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.
244 related articles for article (PubMed ID: 26132221)
1. Validity of a Smartphone-Based Fall Detection Application on Different Phones Worn on a Belt or in a Trouser Pocket. Vermeulen J; Willard S; Aguiar B; De Witte LP Assist Technol; 2015; 27(1):18-23. PubMed ID: 26132221 [TBL] [Abstract][Full Text] [Related]
2. Validation of smartphone step count algorithm used in STARFISH smartphone application. Dybus A; Paul L; Wyke S; Brewster S; Gill JMR; Ramsay A; Campbell E Technol Health Care; 2017 Dec; 25(6):1157-1162. PubMed ID: 28946599 [TBL] [Abstract][Full Text] [Related]
3. A smart phone-based pocket fall accident detection, positioning, and rescue system. Kau LJ; Chen CS IEEE J Biomed Health Inform; 2015 Jan; 19(1):44-56. PubMed ID: 25486656 [TBL] [Abstract][Full Text] [Related]
4. Accelerometer-based goniometer for smartphone and manual measurement on photographs: do they agree? Ferriero G; Vercelli S; Sartorio F; Foti C Biomed Tech (Berl); 2014 Dec; 59(6):549-50. PubMed ID: 24992014 [No Abstract] [Full Text] [Related]
5. Activity recognition with smartphone support. Guiry JJ; van de Ven P; Nelson J; Warmerdam L; Riper H Med Eng Phys; 2014 Jun; 36(6):670-5. PubMed ID: 24641812 [TBL] [Abstract][Full Text] [Related]
6. Portable preimpact fall detector with inertial sensors. Wu G; Xue S IEEE Trans Neural Syst Rehabil Eng; 2008 Apr; 16(2):178-83. PubMed ID: 18403286 [TBL] [Abstract][Full Text] [Related]
7. A comparison of activity classification in younger and older cohorts using a smartphone. Del Rosario MB; Wang K; Wang J; Liu Y; Brodie M; Delbaere K; Lovell NH; Lord SR; Redmond SJ Physiol Meas; 2014 Nov; 35(11):2269-86. PubMed ID: 25340659 [TBL] [Abstract][Full Text] [Related]
8. Inertial sensing-based pre-impact detection of falls involving near-fall scenarios. Lee JK; Robinovitch SN; Park EJ IEEE Trans Neural Syst Rehabil Eng; 2015 Mar; 23(2):258-66. PubMed ID: 25252283 [TBL] [Abstract][Full Text] [Related]
9. Multimodal sensor-based fall detection within the domestic environment of elderly people. Feldwieser F; Gietzelt M; Goevercin M; Marschollek M; Meis M; Winkelbach S; Wolf KH; Spehr J; Steinhagen-Thiessen E Z Gerontol Geriatr; 2014 Dec; 47(8):661-5. PubMed ID: 25112402 [TBL] [Abstract][Full Text] [Related]
10. Fall-detection solution for mobile platforms using accelerometer and gyroscope data. De Cillisy F; De Simioy F; Guidoy F; Incalzi RA; Setolay R Annu Int Conf IEEE Eng Med Biol Soc; 2015 Aug; 2015():3727-30. PubMed ID: 26737103 [TBL] [Abstract][Full Text] [Related]
11. Validity of activity trackers, smartphones, and phone applications to measure steps in various walking conditions. Höchsmann C; Knaier R; Eymann J; Hintermann J; Infanger D; Schmidt-Trucksäss A Scand J Med Sci Sports; 2018 Jul; 28(7):1818-1827. PubMed ID: 29460319 [TBL] [Abstract][Full Text] [Related]
12. Human fall detection on embedded platform using depth maps and wireless accelerometer. Kwolek B; Kepski M Comput Methods Programs Biomed; 2014 Dec; 117(3):489-501. PubMed ID: 25308505 [TBL] [Abstract][Full Text] [Related]
13. Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope. Wang FT; Chan HL; Hsu MH; Lin CK; Chao PK; Chang YJ Physiol Meas; 2018 Oct; 39(10):105002. PubMed ID: 30207983 [TBL] [Abstract][Full Text] [Related]
14. 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]
15. Unconstrained detection of freezing of Gait in Parkinson's disease patients using smartphone. Kim H; Lee HJ; Lee W; Kwon S; Kim SK; Jeon HS; Park H; Shin CW; Yi WJ; Jeon BS; Park KS Annu Int Conf IEEE Eng Med Biol Soc; 2015 Aug; 2015():3751-4. PubMed ID: 26737109 [TBL] [Abstract][Full Text] [Related]
16. Fall detection in homes of older adults using the Microsoft Kinect. Stone EE; Skubic M IEEE J Biomed Health Inform; 2015 Jan; 19(1):290-301. PubMed ID: 24733032 [TBL] [Abstract][Full Text] [Related]
17. 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]
18. Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors. Shoaib M; Bosch S; Incel OD; Scholten H; Havinga PJ Sensors (Basel); 2016 Mar; 16(4):426. PubMed ID: 27023543 [TBL] [Abstract][Full Text] [Related]
19. Sensitivity and specificity of fall detection in people aged 40 years and over. Kangas M; Vikman I; Wiklander J; Lindgren P; Nyberg L; Jämsä T Gait Posture; 2009 Jun; 29(4):571-4. PubMed ID: 19153043 [TBL] [Abstract][Full Text] [Related]
20. Unobtrusive monitoring and identification of fall accidents. van de Ven P; O'Brien H; Nelson J; Clifford A Med Eng Phys; 2015 May; 37(5):499-504. PubMed ID: 25769224 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]