Alban Maxhuni

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Mobile computing is changing the landscape of clinical monitoring and self-monitoring. One of the major impacts will be in healthcare, where increase in number of sensing modalities is providing more and more information on the state of overall wellbeing, behaviour and health. There are numerous applications of mobile computing that range from wellbeing(More)
The level of participation in social interactions has been shown to have an impact on various health outcomes, while it also reflects the overall wellbeing status. In health sciences the standard practice for measuring the amount of social activity relies on periodical self-reports that suffer from memory dependence, recall bias and the current mood. In(More)
There is growing amount of scientific evidence that motor activity is the most consistent indicator of bipolar disorder. Motor activity includes several areas such as body movement, motor response time, level of psychomotor activity, and speech related motor activity. Studies of motor activity in bipolar disorder have typically used self-reported(More)
A number of clinical studies investigated associations between mood states and environmental factors. However, they mostly rely on self-reporting methods to describe past activities which, due to recall difficulties, may not be reliable. In this pilot study, we attempted to measure the amount of social interaction at workplace in an objective way and to(More)
OBJECTIVE Stress at work is a significant occupational health concern. Recent studies have used various sensing modalities to model stress behaviour based on non-obtrusive data obtained from smartphones. However, when the data for a subject is scarce it becomes a challenge to obtain a good model. METHODS We propose an approach based on a combination of(More)
Social interactions play an important role in the overall wellbeing. Current practice of monitoring social interactions through questionnaires and surveys is inadequate due to recall bias, memory dependence and high end-user effort. However, sensing capabilities of smartphones can play a significant role in automatic detection of social interactions. In(More)
In this paper, we present the concept of grouping individuals and detecting their proximity by emitting/receiving inaudible tones using their mobile phones. The inspiration stems from uniforms metaphor (of different colors) that groups subjects based on the roles, occupations or teams. The goal is to get an insight into the social context and social(More)
The emergence of genomically targeted cancer treatments has spurred the development of methods that correlate genomic information with treatments and outcomes. Because this information is usually pulled from published literature, such methods are limited to summarizing only the data generated through the slow and narrow publication pipeline. However, many(More)
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