50 research outputs found
Research to Market Transition of Mobile Assistive Technologies for People with Visual Impairments
Mobile devices are accessible to people with visual impairments and hence they are convenient platforms to support assistive technologies. Indeed, in the last years many scientifc contributions proposed assistive applications for mobile devices. However, few of these solutions were eventually delivered to end-users, depriving people with disabilities of important assistive tools. The underlying problem is that a number of challenges need to be faced for transitioning assistive mobile applications from research to market. This contribution reports authors\u2019 experience in the academic research and successive distribution of three mobile assistive applications for people with visual impairment. As a general message, we describe the relevant characteristics of the target population, analyze different models of transition from academic research to end-users distribution and show how the transitioning process has a positive impact on research
An efficient algorithm for minimizing time granularity periodical representations
This paper addresses the technical problem of efficiently reducing the periodic representation of a time granularity to its minimal form. The minimization algorithm presented in the paper has an immediate practical application: it allows users to intuitively define granularities (and more generally, recurring events) with algebraic expressions that are then internally translated to mathematical characterizations in terms of minimal periodic sets. Minimality plays a crucial role, since the value of the recurring period has been shown to dominate the complexity when processing periodic sets.
Sonification of guidance data during road crossing for people with visual impairments or blindness
In the last years several solutions were proposed to support people with
visual impairments or blindness during road crossing. These solutions focus on
computer vision techniques for recognizing pedestrian crosswalks and computing
their relative position from the user. Instead, this contribution addresses a
different problem; the design of an auditory interface that can effectively
guide the user during road crossing. Two original auditory guiding modes based
on data sonification are presented and compared with a guiding mode based on
speech messages.
Experimental evaluation shows that there is no guiding mode that is best
suited for all test subjects. The average time to align and cross is not
significantly different among the three guiding modes, and test subjects
distribute their preferences for the best guiding mode almost uniformly among
the three solutions. From the experiments it also emerges that higher effort is
necessary for decoding the sonified instructions if compared to the speech
instructions, and that test subjects require frequent `hints' (in the form of
speech messages). Despite this, more than 2/3 of test subjects prefer one of
the two guiding modes based on sonification. There are two main reasons for
this: firstly, with speech messages it is harder to hear the sound of the
environment, and secondly sonified messages convey information about the
"quantity" of the expected movement
Towards privacy protection in a middleware for context-awareness
Privacy is recognized as a fundamental issue for the provision of context-aware services. In this paper we present work in progress regarding the definition of a comprehensive framework for supporting context-aware services while protecting users' privacy. Our proposal is based on a combination of mechanisms for enforcing context-aware privacy policies and k-anonymity. Moreover, our proposed technique involves the use of stereotypes for generalizing precise identity information to the aim of protecting users' privacy
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images
In recent months, the monkeypox (mpox) virus -- previously endemic in a
limited area of the world -- has started spreading in multiple countries until
being declared a ``public health emergency of international concern'' by the
World Health Organization. The alert was renewed in February 2023 due to a
persisting sustained incidence of the virus in several countries and worries
about possible new outbreaks. Low-income countries with inadequate
infrastructures for vaccine and testing administration are particularly at
risk.
A symptom of mpox infection is the appearance of skin rashes and eruptions,
which can drive people to seek medical advice. A technology that might help
perform a preliminary screening based on the aspect of skin lesions is the use
of Machine Learning for image classification. However, to make this technology
suitable on a large scale, it should be usable directly on mobile devices of
people, with a possible notification to a remote medical expert.
In this work, we investigate the adoption of Deep Learning to detect mpox
from skin lesion images. The proposal leverages Transfer Learning to cope with
the scarce availability of mpox image datasets. As a first step, a homogenous,
unpolluted, dataset is produced by manual selection and preprocessing of
available image data. It will also be released publicly to researchers in the
field. Then, a thorough comparison is conducted amongst several Convolutional
Neural Networks, based on a 10-fold stratified cross-validation. The best
models are then optimized through quantization for use on mobile devices;
measures of classification quality, memory footprint, and processing times
validate the feasibility of our proposal. Additionally, the use of eXplainable
AI is investigated as a suitable instrument to both technically and clinically
validate classification outcomes.Comment: Submitted to Pervasive and Mobile Computin