  The increasing number of edge devices with enhanced sensing capabilities,
such as smartphones, wearables, and IoT devices equipped with sensors, holds
the potential for innovative smart-edge applications in healthcare. These
devices generate vast amounts of multimodal data, enabling the implementation
of digital biomarkers which can be leveraged by machine learning solutions to
derive insights, predict health risks, and allow personalized interventions.
Training these models requires collecting data from edge devices and
aggregating it in the cloud. To validate and verify those models, it is
essential to utilize them in real-world scenarios and subject them to testing
using data from diverse cohorts. Since some models are too computationally
expensive to be run on edge devices directly, a collaborative framework between
the edge and cloud becomes necessary. In this paper, we present CLAID, an
open-source cross-platform middleware framework based on transparent computing
compatible with Android, iOS, WearOS, Linux, macOS, and Windows. CLAID enables
logical integration of devices running different operating systems into an
edge-cloud system, facilitating communication and offloading between them, with
bindings available in different programming languages. We provide Modules for
data collection from various sensors as well as for the deployment of
machine-learning models. Furthermore, we propose a novel methodology, "ML-Model
in the Loop" for verifying deployed machine learning models, which helps to
analyze problems that may occur during the migration of models from cloud to
edge devices. We verify our framework in three different experiments and
achieve 100% sampling coverage for data collection across different sensors as
well as an equal performance of a cough detection model deployed on both
Android and iOS devices. We evaluate the memory and battery consumption of our
framework.
