8 research outputs found
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour
Health and fitness wearable technology has recently advanced, making it
easier for an individual to monitor their behaviours. Previously self generated
data interacts with the user to motivate positive behaviour change, but issues
arise when relating this to long term mention of wearable devices. Previous
studies within this area are discussed. We also consider a new approach where
data is used to support instead of motivate, through monitoring and logging to
encourage reflection. Based on issues highlighted, we then make recommendations
on the direction in which future work could be most beneficial
Deep transformation models for functional outcome prediction after acute ischemic stroke
In many medical applications, interpretable models with high prediction
performance are sought. Often, those models are required to handle
semi-structured data like tabular and image data. We show how to apply deep
transformation models (DTMs) for distributional regression which fulfill these
requirements. DTMs allow the data analyst to specify (deep) neural networks for
different input modalities making them applicable to various research
questions. Like statistical models, DTMs can provide interpretable effect
estimates while achieving the state-of-the-art prediction performance of deep
neural networks. In addition, the construction of ensembles of DTMs that retain
model structure and interpretability allows quantifying epistemic and aleatoric
uncertainty. In this study, we compare several DTMs, including
baseline-adjusted models, trained on a semi-structured data set of 407 stroke
patients with the aim to predict ordinal functional outcome three months after
stroke. We follow statistical principles of model-building to achieve an
adequate trade-off between interpretability and flexibility while assessing the
relative importance of the involved data modalities. We evaluate the models for
an ordinal and dichotomized version of the outcome as used in clinical
practice. We show that both, tabular clinical and brain imaging data, are
useful for functional outcome prediction, while models based on tabular data
only outperform those based on imaging data only. There is no substantial
evidence for improved prediction when combining both data modalities. Overall,
we highlight that DTMs provide a powerful, interpretable approach to analyzing
semi-structured data and that they have the potential to support clinical
decision making.Comment: Preprint under revie
The potential of wearable technology for monitoring social interactions based on interpersonal synchrony
Sensing data from wearables have been extensively evaluated for fitness tracking, health monitoring or rehabilitation of individuals. However, we believe that wearable sensing can go beyond the individual and offer insights into social dynamics and interactions with other users by considering multi-user data. In this work, we present a new approach to using wrist-worn wearables for social monitoring and the detection of social interaction features based on interpersonal synchrony - an approach transferable to smartwatches and fitness trackers. We build up on related work in the field of psychology and present a study where we collected wearable sensing data during a social event with 24 participants. Our preliminary results indicate differences in wearable sensing data during a social interaction between two people
Modernisierungskonzept der Architektur von iBEAM
nicht vorhande
Interaction Design for Semi-Public Ambient Displays with Mobile and Motion-Tracking Components
nicht vorhande
HEALTHI: Workshop on Intelligent Healthy Interfaces
The second workshop on intelligent healthy interfaces (HEALTHI), collocated with the 2022 ACM Intelligent User Interfaces (IUI) conference, offers a forum that brings academics and industry researchers together and seeks submissions broadly related to the design of healthy user interfaces. The workshop will discuss intelligent user interfaces such as screens, wearables, voices assistants, and chatbots in the context of accessibly supporting health, health behavior, and wellbeing
Characteristics, origin, and potential for cancer diagnostics of ultrashort plasma cell-free DNA.
Current evidence suggests that plasma cell-free DNA (cfDNA) is fragmented around a mode of 166 bp. Data supporting this view has been mainly acquired through the analysis of double-stranded cfDNA. The characteristics and diagnostic potential of single-stranded and damaged double-stranded cfDNA in healthy individuals and cancer patients remain unclear. Here, through a combination of high-affinity magnetic bead-based DNA extraction and single-stranded DNA sequencing library preparation (MB-ssDNA), we report the discovery of a large proportion of cfDNA fragments centered at âŒ50 bp. We show that these "ultrashort" cfDNA fragments have a greater relative abundance in plasma of healthy individuals (median = 19.1% of all sequenced cfDNA fragments, n = 28) than in plasma of patients with cancer (median = 14.2%, n = 21, P < 0.0001). The ultrashort cfDNA fragments map to accessible chromatin regions of blood cells, particularly in promoter regions with the potential to adopt G-quadruplex (G4) DNA secondary structures. G4-positive promoter chromatin accessibility is significantly enriched in ultrashort plasma cfDNA fragments from healthy individuals relative to patients with cancers (P < 0.0001), in whom G4-cfDNA enrichment is inversely associated with copy number aberration-inferred tumor fractions. Our findings redraw the landscape of cfDNA fragmentation by identifying and characterizing a novel population of ultrashort plasma cfDNA fragments. Sequencing of MB-ssDNA libraries could facilitate the characterization of gene regulatory regions and DNA secondary structures via liquid biopsy. Our data underline the diagnostic potential of ultrashort cfDNA through classification for cancer patients