Ensemble residual network-based gender and activity recognition method with signals

2020 ◽  
Vol 76 (3) ◽  
pp. 2119-2138 ◽  
Author(s):  
Turker Tuncer ◽  
Fatih Ertam ◽  
Sengul Dogan ◽  
Emrah Aydemir ◽  
Paweł Pławiak
2016 ◽  
Vol 24 (3) ◽  
pp. 512-521 ◽  
Author(s):  
Kazuya Murao ◽  
Tsutomu Terada

2010 ◽  
Vol 20 (05) ◽  
pp. 355-364 ◽  
Author(s):  
JOSE ANTONIO IGLESIAS ◽  
PLAMEN ANGELOV ◽  
AGAPITO LEDEZMA ◽  
ARACELI SANCHIS

Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.


2021 ◽  
Vol 693 (1) ◽  
pp. 012041
Author(s):  
Jingxuan Zhang ◽  
Shaojie Qiao ◽  
Zhiyu Lin ◽  
Yang Zhou

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 16217-16225 ◽  
Author(s):  
Hanchuan Xu ◽  
Yuxin Pan ◽  
Jingxuan Li ◽  
Lanshun Nie ◽  
Xiaofei Xu

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