  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 mean 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.
