A Real-Time Living Activity Recognition System Using Off-the-Shelf Sensors on a Mobile Phone

Author(s):  
Kazushige Ouchi ◽  
Miwako Doi
Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.


2015 ◽  
Vol 22 (11) ◽  
pp. 2715-2722 ◽  
Author(s):  
Dulal Acharjee ◽  
Amitava Mukherjee ◽  
J. K. Mandal ◽  
Nandini Mukherjee

2020 ◽  
pp. 1-1
Author(s):  
Leixin Shi ◽  
Hongji Xu ◽  
Wei Ji ◽  
Beibei Zhang ◽  
Xiaojie Sun ◽  
...  

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