Blar i TKD - Institutt for maskin, elektronikk og kjemi på forfatter "Casagrande, Flavia Dias"

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    • Action Recognition in Real Homes using Low Resolution Depth Video Data 

      Casagrande, Flavia Dias; Nedrejord, Oda Olsen; Lee, Wonho; Zouganeli, Evi (Annual IEEE Symposium on Computer-Based Medical Systems;2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Conference object, 2019-08-05)
      We report work in progress from interdisciplinary research on Assisted Living Technology in smart homes for older adults with mild cognitive impairments or dementia. We present our field trial, the set-up for collecting ...

    • 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 ...

    • Involving older adults in technology research and development discussions through dialogue cafés 

      Lund, Anne; Holthe, Torhild; Halvorsrud, Liv Torill; Karterud, Dag; Flakke-Johannessen, Adele; Lovett, Hilde; Thorstensen, Erik; Casagrande, Flavia Dias; Zouganeli, Evi; Norvoll, Reidun; Forsberg, Ellen-Marie (Peer reviewed; Journal article, 2021)
      Citizen involvement is important for ensuring the relevance and quality of many research and innovation efforts. Literature shows that inadequate citizen involvement poses an obstacle during the research, development, and ...

    • Occupancy and daily activity event modelling in smart homes for older adults with mild cognitiveiImpairment or dementia 

      Casagrande, Flavia Dias; Zouganeli, Evi (Linköping Electronic Conference Proceedings;153, Chapter; Peer reviewed, 2018)
      In this paper we present event anticipation and prediction of sensor data in a smart home environment with a limited number of sensors. Data is collected from a real home with one resident. We apply two state-of-the-art ...

    • 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 ...

    • Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes 

      Simonsson, Simon Frederick; Casagrande, Flavia Dias; Zouganeli, Evi (IEEE Access;Volume: 11, Peer reviewed; Journal article, 2023-01-23)
      This work presents a novel method for motion sensor placement within smart homes. Using recordings from 3D depth cameras within six real homes, clusters are created with the resident’s tracked location. The resulting ...