Healthcare

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. Additionally, we compare the memory and battery consumption of our framework across the two mobile operating systems.

Circadian rhythms govern biological patterns that follow a 24-hour cycle. Dysfunctions in circadian rhythms can contribute to various …

Encouraging people to manage their health is essential in preventing chronic diseases like type 2 diabetes and heart disease. With …

Explainable deep-learning time series extrinsic regression methods based on counterfactual approaches and evolutionary algorithms. The goal is to generate recommendations for improving general health using time-series models (TCNs) for biological age estimation.

Development of a Health Asistant for asthma. Using 5 digital sensors and AI methods to passively assess track symptons and predict exacerbations in a 1-year-long study with 160 participants.

Longitudinal tracking of circadian rhythms of individuals using commercial devices in a 14-day study with 36 participants.

An open-source cross-platform, cross-language framework for machine-learning- and data collection applications across cloud, edge, mobile and wearable devices. Supports to run ML models directly in Python, even on Android and WearOS.

Development of a novel open source smart inhaler compatible with the three most common inhaler types (MDI, Turbuhaler and Diskus). Using passive sensors such as accelerometers to infer inhaler usage using machine learning.

In this paper we report on our implementation of a temporal convolutional network trained to detect Freezing of Gait on an FPGA. In …

Trained and implemented Temporal Convolutional Neural Networks (TCNs) for detecting Freezing of Gait. Deployed the neural networks on FPGAs using VitisAI, adhering to strict realtime requirements.