356 related articles for article (PubMed ID: 29021127)
1. Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.
Shawen N; Lonini L; Mummidisetty CK; Shparii I; Albert MV; Kording K; Jayaraman A
JMIR Mhealth Uhealth; 2017 Oct; 5(10):e151. PubMed ID: 29021127
[TBL] [Abstract][Full Text] [Related]
2. Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.
Aziz O; Klenk J; Schwickert L; Chiari L; Becker C; Park EJ; Mori G; Robinovitch SN
PLoS One; 2017; 12(7):e0180318. PubMed ID: 28678808
[TBL] [Abstract][Full Text] [Related]
3. A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.
Aziz O; Musngi M; Park EJ; Mori G; Robinovitch SN
Med Biol Eng Comput; 2017 Jan; 55(1):45-55. PubMed ID: 27106749
[TBL] [Abstract][Full Text] [Related]
4. Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.
Shawen N; Lonini L; Mummidisetty CK; Shparii I; Albert MV; Kording K; Jayaraman A
JMIR Mhealth Uhealth; 2017 Dec; 5(12):e167. PubMed ID: 29261509
[TBL] [Abstract][Full Text] [Related]
5. Hidden Markov Model-Based Fall Detection With Motion Sensor Orientation Calibration: A Case for Real-Life Home Monitoring.
Yu S; Chen H; Brown RA
IEEE J Biomed Health Inform; 2018 Nov; 22(6):1847-1853. PubMed ID: 29990227
[TBL] [Abstract][Full Text] [Related]
6. Highly Portable, Sensor-Based System for Human Fall Monitoring.
Mao A; Ma X; He Y; Luo J
Sensors (Basel); 2017 Sep; 17(9):. PubMed ID: 28902149
[TBL] [Abstract][Full Text] [Related]
7. Fall classification by machine learning using mobile phones.
Albert MV; Kording K; Herrmann M; Jayaraman A
PLoS One; 2012; 7(5):e36556. PubMed ID: 22586477
[TBL] [Abstract][Full Text] [Related]
8. Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.
Bourke AK; Klenk J; Schwickert L; Aminian K; Ihlen EA; Mellone S; Helbostad JL; Chiari L; Becker C
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():3712-3715. PubMed ID: 28269098
[TBL] [Abstract][Full Text] [Related]
9. An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.
He J; Bai S; Wang X
Sensors (Basel); 2017 Jun; 17(6):. PubMed ID: 28621709
[TBL] [Abstract][Full Text] [Related]
10. Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles.
Saeb S; Cybulski TR; Schueller SM; Kording KP; Mohr DC
J Med Internet Res; 2017 Apr; 19(4):e118. PubMed ID: 28420605
[TBL] [Abstract][Full Text] [Related]
11. Detection of falls using accelerometers and mobile phone technology.
Lee RY; Carlisle AJ
Age Ageing; 2011 Nov; 40(6):690-6. PubMed ID: 21596711
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of accelerometer-based fall detection algorithms on real-world falls.
Bagalà F; Becker C; Cappello A; Chiari L; Aminian K; Hausdorff JM; Zijlstra W; Klenk J
PLoS One; 2012; 7(5):e37062. PubMed ID: 22615890
[TBL] [Abstract][Full Text] [Related]
13. Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.
Alizadeh J; Bogdan M; Classen J; Fricke C
Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770473
[TBL] [Abstract][Full Text] [Related]
14. Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.
Palmerini L; Klenk J; Becker C; Chiari L
Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33202738
[TBL] [Abstract][Full Text] [Related]
15. Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests.
Juneau P; Baddour N; Burger H; Bavec A; Lemaire ED
Sensors (Basel); 2022 Feb; 22(5):. PubMed ID: 35270892
[TBL] [Abstract][Full Text] [Related]
16. Automated Detection of Real-World Falls: Modeled From People With Multiple Sclerosis.
Mosquera-Lopez C; Wan E; Shastry M; Folsom J; Leitschuh J; Condon J; Rajhbeharrysingh U; Hildebrand A; Cameron M; Jacobs PG
IEEE J Biomed Health Inform; 2021 Jun; 25(6):1975-1984. PubMed ID: 33245698
[TBL] [Abstract][Full Text] [Related]
17. Wearable airbag technology and machine learned models to mitigate falls after stroke.
Botonis OK; Harari Y; Embry KR; Mummidisetty CK; Riopelle D; Giffhorn M; Albert MV; Heike V; Jayaraman A
J Neuroeng Rehabil; 2022 Jun; 19(1):60. PubMed ID: 35715823
[TBL] [Abstract][Full Text] [Related]
18. A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees.
Griffiths B; Diment L; Granat MH
Sensors (Basel); 2021 Nov; 21(22):. PubMed ID: 34833534
[TBL] [Abstract][Full Text] [Related]
19. Detecting falls with wearable sensors using machine learning techniques.
Özdemir AT; Barshan B
Sensors (Basel); 2014 Jun; 14(6):10691-708. PubMed ID: 24945676
[TBL] [Abstract][Full Text] [Related]
20. Accuracy of a wavelet-based fall detection approach using an accelerometer and a barometric pressure sensor.
Ejupi A; Galang C; Aziz O; Park EJ; Robinovitch S
Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():2150-2153. PubMed ID: 29060322
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
