  Human activity recognition (HAR) is a machine learning task with important
applications in healthcare especially in the context of home care of patients
and older adults. HAR is often based on data collected from smart sensors,
particularly smart home IoT devices such as smartphones, wearables and other
body sensors. Deep learning 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, in home
healthcare applications the centralized approach can raise serious privacy
concerns since the sensors used by a HAR classifier collect a lot of highly
personal and sensitive data about people in the home. In this paper, to address
some of such challenges facing HAR, we propose a novel lightweight (one-patch)
transformer, which can combine the advantages of RNNs and CNNs without their
major limitations, and also TransFed, a more privacy-friendly, federated
learning-based HAR classifier using our proposed lightweight transformer. We
designed a testbed to construct a new HAR dataset from five recruited human
participants, and used the new dataset to evaluate the performance of the
proposed HAR classifier in both federated and centralized settings.
Additionally, we use another public dataset to evaluate the performance of the
proposed HAR classifier in centralized setting to compare it with existing HAR
classifiers. The experimental results showed that our proposed new solution
outperformed state-of-the-art HAR classifiers based on CNNs and RNNs, whiling
being more computationally efficient.
