Multimodal interface for an intelligent wheelchair

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 200

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Treat me well : affective and physiological feedback for wheelchair users

This work reports a electrocardiograph and skin conductivity hardware architecture, based on E-textile electrodes, attached to a wheelchair for affective and physiological computing. Appropriate conditioning circuits and a microcontroller platform that performs acquisition, primary processing, and communication using Bluetooth were designed and implemented. To increase the accuracy and repeatability of the skin conductivity measuring channel, force measurement sensors were attached to the system certifying measuring contact force on the electrode level. Advanced processing including Rwave peak detector, adaptive filtering and autonomic nervous system analysis based on wavelets transform was designed and implemented on a server. A central design of affective recognition and biofeedback system is described.Fundação para a Ciência e a Tecnologia (FCT

EEG signal analysis and characterization for the aid of disabled people

The effectiveness of assistive devices for disabled people is often limited by the human machine interface. This research proposes an intelligent wheelchair system especially for severely disabled people based on analysing electroencephalographic signals by using discrete wavelet transform and higher order statistical methods. The system to be implemented in Field Programmable Gate Array enables an accurate and efficient system of processing signals to control the wheelchair, which makes an attractive option in the hardware realization

Multimodal interface for an intelligent wheelchair

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 201