  In healthcare applications, there is a growing need to develop machine
learning models that use data from a single source, such as that from a wrist
wearable device, to monitor physical activities, assess health risks, and
provide immediate health recommendations or interventions. However, the
limitation of using single-source data often compromises the model's accuracy,
as it fails to capture the full scope of human activities. While a more
comprehensive dataset can be gathered in a lab setting using multiple sensors
attached to various body parts, this approach is not practical for everyday use
due to the impracticality of wearing multiple sensors. To address this
challenge, we introduce a transfer learning framework that optimizes machine
learning models for everyday applications by leveraging multi-source data
collected in a laboratory setting. We introduce a novel metric to leverage the
inherent relationship between these multiple data sources, as they are all
paired to capture aspects of the same physical activity. Through numerical
experiments, our framework outperforms existing methods in classification
accuracy and robustness to noise, offering a promising avenue for the
enhancement of daily activity monitoring.
