J.M. Rodríguez-Ascariz

EOG-based eye movements codification for human computer interaction

Highlights? We present a new eye-control method for eye-based computer interaction using EOG. ? An electrooculographic eye model based on wavelet and neural networks is proposed. ? Results demonstrate the systems reliability in detecting eye movements. ? This model minimizes the problems associated with progressive user tiredness. ? Any HCI can be controlled using eye movements detected by EOG. This paper describes a new eye-control method for eye-based computer interaction using EOG. This work aims to resolve some of the problems encountered in current systems when used for long periods of time and users become tired. For this purpose, a new electrooculographic eye model based on wavelet transform and neural networks is proposed. The results obtained demonstrate the systems reliability in detecting eye movements and show an error of less than 2? during long periods of use. The system proposed may be used to control any graphical interface using eye movements detected by electrooculography.

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes.

Automated Detection of Breast Cancer in Thermal Infrared Images, Based on Independent Component Analysis

Breast cancer, among women, is the second-most common cancer and the leading cause of cancer death. It has become a major health issue in the world over the past decades and its incidence has increased in recent years mostly due to increased awareness of the importance of screening and population ageing. Early detection is crucial in the effective treatment of breast cancer. Current mammogram screening may turn up many tiny abnormalities that are either not cancerous or are slow-growing cancers that would never progress to the point of killing a woman and might never even become known to her. Ideally a better screening method should find a way of distinguishing the dangerous, aggressive tumors that need to be excised from the more languorous ones that do not. This paper therefore proposes a new method of thermographic image analysis for automated detection of high tumor risk areas, based on independent component analysis (ICA) and on post-processing of the images resulting from this algorithm. Tests carried out on a database enable tumor areas of 4 × 4 pixels on an original thermographic image to be detected. The proposed method has shown that the appearance of a heat anomaly indicating a potentially cancerous zone is reflected as an independent source by ICA analysis of the YCrCb components; the set of available images in our small series is giving us a sensitivity of 100% and a specificity of 94.7%.

Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram

The current clinical analysis of the multifocal electroretinography (mfERG) recordings for detecting glaucoma is based on standard signal morphology, measuring amplitudes and latencies. However, this analysis is not sensitive enough for detection of small changes in the multifocal electroretinogram signals. Other, more sophisticated, analysis methods should be explored to improve the sensitivity of this diagnostic technique, such as the discrete wavelet transform, proposed in this paper. We present an alternative method for the detection of open angle glaucoma based on the characterization of global flash mfERG signals. The digital signal processing technique is based on wavelets, hitherto unused in this field, for detection of advanced-stage glaucoma. Two markers were obtained from the recorded signals by applying the discrete wavelet transform, which help discriminate healthy from glaucomatous signals.

Portable system for temperature monitoring in all phases of wine production.

This paper presents a low-cost and highly versatile temperature-monitoring system applicable to all phases of wine production, from grape cultivation through to delivery of bottled wine to the end customer. Monitoring is performed by a purpose-built electronic system comprising a digital memory that stores temperature data and a ZigBee communication system that transmits it to a Control Centre for processing and display. The system has been tested under laboratory conditions and in real-world operational applications. One of the systems advantages is that it can be applied to every phase of wine production. Moreover, with minimum modification, other variables of interest (pH, humidity, etc.) could also be monitored and the system could be applied to other similar sectors, such as olive-oil production.

Multifocal ERG wavelet packet decomposition applied to glaucoma diagnosis

BackgroundGlaucoma is the second-leading cause of blindness worldwide and early diagnosis is essential to its treatment. Current clinical methods based on multifocal electroretinography (mfERG) essentially involve measurement of amplitudes and latencies and assume standard signal morphology. This paper presents a new method based on wavelet packet analysis of global-flash multifocal electroretinogram signals.MethodsThis study comprised twenty-five patients diagnosed with OAG and twenty-five control subjects. Their mfERG recordings data were used to develop the algorithm method based on wavelet packet analysis. By reconstructing the third wavelet packet contained in the fourth decomposition level (ADAA4) of the mfERG recording, it is possible to obtain a signal from which to extract a marker in the 60-80 ms time interval.ResultsThe marker found comprises oscillatory potentials with a negative-slope basal line in the case of glaucomatous recordings and a positive-slope basal line in the case of normal signals. Application of the optimal threshold calculated in the validation cases showed that the technique proposed achieved a sensitivity of 0.81 and validation specificity of 0.73.ConclusionsThis new method based on mfERG analysis may be reliable enough to detect functional deficits that are not apparent using current automated perimetry tests. As new stimulation and analysis protocols develop, mfERG has the potential to become a useful tool in early detection of glaucoma-related functional deficits.

Data Acquisition, Analysis and Transmission Platform for a Pay-As-You-Drive System

This paper presents a platform used to acquire, analyse and transmit data from a vehicle to a Control Centre as part of a Pay-As-You-Drive system. The aim is to monitor vehicle usage (how much, when, where and how) and, based on this information, assess the associated risk and set an appropriate insurance premium. To determine vehicle usage, the system analyses the driver’s respect for speed limits, driving style (aggressive or non-aggressive), mobile telephone use and the number of vehicle passengers. An electronic system on board the vehicle acquires these data, processes them and transmits them by mobile telephone (GPRS/UMTS) to a Control Centre, at which the insurance company assesses the risk associated with vehicles monitored by the system. The system provides insurance companies and their customers with an enhanced service and could potentially increase responsible driving habits and reduce the number of road accidents.

Multifocal electroretinogram diagnosis of glaucoma applying neural networks and structural pattern analysis

Glaucoma is a chronic ophthalmological disease that affects 5% of the 40-60-year-old population and can lead to irreversible blindness. The multifocal electroretinogram (mfERG) is a recently developed diagnostic technique that provides objective spatial data on the visual pathway and may be of potential benefit in early diagnosis of glaucoma. This paper analyses 13 morphological characteristics that define mfERG recordings and classifies them using a radial basis function network trained with the Extreme Learning Machine algorithm. When used to detect glaucomatous sectors, the method proposed produces sensitivity and specificity values of over 0.8.

Continuous-wavelet-transform analysis of the multifocal ERG waveform in glaucoma diagnosis

Abstract The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals’ amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.

A radio frequency energy harvesting laboratory practice

The production of energy by alternative environmentally friendly means that are not dependent on rechargeable pollutant batteries and regular maintenance is proposed as a promising way of powering autonomous low-consumption electronic systems. This paper presents a laboratory practice using a radio frequency (RF) energy harvesting circuit. Students learn to design and characterize a basic patch antenna, understand the process of tuning RF signals, and check operation of the system. It also considers several possible extensions and modifications that helped students enrich their knowledge of potential RF energy harvesting applications. Finally, the paper describes the feedback received from students after implementation of this laboratory practice in three academic years.