity Recognition ◽  
Smart Homes ◽  
Activity Profiling

A distributable event-oriented architecture for activity recognition in smart homes

2021 ◽  
Author(s):  
Cédric Demongivert ◽  
Kévin Bouchard ◽  
Sébastien Gaboury ◽  
Bruno Bouchard ◽  
Maxime Lussier ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Smart Homes

scholarly journals From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes

2017 ◽  
Vol 47 (3) ◽  
pp. 368-379 ◽  
Author(s):  
Joseph Rafferty ◽  
Chris D. Nugent ◽  
Jun Liu ◽  
Liming Chen
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Smart Homes ◽  
Intention Recognition

scholarly journals Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 879 ◽  
Author(s):  
Uwe Köckemann ◽  
Marjan Alirezaie ◽  
Jennifer Renoux ◽  
Nicolas Tsiftes ◽  
Mobyen Uddin Ahmed ◽  
...  
Keyword(s):  
Data Collection ◽  
Activity Recognition ◽  
Care Home ◽  
Open Data ◽  
Ground Truth ◽  
Smart Homes ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Home Setting

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

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