TATU: an Approach for Supporting Tourists with Disabilities to Indoor and Outdoor Navigation using Mobile Devices

People with disabilities living in Brazil face great difficulties in the tasks of daily life mainly due to the lack of accessibility in public spaces, products and services. In this context, we noticed a lack of a computing tool that embraces both people with visual and hearing impairment. This work presents TATU -- a mobile application for both Android and iOS platforms aimed at supporting people with visual or hearing impairment to enjoy Brazilian tourist attractions, including both open-air and indoor spaces. TATU application has an adaptive interface exclusively designed for each of the impairment user profiles, it can work on guided tour mode by indoor navigation using BLE beacons and outdoor navigation using GPS. Our solution was evaluated by three experiments, one of which was carried out with blind volunteers and TATU application obtained satisfactory results for both spacious attractions with the lowest density of collection items and for the smallest spaces.

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive “tool” whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.

Integrated adaptive interface design system development of the magnetic separator considering the shapes of the hopper for a powder line

Foreign metal removal is a key process of quality control in the food and pharmaceutical industries and can possibly be achieved using a magnet separator. Typically, magnetic separators are installed at existing production facilities by remodeling because they have the ability to deal with the problems that arise in production facilities. However, when measuring for remodeling, problems such as measurement error, forgotten measurements, change in location, detail proposal changes, or impossibility to measure occur because of complex, distorted or dented shapes, dimensional inaccuracy, and the surroundings. Additionally, the magnet separators designed to fit an existing production have problems in that the dimensions differ from those of the existing facilities, and deficiency is expected in the performance. To solve these problems using a non- conventional method, we developed an adaptive interface design system that combines high accuracy measurement by means of 3D scan to reproduce the existing production facilities as distorted shape and dented shape by reverse engineering, and the optimized finite element method analysis for magnet field to satisfy an expected performance of the surface flux density, and inspect the shape of the design, dimensions, and performance, using computer aided engineering.

Enhancing the toughness of composites via dynamic thiol–thioester exchange (TTE) at the resin–filler interface

An adaptive interface employing thiol-thioester exchange (TTE) at the resin-filler interface is introduced to promote interfacial stress relaxation and improve the mechanical performance of thermosetting composites.