scholarly journals Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 622
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari
Keyword(s):  
Wireless Sensors ◽  
Inertial Sensors ◽  
Cost Effective ◽  
Fall Detection ◽  
Monitoring Point ◽  
Research Attention ◽  
Battery Lifetime ◽  
Detection Systems ◽  
Wearable Systems ◽  
Multiple Sensing

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.

scholarly journals A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari
Keyword(s):  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Point Of View ◽  
False Alarms ◽  
Battery Life ◽  
Area Network ◽  
Battery Lifetime ◽  
Detection Systems ◽  
Operational Aspects

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.

scholarly journals On the Heterogeneity of Existing Repositories of Movements Intended for the Evaluation of Fall Detection Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-36
Author(s):  
Eduardo Casilari ◽  
José A. Santoyo-Ramón ◽  
José M. Cano-García
Keyword(s):  
Wireless Sensors ◽  
Inertial Sensor ◽  
Fall Detection ◽  
Great Variability ◽  
Similar Degree ◽  
Time Interval ◽  
Scientific Interest ◽  
Physical Effort ◽  
Detection Systems ◽  
Alerting Systems

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.

scholarly journals A study on the impact of the users’ characteristics on the performance of wearable fall detection systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Antonio Santoyo-Ramón ◽  
Eduardo Casilari-Pérez ◽  
José Manuel Cano-García
Keyword(s):  
Daily Living ◽  
Inertial Sensors ◽  
Fall Detection ◽  
Test Phase ◽  
Controlled Environment ◽  
Discrimination Capability ◽  
Detection Systems ◽  
Activity Types ◽  
Experimental Subjects ◽  
The Impact

AbstractWearable Fall Detection Systems (FDSs) have gained much research interest during last decade. In this regard, Machine Learning (ML) classifiers have shown great efficiency in discriminating falls and conventional movements or Activities of Daily Living (ADLs) based on the analysis of the signals captured by transportable inertial sensors. Due to the intrinsic difficulties of training and testing this type of detectors in realistic scenarios and with their target audience (older adults), FDSs are normally benchmarked against a predefined set of ADLs and emulated falls executed by volunteers in a controlled environment. In most studies, however, samples from the same experimental subjects are used to both train and evaluate the FDSs. In this work, we investigate the performance of ML-based FDS systems when the test subjects have physical characteristics (weight, height, body mass index, age, gender) different from those of the users considered for the test phase. The results seem to point out that certain divergences (weight, height) of the users of both subsets (training ad test) may hamper the effectiveness of the classifiers (a reduction of up 20% in sensitivity and of up to 5% in specificity is reported). However, it is shown that the typology of the activities included in these subgroups has much greater relevance for the discrimination capability of the classifiers (with specificity losses of up to 95% if the activity types for training and testing strongly diverge).

scholarly journals A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 649 ◽  
Author(s):  
Eduardo Casilari ◽  
Moisés Álvarez-Marco ◽  
Francisco García-Lagos
Keyword(s):  
Inertial Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Systems ◽  
Combined Application ◽  
Measurement Units ◽  
Public Dataset ◽  
Cost Efficient ◽  
Wide Group

Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users.

Recent advances in chalcone-based anticancer heterocycles: A structural and molecular target perspective

2021 ◽  
Vol 28 ◽  
Author(s):  
Vishal Kumar ◽  
Sanjeev Dhawan ◽  
Pankaj Sanjay Girase ◽  
Paul Awolade ◽  
Suraj Raosaheb Shinde ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Biological Activities ◽  
Molecular Targets ◽  
Cathepsin K ◽  
Cost Effective ◽  
Sirtuin 1 ◽  
Anticancer Activities ◽  
Structural Variations ◽  
Research Attention ◽  
Endothelial Growth Factor

: Chalcones are an interesting class of compounds endowed with a plethora of biological activities beneficial to human health. These chemo types have continued to attract increased research attention over the years; hence, numerous natural and synthetic chalcones have found with interesting anticancer activities through the inhibition of various molecular targets including ABCG2, BCRP, P-glycoprotein, 5α-reductase, Androgen receptor (AR), Histone deacetylases (HDAC), Sirtuin 1, proteasome, Vascular endothelial growth factor (VEGF), Cathepsin-K, tubulin, CDC25B phosphatase, Topoisomerase, EBV, NF-κB, mTOR, BRAF and Wnt/β-catenin. Moreover, the study of intrinsic mechanisms of action, particularly relating to specific cellular pathways and modes of engagement with molecular targets, may help medicinal chemists to develop a more effective, selective, and cost-effective chalcone-based anticancer drugs. This review, therefore, sheds light on the effect of structural variations on the anticancer potency of chalcone hybrids reported in 2018-2019 alongside their mechanism of action, molecular targets, and potential impacts on effective cancer chemotherapy.

scholarly journals A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

2021 ◽  
Author(s):  
Nima Safaei ◽  
Omar Smadi ◽  
Babak Safaei ◽  
Arezoo Masoud
Keyword(s):  
Crack Detection ◽  
Low Cost ◽  
Local Level ◽  
Cost Effective ◽  
Segmentation Algorithm ◽  
Pavement Maintenance ◽  
Detection Systems ◽  
Wide Range ◽  
Cost Efficient ◽  
Pavement Crack Detection

<p>Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types.</p> <p>This paper proposes a new method that uses an improved version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. This method uses the Gaussian cumulative density function as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of different noise conditions. This method proved to be time and cost-efficient as it took less than 3.15 seconds per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to determine the detection results. This makes the model a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of low to severe-level cracks (Accuracy = 97.3%, Precision = 79.21%, Recall= 89.18% and F<sub>1</sub> score = 83.9%).</p>

scholarly journals Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study

10.2196/13961 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e13961
Author(s):  
Kim Sarah Sczuka ◽  
Lars Schwickert ◽  
Clemens Becker ◽  
Jochen Klenk
Keyword(s):  
Real World ◽  
Independent Living ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Center Of Mass ◽  
Fall Detection ◽  
Video Data ◽  
Sensor Data ◽  
Movement Behavior ◽  
The Real

Background Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. Objective The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. Methods Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. Results A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person’s center of mass during fall events based on the available sensor information. Conclusions Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available.

Performance, challenges, and limitations in multimodal fall detection systems: a review

2021 ◽  
pp. 1-1
Author(s):  
Vasileios-Rafail Xefteris ◽  
Athina Tsanousa ◽  
Georgios Meditskos ◽  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris
Keyword(s):  
Fall Detection ◽  
Detection Systems ◽  
Performance Challenges
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