  Fall detection (FD) systems are important assistive technologies for
healthcare that can detect emergency fall events and alert caregivers. However,
it is not easy to obtain large-scale annotated fall events with various
specifications of sensors or sensor positions during the implementation of
accurate FD systems. Moreover, the knowledge obtained through machine learning
has been restricted to tasks in the same domain. The mismatch between different
domains might hinder the performance of FD systems. Cross-domain knowledge
transfer is very beneficial for machine-learning-based FD systems to train a
reliable FD model with well-labeled data in new environments. In this study, we
propose domain-adaptive fall detection (DAFD) using deep adversarial training
(DAT) to tackle cross-domain problems, such as cross-position and
cross-configuration. The proposed DAFD can transfer knowledge from the source
domain to the target domain by minimizing the domain discrepancy to avoid
mismatch problems. The experimental results show that the average F1-score
improvement when using DAFD ranges from 1.5% to 7% in the cross-position
scenario, and from 3.5% to 12% in the cross-configuration scenario, compared to
using the conventional FD model without domain adaptation training. The results
demonstrate that the proposed DAFD successfully helps to deal with cross-domain
problems and to achieve better detection performance.
