  A fall is an abnormal activity that occurs rarely, so it is hard to collect
real data for falls. It is, therefore, difficult to use supervised learning
methods to automatically detect falls. Another challenge in using machine
learning methods to automatically detect falls is the choice of engineered
features. In this paper, we propose to use an ensemble of autoencoders to
extract features from different channels of wearable sensor data trained only
on normal activities. We show that the traditional approach of choosing a
threshold as the maximum of the reconstruction error on the training normal
data is not the right way to identify unseen falls. We propose two methods for
automatic tightening of reconstruction error from only the normal activities
for better identification of unseen falls. We present our results on two
activity recognition datasets and show the efficacy of our proposed method
against traditional autoencoder models and two standard one-class
classification methods.
