Inmaculada Plaza

Challenges, issues and trends in fall detection systems.

Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.

Mobile applications in an aging society: Status and trends

Today, many countries, including several European states, the USA, and Japan, are aging; both the number and the percentage of elderly people are increasing. To create a cohesive and inclusive intergenerational society, technological products and services must be adapted to the needs and preferences of these citizens. Mobile phones are promising tools to improve the quality of life for the elderly. This work presents a review of the status of mobile functionalities and applications that can satisfy the requirements and needs of older people and improve their quality of life. This analysis of the state of the art enables us to identify the strengths and weaknesses of the current systems as well as discover trends and promising future lines of research. This paper outlines several needs that should be met to improve the quality of research in this area. This work provides a basis for researchers, designers, and mobile phone service providers to think about the existing needs of the elderly, the developing trends in the field and the opportunities that mobile applications offer to improve the quality of life of the elderly and to support a cohesive and inclusive society.

Mindfulness-Based Mobile Applications: Literature Review and Analysis of Current Features

Background Interest in mindfulness has increased exponentially, particularly in the fields of psychology and medicine. The trait or state of mindfulness is significantly related to several indicators of psychological health, and mindfulness-based therapies are effective at preventing and treating many chronic diseases. Interest in mobile applications for health promotion and disease self-management is also growing. Despite the explosion of interest, research on both the design and potential uses of mindfulness-based mobile applications (MBMAs) is scarce. Objective Our main objective was to study the features and functionalities of current MBMAs and compare them to current evidence-based literature in the health and clinical setting. Methods We searched online vendor markets, scientific journal databases, and grey literature related to MBMAs. We included mobile applications that featured a mindfulness-based component related to training or daily practice of mindfulness techniques. We excluded opinion-based articles from the literature. Results The literature search resulted in 11 eligible matches, two of which completely met our selection criteria–a pilot study designed to evaluate the feasibility of a MBMA to train the practice of “walking meditation,” and an exploratory study of an application consisting of mood reporting scales and mindfulness-based mobile therapies. The online market search eventually analyzed 50 available MBMAs. Of these, 8% (4/50) did not work, thus we only gathered information about language, downloads, or prices. The most common operating system was Android. Of the analyzed apps, 30% (15/50) have both a free and paid version. MBMAs were devoted to daily meditation practice (27/46, 59%), mindfulness training (6/46, 13%), assessments or tests (5/46, 11%), attention focus (4/46, 9%), and mixed objectives (4/46, 9%). We found 108 different resources, of which the most used were reminders, alarms, or bells (21/108, 19.4%), statistics tools (17/108, 15.7%), audio tracks (15/108, 13.9%), and educational texts (11/108, 10.2%). Daily, weekly, monthly statistics, or reports were provided by 37% (17/46) of the apps. 28% (13/46) of them permitted access to a social network. No information about sensors was available. The analyzed applications seemed not to use any external sensor. English was the only language of 78% (39/50) of the apps, and only 8% (4/50) provided information in Spanish. 20% (9/46) of the apps have interfaces that are difficult to use. No specific apps exist for professionals or, at least, for both profiles (users and professionals). We did not find any evaluations of health outcomes resulting from the use of MBMAs. Conclusions While a wide selection of MBMAs seem to be available to interested people, this study still shows an almost complete lack of evidence supporting the usefulness of those applications. We found no randomized clinical trials evaluating the impact of these applications on mindfulness training or health indicators, and the potential for mobile mindfulness applications remains largely unexplored.

State of the art of frameworks and middleware for facilitating mobile and ubiquitous learning development

The emergence of mobile and ubiquitous technologies as important tools to complement formal learning has been accompanied by a growing interest in their educational benefits and applications. Mobile devices can be used to promote learning anywhere and anytime, to foster social learning and knowledge sharing, or to visualize augmented reality applications for learning purposes. However, the development of these applications is difficult for many researchers because it requires understanding many different protocols; dealing with distributed schemas, processes, platforms, and services; learning new programming languages; and interacting with different hardware sensors and drivers. For that reason, the use of frameworks and middleware that encapsulate part of this complexity appears to be fundamental to the further development of mobile learning projects. This study analyzes the state of the art of frameworks and middleware devoted to simplifying the development of mobile and ubiquitous learning applications. The results can be useful to many researchers involved in the development of projects using these technologies by providing an overview of the features implemented in each of these frameworks.

Detecting falls as novelties in acceleration patterns acquired with smartphones.

Despite being a major public health problem, falls in the elderly cannot be detected efficiently yet. Many studies have used acceleration as the main input to discriminate between falls and activities of daily living (ADL). In recent years, there has been an increasing interest in using smartphones for fall detection. The most promising results have been obtained by supervised Machine Learning algorithms. However, a drawback of these approaches is that they rely on falls simulated by young or mature people, which might not represent every possible fall situation and might be different from older peoples falls. Thus, we propose to tackle the problem of fall detection by applying a kind of novelty detection methods which rely only on true ADL. In this way, a fall is any abnormal movement with respect to ADL. A system based on these methods could easily adapt itself to new situations since new ADL could be recorded continuously and the system could be re-trained on the fly. The goal of this work is to explore the use of such novelty detectors by selecting one of them and by comparing it with a state-of-the-art traditional supervised method under different conditions. The data sets we have collected were recorded with smartphones. Ten volunteers simulated eight type of falls, whereas ADL were recorded while they carried the phone in their real life. Even though we have not collected data from the elderly, the data sets were suitable to check the adaptability of novelty detectors. They have been made publicly available to improve the reproducibility of our results. We have studied several novelty detection methods, selecting the nearest neighbour-based technique (NN) as the most suitable. Then, we have compared NN with the Support Vector Machine (SVM). In most situations a generic SVM outperformed an adapted NN.

Continuous Improvement in Electronic Engineering Education

In this paper, the authors show an educational method based on a specific application of the quality philosophy in the development of digital electronics laboratory courses during two academic years. The authors present two main goals: to provide students with a methodology to manage problems and to start a process of continuous improvement. The level of achievement and the student evaluation show that the objectives have been fulfilled. Furthermore, the interdisciplinary character of this method allows for its application in other subjects.

A comparison of public datasets for acceleration-based fall detection

Falls are one of the leading causes of mortality among the older population, being the rapid detection of a fall a key factor to mitigate its main adverse health consequences. In this context, several authors have conducted studies on acceleration-based fall detection using external accelerometers or smartphones. The published detection rates are diverse, sometimes close to a perfect detector. This divergence may be explained by the difficulties in comparing different fall detection studies in a fair play since each study uses its own dataset obtained under different conditions. In this regard, several datasets have been made publicly available recently. This paper presents a comparison, to the best of our knowledge for the first time, of these public fall detection datasets in order to determine whether they have an influence on the declared performances. Using two different detection algorithms, the study shows that the performances of the fall detection techniques are affected, to a greater or lesser extent, by the specific datasets used to validate them. We have also found large differences in the generalization capability of a fall detector depending on the dataset used for training. In fact, the performance decreases dramatically when the algorithms are tested on a dataset different from the one used for training. Other characteristics of the datasets like the number of training samples also have an influence on the performance while algorithms seem less sensitive to the sampling frequency or the acceleration range.

Analysis and implementation of the IEC 61131-3 software model under POSIX Real-Time operating systems

Abstract In this paper, we present a proposal to implement the IEC 61131-3 software model. This model establishes the high-level elements of the Programmable Logic Controllers. We discuss some of the points of the standard that should be clarified before implementing it, such as concurrency, variable scope and cyclic operation. We show the implementation guidelines using POSIX compliant Real-Time Operating Systems, in particular RTLinux running on PCs. A translator has been implemented accepting textual descriptions following the IEC 61131-3 standard.

The Effect of Personalization on Smartphone-Based Fall Detectors

The risk of falling is high among different groups of people, such as older people, individuals with Parkinsons disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training.

Proposal of a quality model for educational software

The extensive application of ICTs in Education during the last years has caused that several authors suggested different approaches, criteria and tools for evaluation of educational software. After reviewing some of them, the present document shows a proposal of a quality model for educational software based on international standards, but adapted to teaching environment. This model allows to unify criteria and to standardize, as well as it is generic enough to make it suitable for any sort of educational software.