These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
124 related articles for article (PubMed ID: 23367263)
1. Improving automatic sound-based fall detection using iVAT clustering and GA-based feature selection. Li Y; Popescu M; Ho KC Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():5867-70. PubMed ID: 23367263 [TBL] [Abstract][Full Text] [Related]
2. Acoustic fall detection using a circular microphone array. Li Y; Zeng Z; Popescu M; Ho KC Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():2242-5. PubMed ID: 21096795 [TBL] [Abstract][Full Text] [Related]
3. A microphone array system for automatic fall detection. Li Y; Ho KC; Popescu M IEEE Trans Biomed Eng; 2012 May; 59(5):1291-301. PubMed ID: 22532430 [TBL] [Abstract][Full Text] [Related]
4. A theoretical study on the placement of microphone arrays for improving the localization accuracy of a fall. Li Y; Ho KC; Popescu M; Skubic M Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():4523-6. PubMed ID: 25570997 [TBL] [Abstract][Full Text] [Related]
5. Acoustic fall detection using one-class classifiers. Popescu M; Mahnot A Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():3505-8. PubMed ID: 19964801 [TBL] [Abstract][Full Text] [Related]
6. An acoustic fall detector system that uses sound height information to reduce the false alarm rate. Popescu M; Li Y; Skubic M; Rantz M Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():4628-31. PubMed ID: 19163747 [TBL] [Abstract][Full Text] [Related]
7. Improving acoustic fall recognition by adaptive signal windowing. Li Y; Popescu M; Ho KC; Nabelek DP Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():7589-92. PubMed ID: 22256095 [TBL] [Abstract][Full Text] [Related]
8. Efficient source separation algorithms for acoustic fall detection using a microsoft kinect. Li Y; Ho KC; Popescu M IEEE Trans Biomed Eng; 2014 Mar; 61(3):745-55. PubMed ID: 24235295 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. Improvement of acoustic fall detection using Kinect depth sensing. Li Y; Banerjee T; Popescu M; Skubic M Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():6736-9. PubMed ID: 24111289 [TBL] [Abstract][Full Text] [Related]
11. Fall detection of elderly through floor vibrations and sound. Litvak D; Zigel Y; Gannot I Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():4632-5. PubMed ID: 19163748 [TBL] [Abstract][Full Text] [Related]
12. A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features. Droghini D; Ferretti D; Principi E; Squartini S; Piazza F Comput Intell Neurosci; 2017; 2017():1512670. PubMed ID: 28638405 [TBL] [Abstract][Full Text] [Related]
13. Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch. Casilari E; Oviedo-Jiménez MA PLoS One; 2015; 10(11):e0140929. PubMed ID: 26560737 [TBL] [Abstract][Full Text] [Related]
14. Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety. Almukadi WS; Alrowais F; Saeed MK; Yahya AE; Mahmud A; Marzouk R Sci Rep; 2024 Sep; 14(1):21537. PubMed ID: 39278949 [TBL] [Abstract][Full Text] [Related]
15. Sensitivity and false alarm rate of a fall sensor in long-term fall detection in the elderly. Kangas M; Korpelainen R; Vikman I; Nyberg L; Jämsä T Gerontology; 2015; 61(1):61-8. PubMed ID: 25138139 [TBL] [Abstract][Full Text] [Related]
16. A method for automatic fall detection of elderly people using floor vibrations and sound--proof of concept on human mimicking doll falls. Zigel Y; Litvak D; Gannot I IEEE Trans Biomed Eng; 2009 Dec; 56(12):2858-67. PubMed ID: 19709955 [TBL] [Abstract][Full Text] [Related]
17. Selecting Power-Efficient Signal Features for a Low-Power Fall Detector. Wang C; Redmond SJ; Lu W; Stevens MC; Lord SR; Lovell NH IEEE Trans Biomed Eng; 2017 Nov; 64(11):2729-2736. PubMed ID: 28212076 [TBL] [Abstract][Full Text] [Related]
18. A Low-Power Fall Detector Balancing Sensitivity and False Alarm Rate. Wang C; Lu W; Redmond SJ; Stevens MC; Lord SR; Lovell NH IEEE J Biomed Health Inform; 2018 Nov; 22(6):1929-1937. PubMed ID: 29990072 [TBL] [Abstract][Full Text] [Related]
19. On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices. Arias-Londoño JD; Godino-Llorente JI; Markaki M; Stylianou Y Logoped Phoniatr Vocol; 2011 Jul; 36(2):60-9. PubMed ID: 21073260 [TBL] [Abstract][Full Text] [Related]
20. A Comparative Study of Features for Acoustic Cough Detection Using Deep Architectures Miranda IDS; Diacon AH; Niesler TR Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():2601-2605. PubMed ID: 31946429 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]