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Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults
2021
Sensors
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We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING ...
Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications. ...
Acknowledgments: We thank all participating men and women in the FARSEEING and SisFall pro ject, as well as all FARSEEING and SisFall research scientists, study and data managers and clinical and administrative ...
doi:10.3390/s21217166
pmid:34770473
pmcid:PMC8588363
fatcat:3ckprhsydrgknpuk7yswwx2eja
SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning
2018
Sensors
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We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different ...
fall datasets (Smartwatch, Notch, Farseeing). ...
Yeahuay Wu for her assistance, and former graduate students, Mr. Manvick Paliwal and Po-Teng Tseng for helping with the fall data collection process. ...
doi:10.3390/s18103363
pmid:30304768
pmcid:PMC6210545
fatcat:jm7jajbdabgajgvjwpjkefgakm
Improving Fall Detection Using an On-Wrist Wearable Accelerometer
2018
Sensors
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Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls ...
This research focuses on a method for fall detection with a sensor placed on the wrist. ...
On the other hand, the FARSEEING dataset [3] is also used for studying the validity of the simulated falls compared with real falls. ...
doi:10.3390/s18051350
pmid:29701721
pmcid:PMC5982860
fatcat:qdxaarlombhn5jokc4oxh2fsqq
Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls
2020
Sensors
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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. ...
We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. ...
Acknowledgments: The authors wish to thank Julia Marshall Leach for her valuable English revision of the manuscript. ...
doi:10.3390/s20226479
pmid:33202738
fatcat:vgi3fvcf5baelb5rz3xts22lq4
Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets
2017
PLoS ONE
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the target population to perform fall validation studies for fall detection systems on bigger real-world fall and non-fall datasets. ...
In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. ...
Acknowledgments This work was supported by Mitacs and team grants from the Canadian Institutes of Health Research (funding reference numbers: AMG-100487 and TIR-103945). ...
doi:10.1371/journal.pone.0180318
pmid:28678808
pmcid:PMC5498034
fatcat:gzgf42ivz5g7lk5ecmzp2r453u
Domain-adaptive Fall Detection Using Deep Adversarial Training
[article]
2021
arXiv
pre-print
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Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. ...
Cross-domain knowledge transfer is very beneficial for machine-learning-based FD systems to train a reliable FD model with well-labeled data in new environments. ...
One limitation of this study is that we only employed the simulated datasets instead of real-world datasets (e.g., FARSEEING [40] ). ...
arXiv:2012.10911v2
fatcat:cra3ttt6mvd4zmp5kl5zlwwuca
Domain-adaptive Fall Detection Using Deep Adversarial Training
2021
IEEE transactions on neural systems and rehabilitation engineering
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Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. ...
Cross-domain knowledge transfer is very beneficial for machine-learning based FD systems to train a reliable FD model with well-labeled data in new environments. ...
One limitation of this study is that we only employed the simulated datasets instead of real-world datasets (e.g., FARSEEING [40] ). ...
doi:10.1109/tnsre.2021.3089685
pmid:34133280
fatcat:fr6bdtdyfvgbjna4j7k4xwdj5i
A Survey on Recent Advances in Wearable Fall Detection Systems
2020
BioMed Research International
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Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. ...
According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. ...
Conflicts of Interest e authors declare that there are no conflicts of interest regarding the publication of this paper. ...
doi:10.1155/2020/2167160
pmid:32420327
pmcid:PMC7201510
fatcat:76hoyk4sgferhdecmkxe2eog3q
NT-FDS—A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices
2021
Sensors
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The proposed technique is evaluated on two publicly available datasets—SisFall and UP-Fall Detection. ...
data making it unfit for reliable fall detection. ...
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations The following abbreviations are used in this manuscript:
IoT Internet of Things ...
doi:10.3390/s21062006
pmid:33809080
fatcat:pmgj6imb5zbh7exwwdv63kkdte
Deep Learning Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems
[article]
2021
arXiv
pre-print
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Among them, a reduced sampling rate is a common approach for an energy-efficient system in the real-world. ...
Such systems automatically detect critical fall events and immediately alert medical professionals or caregivers. ...
Compared to other datasets, these two datasets with diverse fall types and activities of daily living (ADL) are more challenging and closer to the real-world situation. ...
arXiv:2012.03426v2
fatcat:4dmvvyx5yjddpppq5s5dtponqi
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
[article]
2021
arXiv
pre-print
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We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. ...
In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. ...
CASAS-4 [138]
Real-world Home Living
2
15
Object, Ambient
Multi-occupant
Composite Activity
Multimodal
Smartwatch/Notch/Farseeing [96]
Daily Living & Fall Detection
7
4 ADL & 4 Fall ...
arXiv:2001.07416v2
fatcat:km2b3xn4sngtxgkdck6ymlmu3m
Sponsoring institutions
1985
Nuclear Physics A
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Acknowledgements We warmly acknowledge Sapienza University, in particular our Department and our Faculty, for continuous support to student participation in the competitions. ...
Cortelazzo, for the hand gesture recognition library for the RGB-D sensor. Towards smart robots: rock-paper-scissors gaming versus human players ...
A number of publicly-and privately-financed initiatives and projects are dealing with the many aspects related to the falls, such as fall risk assessment, fall risk prevention, falls detection, fall treatment ...
doi:10.1016/0375-9474(85)90488-9
fatcat:rduhsx664rbqnegxl4hkxgp3lu
Sponsoring institutions
2002
Physica A: Statistical Mechanics and its Applications
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Acknowledgements We warmly acknowledge Sapienza University, in particular our Department and our Faculty, for continuous support to student participation in the competitions. ...
Cortelazzo, for the hand gesture recognition library for the RGB-D sensor. Towards smart robots: rock-paper-scissors gaming versus human players ...
A number of publicly-and privately-financed initiatives and projects are dealing with the many aspects related to the falls, such as fall risk assessment, fall risk prevention, falls detection, fall treatment ...
doi:10.1016/s0378-4371(02)01572-8
fatcat:qcyfgqu3sfd45jncgigcqlzjc4
Towards a smart fall detection system using wearable sensors
2022
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Toward this goal, existing fall-detection approaches (both threshold- and machine-learning-based) are explored and evaluated using publicly accessible datasets: Cogent, SisFall, and FARSEEING. ...
A novel approach called an event-triggered machine-learning approach for fall detection (EvenT-ML) is proposed, which can correctly align fall stages into a data segment and extract features based on those ...
Information related to this dataset request can be found at:http://farseeingresearch.eu/ the-farseeing-real-world-fall-repository-a-large-scale-collaborative-databaseto-collect-and-share-sensor-signals-from-real-world-falls ...
doi:10.25949/19443677.v1
fatcat:5jvdnpgkxzdqhev3j6miimn2yi
A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology—The ADAPT Study Data-Set
2017
Sensors
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This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms. ...
This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults ...
datasets recorded from independent living older adults in previous projects, where detection of falls and the assessment of fall risk was the focus (see Table 1 and Figure 1 ). ...
doi:10.3390/s17030559
pmid:28287449
pmcid:PMC5375845
fatcat:pxokho72kfeerjrggw7vaigvje
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