hello everyone I am setareh from queen's university kingston canada I present
our paper self-supervised human activity recognition by learning
to predict cross-dimensional motion this is a joint work with michael rainbow and ali etemad
human activity recognition is a popular field of research
because of its wide applications in different
areas such as health personalization of user experience and sports due to the pervasiveness of
smartphones and wearable devices massive amounts of wearable data have been collected
which as a result has created new research opportunities for activity recognition studies
although fully supervised techniques have shown robust performance in
activity recognition area there are some challenges that they might face first
these approaches are highly impacted by the amount of training data and require large sets
of "labeled" data as we mentioned collecting these large activity data is feasible however
annotating them is a problem since it is expensive and time consuming secondly such approaches learn
very task specific representations which may not be easily used for other similar tasks
to leverage this unlabeled data self-supervised or semi-supervised approaches can be useful
so in this work we propose our self-supervised solution for human activity recognition based
on cross-dimensional motion prediction using this approach as the pretext of the
self-supervised method results in learning generalized and informative motion representations
our approach consists of two phases first the pretext which is our cross-dimensional motion
prediction and second the downstream which is the human activity recognition task we create fixed
size windows from the acceleration data containing three axes x y and z then uh we break each window
into two segments by assuming uh the last time steps of axis z as the output segment
and the remaining values of axis z along with the values so in axes x and y as the input segment
we implement a network containing convolution and fully connected layers to learn to predict the
output segment from the input one in other words our model leverages the past and present motion
in x and y dimensions as well as past values of the z-axis to predict values in the z dimension
as a result the model learns to extract strong motion representations from the acceleration data
after we're done uh with the pre-training we freeze the convolution part of the network
and pass it to the next phase in which the frozen network followed by a mlp classifier
are used for the human activity recognition task
we evaluate the robustness of the proposed model with comparing the performance of the model with
other self-supervised methods such as multi-task and mask reconstruction-based approaches
for human activity recognition and three activity datasets the proposed method outperforms all other
approaches uh moreover we compare the performance of our self-supervised model to a fully supervised
one with the same architecture uh when different amounts of labeled data are used it can be observed
that when only small amounts of labeled data like only one percent are available the self-supervised
model clearly outperforms the fully-supervised one it shows that the proposed method can learn
to extract effective representations even with the small amounts of labeled data
so uh put it all together we propose cross-dimensional motion prediction as
a novel deep self-supervised solution for learning representations from unlabeled accelerometer data
we demonstrate that our method outperforms existing self-supervised methods in human
activity recognition for UCI HAR motionSense and HAPT datasets
uh we also show that in cases where really small amounts of labeled data are available
um our self-supervised model clearly outperforms the fully supervised approaches
uh that that are using the same architecture thank you everyone