Open Source Implementation for Fall Classification and Fall Detection Systems

2020 ◽  
pp. 3-29
Author(s):  
Hiram Ponce ◽  
Lourdes Martínez-Villaseñor ◽  
José Núñez-Martínez ◽  
Ernesto Moya-Albor ◽  
Jorge Brieva
Keyword(s):  
Open Source ◽  
Fall Detection ◽  
Detection Systems

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.

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

scholarly journals UP-Fall Detection Dataset: A Multimodal Approach

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1988 ◽  
Author(s):  
Lourdes Martínez-Villaseñor ◽  
Hiram Ponce ◽  
Jorge Brieva ◽  
Ernesto Moya-Albor ◽  
José Núñez-Martínez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Wearable Sensors ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Elderly Persons ◽  
Negative Consequences ◽  
Detection Systems ◽  
Important Health ◽  
Experimental Use

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.

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 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 Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1831
Author(s):  
Armando Collado-Villaverde ◽  
Mario Cobos ◽  
Pablo Muñoz ◽  
David F. Barrero
Keyword(s):  
Machine Learning ◽  
Elderly Population ◽  
Real Data ◽  
Fall Detection ◽  
Wearable Devices ◽  
Accelerometer Data ◽  
Detection Systems ◽  
Detection Software ◽  
Health Complications ◽  
Public Datasets

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

An Analysis of Segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems With Machine Learning Models

2020 ◽  
Vol 20 (6) ◽  
pp. 3303-3313 ◽  
Author(s):  
Kai-Chun Liu ◽  
Chia-Yeh Hsieh ◽  
Hsiang-Yun Huang ◽  
Steen Jun-Ping Hsu ◽  
Chia-Tai Chan
Keyword(s):  
Machine Learning ◽  
Fall Detection ◽  
Learning Models ◽  
Detection Systems ◽  
Machine Learning Models

scholarly journals Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6479
Author(s):  
Luca Palmerini ◽  
Jochen Klenk ◽  
Clemens Becker ◽  
Lorenzo Chiari
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Inertial Sensor ◽  
Fall Detection ◽  
Medical Attention ◽  
Support Vector ◽  
Detection Algorithms ◽  
Detection Systems ◽  
Machine Learning Approach ◽  
Extensive Collection

Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.

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