A short curriculum of the robotics and technology of computer lab

Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23 researchers that carry out their teaching and researching labor at the Escuela Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería Informática (Computer Engineering School). The main research fields are: a) Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes, c) Embedded and real-time systems, d) Parallel and massive processing computer architecture, d) Information Technologies for rehabilitation, handicapped and elder people, e) Web accessibility and usability In this paper, the Lab history is presented and its main publications and research projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo multidisciplinar de 23 investigadores que realizan su labor docente e investigadora en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las principales líneas de investigaciones son: a) Robótica industrial y móvil. b) Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo. e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las personas mayores. f) Usabilidad y accesibilidad Web. En este artículo se reseña la historia del grupo y se resumen las principales publicaciones y proyectos que ha conseguido en los últimos años

Universal access to mobile telephony as a way to enhance the autonomy of elderly people

The rise of mobile telephony has opened a vast diversity of new opportunities for older people with different levels of physical restrictions due to ageing. Mobile technology allows not only ubiquitous communications but also anytime access to some services that are vital for elderly people's security and autonomy. Nevertheless, with the numerous advantages, remote services can also introduce important social and ethical risks for this group of users. This paper tries to analyse the novelties that mobile technology may introduce into the lives of older users, points out some dangers and challenges arising from the use of these technologies and revises some future applications of the present mobile technologies.Ministerio de Ciencia y Tecnología TIC2001-1868-C03-0

Mobile Communication for Older People: New Opportunities for Autonomous Life

The fast diffusion of mobile telephony is opening a vast diversity of new opportunities for older people with different levels of physical restrictions due to ageing. Mobile technology not only allows ubiquitous communications but also anytime access to some services that are vital for their security and autonomy. Together with the numerous advantages, remote services can also mean important social and ethical risks for this group of users making indispensable that these risks are detected, analysed and avoided. Therefore, this paper analyses the novelties that mobile technology has introduced into the lives of older users, points out some dangers and challenges arising from the use of these technologies and revises some future applications of the present mobile technologies

Opportunities and Risks of the Information and Communication Technologies for Users with Special Needs

The fast developing of information and communication technologies has aroused the hope of P new society in which all people would kwe the same opportunities to access -through diverse eservices- to knowledge, work, leisure, etc. Information society has also offered a promising opportunity for social inclusion of people with disabilities. The combination of technological advancer (such as wireless personal area networks, wearable computing, etc.) with social advances (such as new inclusive legislation and social awareness) would make the social inclusion of people with special needs possible. Nevertheless, this will not automatically happen. It is necessary to apply inclusive design methods and to identify and avoid technological, ethicnl and social risk. This paper analyses the opportunities that information technology can offer to disabled people and the main risks that must be avoided. As a conclusion some guidelines to avoid these risks are outlined.Ministerio de Ciencia y Tecnología TIC2000-0087-P4Ministerio de Ciencia y Tecnología TIC2001-1868-C0

Mobile Communication for People with Disabilities and Older People: New Opportunities for Autonomous Life

The fast diffusion of mobile telephony is opening a vast diversity of new opportunities for people with different levels of physical restrictions, these due to disability or ageing. For this people mobile technology not only allows ubiquity for communications but also anytime access to some services that are vital for their security and autonomy. Together with the numerous advantages, remote services can also mean important social and ethical risks for this group of users making indispensable that these risks are detected, analysed and avoided. Therefore, this paper analyses the novelties that mobile technology has introduced into the lives of users with disabilities and older people, points out some dangers and challenges arising from the use of these technologies and revises some future applications of the present mobile technologies

A 5 Meps $100 USB2.0 Address-Event Monitor-Sequencer Interface

This paper describes a high-speed USB2.0 Address- Event Representation (AER) interface that allows simultaneous monitoring and sequencing of precisely timed AER data. This low-cost (<$100), two chip, bus powered interface can achieve sustained AER event rates of 5 megaevents per second (Meps). Several boards can be electrically synchronized, allowing simultaneous synchronized capture from multiple devices. It has three AER ports, one for sequencing, one for monitoring and one for passing through the monitored events. This paper also describes the host software infrastructure that makes the board usable for a heterogeneous mixture of AER devices and that allows recording and playback of recorded data

Wearable Fall Detector Using Recurrent Neural Networks

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time

TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation

Medical images from different clinics are acquired with different instruments and settings. To perform segmentation on these images as a cloud-based service we need to train with multiple datasets to increase the segmentation independency from the source. We also require an ef cient and fast segmentation network. In this work these two problems, which are essential for many practical medical imaging applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep neural networks which have been shown to be effective for medical image segmentation. Many different U-Net implementations have been proposed.With the recent development of tensor processing units (TPU), the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud services. In this paper, we study, using Google's publicly available colab environment, a generalized fully con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction. As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to glaucoma detection. To obtain networks with a good performance, independently of the image acquisition source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result of this study, we have developed a set of functions that allow the implementation of generalized U-Nets adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-

The Usefulness of Activity Trackers in Elderly with Reduced Mobility: A Case Study

This study was conducted to determine the accuracy and usefulness of two current commercially available activity trackers in rollator dependent elderly with reduced mobility (RME), compared with elderly with normal mobility (NME) and healthy adults (HA). Methods: Accuracy of pedometers placed at hip (Fitbit Ultra and Samsung GT-I9300 mobile phone) and wrist (Fitbit Ultra) were evaluated against actual steps (video) in RME (n=5), NME (n=7) and HA (n=6). Walk speed, Tinetti gait score and device percent error was calculated and analyzed in SPSS using Kruskal-Wallis, Mann-Whitney U and correlation tests. Results: NME and HA walked significantly faster (p = 0.001) than RME, had significantly higher gait score (p < 0.05). Gait scores were correlated with walking speed and negatively with pedometer percent error (p < 0.01). Estimation error in RME were >60% at all device locations Conclusions: Slow walking speed and gait disorders hamper the utility of pedometers for physical activity measurement in rollator dependent elderly, with estimation errors >60%. The tested devices are better suited for use by ostensibly healthy elderly or adult populations.European Ambient Assisted Living (AAL) Project AAL-2011-4- 09