1 research outputs found
Lightweight Transformer in Federated Setting for Human Activity Recognition
Human activity recognition (HAR) is a machine learning task with applications
in many domains including health care, but it has proven a challenging research
problem. In health care, it is used mainly as an assistive technology for elder
care, often used together with other related technologies such as the Internet
of Things (IoT) because HAR can be achieved with the help of IoT devices such
as smartphones, wearables, environmental and on-body sensors. Deep neural
network techniques like convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) have been used for HAR, both in centralized and
federated settings. However, these techniques have certain limitations: RNNs
cannot be easily parallelized, CNNs have the limitation of sequence length, and
both are computationally expensive. Moreover, the centralized approach has
privacy concerns when facing sensitive applications such as healthcare. In this
paper, to address some of the existing challenges facing HAR, we present a
novel one-patch transformer based on inertial sensors that can combine the
advantages of RNNs and CNNs without their major limitations. We designed a
testbed to collect real-time human activity data and used the data to train and
test the proposed transformer-based HAR classifier. We also propose TransFed: a
federated learning-based HAR classifier using the proposed transformer to
address privacy concerns. The experimental results showed that the proposed
solution outperformed the state-of-the-art HAR classifiers based on CNNs and
RNNs, in both federated and centralized settings. Moreover, the proposed HAR
classifier is computationally inexpensive as it uses much fewer parameters than
existing CNN/RNN-based classifiers.Comment: An updated version of this paper is coming soo