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 health problems, such as sleep disorders. Current circadian rhythm assessment methods, often invasive or subjective, limit circadian rhythm monitoring to laboratories. Hence, this study aims to investigate scalable consumer-centric wearables for circadian rhythm monitoring outside traditional laboratories. In a two-week longitudinal study conducted in real-world settings, 36 participants wore an Actigraph, a smartwatch, and a core body temperature sensor to collect activity, temperature, and heart rate data. We evaluated circadian rhythms calculated from commercial wearables by comparing them with circadian rhythm reference measures, i.e., Actigraph activities and chronotype questionnaire scores. The circadian rhythm metric acrophases, determined from commercial wearables using activity, heart rate, and temperature data, significantly correlated with the acrophase derived from Actigraph activities (r=0.96, r=0.87, r=0.79; all p<0.001) and chronotype questionnaire (r=-0.66, r=-0.73, r=-0.61; all p<0.001). The acrophases obtained concurrently from consumer sensors significantly predicted the chronotype (R2=0.64; p<0.001). Our study validates commercial sensors for circadian rhythm assessment, highlighting their potential to support maintaining healthy rhythms and provide scalable and timely health monitoring in real-life scenarios.

Encouraging people to manage their health is essential in preventing chronic diseases like type 2 diabetes and heart disease. With mobile technologies such as health apps and fitness trackers, monitoring health has become more accessible and affordable compared to doctoral visits and traditional health check-ups. These technologies are, however, mainly used for disease management rather than prevention and there is no common understanding of how they can best be used for preventive purposes. To this end, we introduce the Bitemporal Lens Model a comprehensive method to use mobile technologies for disease prevention. We explain the structure and usefulness of the Bitemporal Lens Model, discuss its advantages and limitations, and present potential use cases.

A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objects are present, where they are, what their spatial extend is, and how they can be reached, i.e., information about free space is also crucial. Semantic maps are a powerful instrument providing such information. However, applying semantic segmentation and building 3D maps with high spatial resolution is challenging given limited resources on mobile robots. In this paper, we incorporate semantic information into efficient occupancy normal distribution transform (NDT) maps to enable real-time semantic mapping on mobile robots. On the publicly available dataset Hypersim, we show that, due to their sub-voxel accuracy, semantic NDT maps are superior to other approaches. We compare them to the recent state-of-the-art approach based on voxels and semantic Bayesian spatial kernel inference~(S-BKI) and to an optimized version of it derived in this paper. The proposed semantic NDT maps can represent semantics to the same level of detail, while mapping is 2.7 to 17.5 times faster. For the same grid resolution, they perform significantly better, while mapping is up to more than 5 times faster. Finally, we prove the real-world applicability of semantic NDT maps with qualitative results in a domestic application.

In this paper we report on our implementation of a temporal convolutional network trained to detect Freezing of Gait on an FPGA. In order to be able to compare our results with state of the art solutions we used the well-known open dataset Daphnet. Our most important findings are even though we used a tool to map the trained model to the FPGA we can detect FoG in less than a millisecond which will give us sufficient time to trigger cueing and by that prevent the patient from falling. In addition, the average sensitivity achieved by our implementation is comparable to solutions running on high end devices.