  Human Activity Recognition (HAR) on mobile devices has shown to be achievable
with lightweight neural models learned from data generated by the user's
inertial measurement units (IMUs). Most approaches for instanced-based HAR have
used Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), or a
combination of the two to achieve state-of-the-art results with real-time
performances. Recently, the Transformers architecture in the language
processing domain and then in the vision domain has pushed further the
state-of-the-art over classical architectures. However, such Transformers
architecture is heavyweight in computing resources, which is not well suited
for embedded applications of HAR that can be found in the pervasive computing
domain. In this study, we present Human Activity Recognition Transformer
(HART), a lightweight, sensor-wise transformer architecture that has been
specifically adapted to the domain of the IMUs embedded on mobile devices. Our
experiments on HAR tasks with several publicly available datasets show that
HART uses fewer FLoating-point Operations Per Second (FLOPS) and parameters
while outperforming current state-of-the-art results. Furthermore, we present
evaluations across various architectures on their performances in heterogeneous
environments and show that our models can better generalize on different
sensing devices or on-body positions.
