A study of the influence of the sensor sampling frequency on the performance of wearable fall detectors

Last decade has witnessed a major research interest on wearable fall detection systems. Sampling rate in these detectors strongly affects the power consumption and required complexity of the employed wearables. This study investigates the effect of the sampling frequency on the efficacy of the detection process. For this purpose, we train a convolutional neural network to directly discriminate falls from conventional activities based on the raw acceleration signals captured by a transportable sensor. Then, we analyze the changes in the performance of this classifier when the sampling rate is progressively reduced. In contrast with previous studies, the detector is tested against a wide set of public repositories of benchmarking traces. The quality metrics achieved for the different frequencies and the analysis of the spectrum of the signals reveal that a sampling rate of 20 Hz can be enough to maximize the effectiveness of a fall detector.This research was funded by the Andalusian Regional Government (-Junta de Andalucía-) under grants FEDER UMA18-FEDERJA-022 and PAIDI P18-RT-1652, and by the Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech. Funding for open access charge: Universidad de Malaga / CBUA

Cross-dataset evaluation of wearable fall detection systems using data from real falls and long-term monitoring of daily life

The evaluation of fall detection systems based on wearables is controversial as most studies in the literature benchmark their proposals against falls that are simulated by experimental subjects under unrealistic laboratory conditions. In order to systematically investigate the suitability of this procedure, this paper evaluates a wide set of artificial intelligence algorithms used for fall detection, when trained with a large number of datasets containing acceleration samples captured during the emulation of falls and ordinary movements and then tested with the signals of both actual falls and long-term traces collected from the constant monitoring of users during their daily routines. The results, based on a large number of repositories, show a remarkable degradation in all performance metrics (sensitivity, specificity and false alarm hourly rate) with respect to the typical case in which the detectors are tested with the same types of laboratory movements for which they were trained.Funding for open access charge: Universidad de Málaga / CBU

An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Automatic fall detection is one of the most promising applications of wearables in the field of mobile health. The characterization of the effectiveness of wearable fall detectors is hampered by the inherent difficulty of testing these devices with real-world falls. In fact, practically all the proposals in the literature assess the detection algorithms with ‘scripted’ falls that are simulated in a controlled laboratory environment by a group of volunteers (normally young and healthy participants). Aiming at appraising the adequacy of this method, this work systematically compares the statistical characteristics of the acceleration signals from two databases with real falls and those computed from the simulated falls provided by 18 well-known repositories commonly employed by the related works. The results show noteworthy differences between the dynamics of emulated and real-life falls, which undermines the testing procedures followed to date and forces to rethink the strategies for evaluating wearable fall detectors.Funding for open access charge: Universidad de Málaga / CBUA. This research was funded by FEDER Funds (under grant UMA18-FEDERJA-022), Andalusian Regional Government (-Junta de Andalucía- grant PAIDI P18-RT-1652) and Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech

Electronic textile garments for fall and near-fall detection

The world population is ageing and one of the biggest detriments to the quality of life of older people is falls. The aim of this thesis is to develop an electronic textiles (E-textile) garment using electronic yarn (E-yarn) technology for near-fall and fall detection. Near-falls are a loss of balance that can be corrected. An increased number in near-falls is seen as a precursor for falls. If near-falls can be detected, hopefully this can lead to fall prevention. The first step to creating an E-textile for near-fall was to determine the appropriate sensor for near-fall detection. Within the literature there are more studies conducted on fall detection systems rather than near-fall detection. Consequently, both types of system were reviewed. Informed by the literature, it was concluded that an inertial measurement unit (IMU) would be used to manufacturing a motion sensing E-yarn. Once the sensor had been determined, the optimal placement of the sensor on the body needed to be found. In accordance with the literature six locations were explored, the waist, chest, wrist, lower back, thigh and ankle. A pilot study was conducted, and the results showed that either the waist, thigh or ankle were best. Interviews and a focus group were held to design an E-textile garment that an older person would be willing to wear. Interviews on clothing preferences, attitudes towards falls, and wearable technology for fall prevention were conducted. Non-functioning prototypes were made and shared with a focus group to determine which would be used in the final design. The design chosen was an over-sock. Lastly, a functioning E-textile garment was developed and tested on young healthy volunteers. The E-textile garment can accurately classify between three types of activities of daily living and three type of falls with an accuracy of 85.7%. When classifying between ADLs and the falls, the accuracy of detection was 99.4%. Furthermore, when classifying between the ADLs, the falls, and a near-fall event an accuracy of 94.2% was achieved. This thesis contributes new knowledge to the field of E-textiles by using human centered design to create an E-textile garment people are willing to wear. It also has created the first near-fall and fall detection system in the form of an E-textile and presents the first E-yarn to contain an IMU

Scientific Advances in STEM: From Professor to Students

This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields