Blar i TKD - Institutt for maskin, elektronikk og kjemi på emneord "Recurrent neural networks"

Viser treff 1-4 av 4

    • Activity Recognition and Prediction in Real Homes 

      Casagrande, Flavia Dias; Zouganeli, Evi (Nordic Artificial Intelligence Research and Development: Third Symposium of the Norwegian AI Society, NAIS 2019, Trondheim, Norway, May 27–28, 2019, Proceedings;, Chapter; Peer reviewed, 2019)
      In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, ...

    • Comparison of Probabilistic Models and Neural Networks on Prediction of Home Sensor Events 

      Casagrande, Flavia Dias; Tørresen, Jim; Zouganeli, Evi (International Joint Conference on Neural Networks (IJCNN); 2019 International Joint Conference on Neural Networks (IJCNN), Conference object, 2019-09-30)
      We present results and comparative analysis on the prediction of sensor events in a smart home environment with a limited number of binary sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced ...

    • Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults 

      Casagrande, Flavia Dias; Tørresen, Jim; Zouganeli, Evi (IEEE Access;Volume 7, Journal article; Peer reviewed, 2019-08-08)
      We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 ...

    • Sensor Event Prediction using Recurrent Neural Network in Smart Homes for Older Adults 

      Casagrande, Flavia Dias; Tørresen, Jim; Zouganeli, Evi (2018 International Conference on Intelligent Systems (IS);, Chapter; Chapter; Peer reviewed, 2018)
      We present preliminary results on sensor data prediction in a smart home environment with a limited number of binary sensors. The data has been collected from a real home with one resident over a period of 17 weeks. We ...