Detecting Chronic Diseases from Sleep-Wake Behaviour and Clinical Features

The file attached to this record is the author's final peer reviewed version.Many chronic diseases show evidence of correlations with sleep-wake behaviour, and there is an increasing interest in making use of such correlations for early warning systems. This research presents an approach towards early chronic disease detection by mining sleep-wake measurements using deep learning. Specifically, a Long-Short-Term-Memory network is applied on actigraph data enriched with clinical history of patients. Experiments and analysis are performed targeting detection at an early and advanced disease stage based on different clinical data features. The results show for disease detection an averaged accuracy of 0:62, 0:73, 0:81, 0:77 for hypertension, diabetes, sleep apnea and chronic kidney disease, respectively. Early detection performs with an averaged accuracy of 0:49 for sleep apnea and 0:56 for diabetes. Nevertheless, compared to existing work, our approach shows an improvement in performance and demonstrates that predicting chronic diseases from sleep-wake behavior is feasible, though further investigation will be needed for early prediction

Secondary effects of urban heat island mitigation measures on air quality

This study presents numerical simulations analysing the effect of urban heat island (UHI) mitigation measures on the chemical composition of the urban atmosphere. The mesoscale chemical transport model WRF-Chem is used to investigate the impact of urban greening and highly reflective surfaces on the concentrations of primary (CO, NO) as well as secondary pollutants (O$_{3}$) inside the urban canopy. In order to account for the sub-grid scale heterogeneity of urban areas, a multi-layer urban canopy model is coupled to WRF-Chem. Using this canopy model at its full extend requires the introduction of several urban land use classes in WRF-Chem. The urban area of Stuttgart serves as a test bed for the modelling of a case scenario of the 2003 European Heat Wave. The selected mitigation measures are able to reduce the urban temperature by about 1 K and the mean ozone concentration by 5–8%. Model results however document also negative secondary effects on urban air quality, which are closely related to a decrease of vertical mixing in the urban boundary layer. An increase of primary pollutants NO and CO by 5–25% can be observed. In addition, highly reflective surfaces can increase peak ozone concentration by up to 12% due to a high intensity of reflected shortwave radiation accelerating photochemical reactions

Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments

The file attached to this record is the author's final peer reviewed versionActivity Recognition (AR) is at the heart of any types of assistive living systems. One of the key challenges faced in AR is segmentation of the sensor events when inhabitant performs simple or composite activities of daily living (ADLs). In addition, each inhabitant may follow a particular ritual or a tradition in performing different ADLs and their patterns may change overtime. Many recent studies apply methods to segment and recognise generic ADLs performed in a composite manner. However, little has been explored in semantically distinguishing individual sensor events and directly passing it to the relevant ongoing/new atomic activities. This paper proposes to use the ontological model to capture generic knowledge of ADLs and methods which also takes inhabitant-specific preferences into considerations when segmenting sensor events. The system implementation was developed, deployed and evaluated against 84 use case scenarios. The result suggests that all sensor events were adequately segmented with 98% accuracy and the average classification time of 3971ms and 62183ms for single and composite ADL scenarios were recorded, respectively