Abstract
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, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low resolution depth video data from seven apartments, and classify four activities – no movement, standing up, sitting down, and TV interaction – by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.
Financed by the Norwegian Research Council under the SAMANSVAR programme (247620/O70).
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Acknowledgement
The authors would like to thank the residents and the housekeepers at the seniors’ care unit Skøyen Omsorg+; Torhild Holthe and Dr. Anne Lund (OsloMet) for recruiting participants for the trial and communicating with the residents throughout the trial; Dejan Krunić and Øyvind Width (Sensio AS) for installations of the sensors; Oda Olsen Nedrejord and Wonho Lee (OsloMet) for contributions to the video data work; Prof. Jim Tørresen (University of Oslo) for valuable inputs; and the rest of the participants of the Assisted Living Project for a fruitful interdisciplinary collaboration.
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Casagrande, F.D., Zouganeli, E. (2019). Activity Recognition and Prediction in Real Homes. In: Bach, K., Ruocco, M. (eds) Nordic Artificial Intelligence Research and Development. NAIS 2019. Communications in Computer and Information Science, vol 1056. Springer, Cham. https://doi.org/10.1007/978-3-030-35664-4_2
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