scholarly journals Recognition of false alarms in fall detection systems

Author(s):  
Stefano Abbate ◽  
Marco Avvenuti ◽  
Guglielmo Cola ◽  
Paolo Corsini ◽  
Janet Light ◽  
...  
Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.


2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Glen Debard ◽  
Marc Mertens ◽  
Toon Goedemé ◽  
Tinne Tuytelaars ◽  
Bart Vanrumste

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.


Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5361
Author(s):  
Maurizio Capra ◽  
Stefano Sapienza ◽  
Paolo Motto Ros ◽  
Alessio Serrani ◽  
Maurizio Martina ◽  
...  

Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.


Author(s):  
Chris Dawson ◽  
Stuart Inkpen ◽  
Chris Nolan ◽  
David Bonnell

Many different approaches have been adopted for identifying leaks in pipelines. Leak detection systems, however, generally suffer from a number of difficulties and limitations. For existing and new pipelines, these inevitably force significant trade-offs to be made between detection accuracy, operational range, responsiveness, deployment cost, system reliability, and overall effectiveness. Existing leak detection systems frequently rely on the measurement of secondary effects such as temperature changes, acoustic signatures or flow differences to infer the existence of a leak. This paper presents an alternative approach to leak detection employing electromagnetic measurements of the material in the vicinity of the pipeline that can potentially overcome some of the difficulties encountered with existing approaches. This sensing technique makes direct measurements of the material near the pipeline resulting in reliable detection and minimal risk of false alarms. The technology has been used successfully in other industries to make critical measurements of materials under challenging circumstances. A number of prototype sensors were constructed using this technology and they were tested by an independent research laboratory. The test results show that sensors based on this technique exhibit a strong capability to detect oil, and to distinguish oil from water (a key challenge with in-situ sensors).


Author(s):  
Рустам Зайтунович Сунагатуллин ◽  
Антон Михайлович Чионов ◽  
Семен Васильевич Петренко

Автоматизированные системы управления используются в нефтепроводном транспорте с целью автоматизации технологических процессов транспортировки нефти и нефтепродуктов, при этом основной задачей является обеспечение надежности и безопасности перекачки, что невозможно без контроля целостности трубопровода. В связи с этим актуальной остается тема обнаружения утечек, требуют продолжения исследования в области повышения надежности автоматизированных систем обнаружения утечек (СОУ). При эксплуатации СОУ особую важность представляет описание процессов заполнения и опорожнения участков трубопровода с безнапорным течением. Скорость установления стационарного режима работы таких участков и участков с полным сечением существенно отличается. Слабые возмущения давления могут приводить к значительному дебалансу расхода нефти и, как следствие, вызывать ложные срабатывания СОУ. Авторами представлен алгоритм вычисления скорости изменения запаса нефти на участке трубопровода при медленном изменении размера самотечной полости, на основании которого предложен способ корректировки уравнения баланса вещества. Показано использование разработанного алгоритма для повышения чувствительности СОУ и уменьшения количества ложных срабатываний. During the operation of leak detection systems (LDS), it is of great importance to describe the processes of filling and emptying pipeline free flow sections. The speed of establishing a stationary operation mode of such sections and full sections is significantly different. Weak pressure perturbations can lead to significant imbalance in the oil flow rate and, as a consequence, cause false LDS positives. The authors present an algorithm for calculating rate of change in oil reserve in the pipeline section with a slow change in the size of gravity cavity, on the basis of which a method for adjusting the substance balance equation is proposed. The use of a developed algorithm is shown to increase the sensitivity of LDS and reduce the number of false alarms.


2021 ◽  
pp. 1-1
Author(s):  
Vasileios-Rafail Xefteris ◽  
Athina Tsanousa ◽  
Georgios Meditskos ◽  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1988 ◽  
Author(s):  
Lourdes Martínez-Villaseñor ◽  
Hiram Ponce ◽  
Jorge Brieva ◽  
Ernesto Moya-Albor ◽  
José Núñez-Martínez ◽  
...  

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.


2020 ◽  
Vol 2020 ◽  
pp. 1-36
Author(s):  
Eduardo Casilari ◽  
José A. Santoyo-Ramón ◽  
José M. Cano-García

Due to the serious impact of falls on the autonomy and health of older people, the investigation of wearable alerting systems for the automatic detection of falls has gained considerable scientific interest in the field of body telemonitoring with wireless sensors. Because of the difficulties of systematically validating these systems in a real application scenario, Fall Detection Systems (FDSs) are typically evaluated by studying their response to datasets containing inertial sensor measurements captured during the execution of labelled nonfall and fall movements. In this context, during the last decade, numerous publicly accessible databases have been released aiming at offering a common benchmarking tool for the validation of the new proposals on FDSs. This work offers a comparative and updated analysis of these existing repositories. For this purpose, the samples contained in the datasets are characterized by different statistics that model diverse aspects of the mobility of the human body in the time interval where the greatest change in the acceleration module is identified. By using one-way analysis of variance (ANOVA) on the series of these features, the comparison shows the significant differences detected between the datasets, even when comparing activities that require a similar degree of physical effort. This heterogeneity, which may result from the great variability of the sensors, experimental users, and testbeds employed to generate the datasets, is relevant because it casts doubt on the validity of the conclusions of many studies on FDSs, since most of the proposals in the literature are only evaluated using a single database.


Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 622
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari

Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.


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