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

Evaluation of a Fall Alerting System based on a Convolutional Deep Neural Network

Artículo sobre detección de caídas con redes neuronales profundasOwing to the effects of falls on quality of life of the elderly, automatic fall detection systems (FDS) have become a key research topic in the ambit of telecare. This works assesses the performance of convolutional neural networks when they are applied to identify fall accidents in a wearable FDS provided with a tri-axial accelerometer. The evaluation of the detection algorithm is carried out by employing a benchmarking repository with a wide set of traces captured from a wide group of volunteers that executed a programmed series of Activities of the Daily Living (ADLs) and emulated falls. Results show that the CNN can properly distinguish both types of movements with a success rate (specificity and sensitivity) around 99%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis of a public repository for the study of automatic fall detection algorithms

The use of publicly available repositories containing movement traces of real or experimental subjects is a key aspect to define an evaluation framework that allows a systematic assessment of wearable fall detection systems. This papers presents a detailed analysis of a public dataset of traces which employed five sensing points to characterize the user’s mobility during the execution of ADLs (Activities of Daily Living) and emulated falls. The analysis is aimed at analysing two main factors: the importance of the election of the position of the sensor and the possible impact of the user’s personal features on the statistical characterization of the movements. Results reveal the importance of the nature of the ADL for the effectiveness of the discrimination of the falls.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Review on the Simulation of Cooperative Caching Schemes for MANETs

In this paper, a review of the main simulation parameters utilized to evaluate the performance of cooperative caching schemes in Mobile Ad Hoc Networks is presented. Firstly, a taxonomy of twenty five caching schemes proposed in the literature about Mobile Ad Hoc Networks is defined. Those caching schemes are briefly described in order to illustrate their basis and fundamentals. The review takes into consideration the utilized network simulator, the wireless connection standard, the propagation model and routing protocol, the employed simulation area and number of data servers, the number of mobile devices and their coverage area, the mobility model, the number of documents in the network, the replacement policy and cache size, the mean time between requests, the document popularity distribution, the TTL (Time To Live) of the documents and the simulation time. Those simulation parameters have been compared among the evaluation of the studied cooperative caching schemes in order to obtain the most common utilized values. This work will allow to compare the performance of the proposed cooperative caching schemes using a common simulation environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Monitoring and detection of toothbrushing with smart watches and artificial intelligence.

It has been estimated that oral and dental diseases affect almost half of humanity, largely due to the infrequency with which a considerable proportion of the population brush their teeth (less than twice a day, with a regularity that markedly decreases for older ages). In this context, the automatic monitoring of dental hygiene routines may be of great interest, since it can help promote healthy habits and remind the user (especially older persons or patients in the initial stages of dementia) of the need to brushing her/his teeth after each meal.In this sense, although initially envisaged to track sporting performance, current smartwatches and smartbands have found a relevant field in HAR (Human Activity Recognition) systems, especially in those applications intended to supervise health status and personal well-being. Thus, these wearables offer a low-cost, non-invasive tool, which is already fully integrated into our daily lives, capable of informing us at all times about the evolution of diverse biosignals or health parameters and even generating alerts in case a medical alarm is suspected. This work proposes to combine wearables and the use of artificial intelligence techniques to detect manual mobility patterns caused by brushing teeth. Specifically, the article describes and evaluate a system based on convolutional neural networks, able to identify brushing gestures from samples of few seconds of inertial accelerometry signals gathered by wrist-worn devices. The architecture is systematically trained and validated with the signals provided by different public databases which were collected when different experimental subjects executed different manual actions. The results show the effectiveness of the detector, since it reaches a sensitivity and specificity greater than 95% when applied to discriminate brushing from other hand movements. In addition, the system is re-trained and assessed with the real-life samples captured by a smartwatch, where the neural model is implemented to operate and produce real-time decisionsThis work proposes to combine wearables and the use of artificial intelligence techniques to detect manual mobility patterns caused by brushing teeth. Specifically, the article describes and evaluate a system based on convolutional neural networks, able to identify brushing gestures from samples of few seconds of inertial accelerometry signals gathered by wrist-worn devices. The architecture is systematically trained and validated with the signals provided by different public databases which were collected when different experimental subjects executed different manual actions. The results show the effectiveness of the detector, since it reaches a sensitivity and specificity greater than 95% when applied to discriminate brushing from other hand actions. In addition, the system is re-trained and assessed with the real-life samples captured by a smartwatch, where the neural model could be implemented to operate and produce real-time decisions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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

UMATUG: A dataset of inertial signals of older and young adults using a gerontologic simulator collected during instrumented Timed Up and Go (iTUG) tests

Timed Up and Go (TUG) test is one of the most popular clinical tools aimed at the assessment of functional mobility and fall risk in older adults. The automation of the analysis of TUG movements is of great medical interest not only to speed up the test but also to maximize the information inferred from the subjects under study. In this context, this article describes a dataset collected from a cohort of 69 experimental subjects (including 30 adults over 60 years), during the execution of several repetitions of the TUG test. In particular, the dataset includes the measurements gath- ered with four wearables devices embedding four sensors (accelerometer, gyroscope magnetometer and barometer) located on four body locations (waist, wrist, ankle and chest). As a particularity, the dataset also includes the same measurements recorded when the young subjects repeat the test while wearing a commercial geriatric simulator, consisting of a set of weighted vests and other elements intended to replicate the limitations caused by aging. Thus, the generated dataset also enables the investigation into the potential of such tools to emulate the actual dynamics of older individuals.Funding for open access charge: Partial funding for open access charge: Universidad de Málaga / CBUA This research was funded by the Spanish Ministry of Science, Innovation, and Universities ( MCIN/AEI/10.13039/50110 0 011033 ) and NextGenerationEU/PRTR Funds under grant TED2021- 130456B-I00, by Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech (grant B4-2023-12) and DIANA PAIDI research group

UMAHand: A dataset of inertial signals of typical hand activities

Given the popularity of wrist-worn devices, particularly smartwatches, the identification of manual movement pat- terns has become of utmost interest within the research field of Human Activity Recognition (HAR) systems. In this con- text, by leveraging the numerous sensors natively embedded in smartwatches, the HAR functionalities that can be imple- mented in a watch via software and in a very cost-efficient way cover a wide variety of applications, ranging from fit- ness trackers to gesture detectors aimed at disabled individ- uals (e.g., for sending alarms), promoting behavioral activa- tion or healthy lifestyle habits. In this regard, for the devel- opment of artificial intelligence algorithms capable of effec- tively discriminating these activities, it is of great importance to have repositories of movements that allow the scientific community to train, evaluate, and benchmark new proposals of movement detectors. The UMAHand dataset offers a col- lection of files containing the signals captured by a Shim- mer 3 sensor node, which includes an accelerometer, a gy- roscope, a magnetometer and a barometer, during the ex- ecution of different typical hand movements. For that pur- pose, the measurements from these four sensors, gathered at a sampling rate of 100 Hz, were taken from a group of 25 volunteers (16 females and 9 males), aged between 18 and 56, during the performance of 29 daily life activities involv- ing hand mobility. Participants wore the sensor node on their dominant hand throughout the experiments.This research was funded by the Spanish Ministry of Science, Innovation, and Universities ( MCIN/AEI/10.13039/50110 0 011033 ) and NextGenerationEU/PRTR Funds under grant TED2021- 130456B-I00 , by Universidad de Málaga /CBUA , Campus de Excelencia Internacional Andalucia Tech (grant B4-2023-12 ) and DIANA TIC171 PAIDI research group. Partial funding for open access charge: Universidad de Málaga / CBU

A characterization of the performance of Bluetooth 2.x + EDR technology in noisy environments

Bluetooth (BT) is by far the most popular shortrange technology for the development of wireless personal area networks and body area networks. Nowadays, BT 2.0 and 2.1 ? EDR are the most extended and implemented versions of BT standard. This article presents an analytical model that computes the packet delay of transmissions that utilize this version of BT in noisy environments. The model, which takes into account the packet retransmissions caused by noise, is particularized to calculate the mean packet delay as a function of the signal-to-noise ratio for the different enhanced data rates provided by BT 2.0 and 2.1 specifications. Thus, the model permits evaluating the efficiency of using these enhanced rates in the presence of a certain noise level.Ministerio de Ciencia e Innovación TEC2009-13763-C02-01Ministerio de Ciencia e Innovación TEC2013-42711-

UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection

The progress in the field of inertial sensor technology and the widespread popularity of personal electronics such as smartwatches or smartphones have prompted the research on wearable Fall Detection Systems (FDSs). In spite of the extensive literature on FDSs, an open issue is the definition of a common framework that allows a methodical and agreed evaluation of fall detection policies. In this regard, a key aspect is the lack of a public repository of movement datasets that can be employed by the researchers as a common reference to compare and assess their proposals. This work describes UMAFall, a new dataset of movement traces acquired through the systematic emulation of a set of predefined ADLs (Activities of Daily Life) and falls. In opposition to other existing databases for FDSs, which only include the signals captured by one or two sensing points, the testbed deployed for the generation of UMAFall dataset incorporated five wearable sensing points, which were located on five different points of the body of the participants that developed the movements. As a consequence, the obtained data offer an interesting tool to investigate the importance of the sensor placement for the effectiveness of the detection decision in FDSs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech