Raul Igual

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.

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.

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.

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.

Personalizable smartphone application for detecting falls

A personalizable fall detector system is presented in this paper. It relies on a semisupervised novelty detection technique and has been implemented in a smartphone application. Thus, it has been tested that the algorithm can run comfortably in this kind of devices. Details about the internal structure of the application and a preliminary evaluation are also shown. The main difference with previous approaches relies in the fact that semisupervised techniques only require activities of daily life for its operation. Departures from normal movements are considered as falls. In this way, no simulated falls are needed, except for testing the performance. Therefore, the system can be easily adapted to each user.

Guidelines to Design Smartphone Applications for People with Intellectual Disability: A Practical Experience

Applications for smartphones have a great potential to facilitate the lives of people with intellectual disability. In fact, it is possible to design specific applications adapted to their needs. But even in this case, users may experience accessibility issues with some structural elements of smartphones. In this study, we have identified these elements through a 2-month test period with some people with intellectual disability. They used a simple smartphone application that met some needs identified by their caregivers. Through this practical experience, problems with the notification bar and the home, back, menu, search, volume and power buttons have been detected. Potential solutions to overcome these issues have also been proposed.

From Companies to Universities: Application of R&D&I Concepts in Higher Education Teaching

As a result of their involvement in several research and development and innovation (R&D&I) projects developed in various companies, the authors acquired knowledge of two basic concepts: quality and innovation. The application of these concepts in the teaching-learning process can help teachers to incorporate informal changes in the curriculum to provide for continuous improvement. This paper presents a simple methodology to apply quality and innovation concepts and tools in the daily teaching activity. In order to show the ease of use, as well as the efficiency and effectiveness of this methodology, the experience of applying it in an engineering course over nine years, the actions undertaken, the results obtained, and the lessons learned are described here. The methodology incorporates reflection, innovation, and decision taking based on objective data. Contact with the companies involved has enabled the teachers to learn continuously and to enhance their motivation when working with students. The methodology applied and the example described in this paper can be a starting point for other teachers interested in improving the quality of their teaching.

Combining novelty detectors to improve accelerometer-based fall detection

Research on body-worn sensors has shown how they can be used for the detection of falls in the elderly, which is a relevant health problem. However, most systems are trained with simulated falls, which differ from those of the target population. In this paper, we tackle the problem of fall detection using a combination of novelty detectors. A novelty detector can be trained only with activities of daily life (ADL), which are true movements recorded in real life. In addition, they allow adapting the system to new users, by recording new movements and retraining the system. The combination of several detectors and features enhances performance. The proposed approach has been compared with a traditional supervised algorithm, a support vector machine, which is trained with both falls and ADL. The combination of novelty detectors shows better performance in a typical cross-validation test and in an experiment that mimics the effect of personalizing the classifiers. The results indicate that it is possible to build a reliable fall detector based only on ADL.

A survey on modeling and simulation practices for teaching power harmonics

In recent years the undergraduate engineering curriculum of several areas has started to incorporate the study of harmonic distortion on electrical systems. Several education simulation tools have been developed and investigated, most of them a short time ago. However, to the best of our knowledge, there is not any literature review on this topic. In this work, we present for the first time a systematic survey on educational tools to model and simulate the effect of power harmonics on industrial electrical networks. As a result, several improvement aspects have been identified and recommendations and implications for future research have been provided. For that, a retrospective study has been accomplished, showing a growing trend toward simulation and modeling. The key teachers’ perceptions to incorporate these practices are the ease of the teaching‐learning process and the importance of modeling and simulation in the industry. The main research challenge is the conduction of integral assessments that comprise both tools’ quality and use experience, and their contribution to achieve learning outcomes. Statistical tests should be used to validate the academic results obtained. This is a major challenge, since only a few of the existing studies incorporate such tests. Other implications for future research refer to the involvement of a higher number of students in the evaluations and the use of free software in the development of the applications.