111 related articles for article (PubMed ID: 26738086)
1. Wearable technology and ECG processing for fall risk assessment, prevention and detection.
Melillo P; Castaldo R; Sannino G; Orrico A; de Pietro G; Pecchia L
Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():7740-3. PubMed ID: 26738086
[TBL] [Abstract][Full Text] [Related]
2. Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis.
Castaldo R; Melillo P; Izzo R; De Luca N; Pecchia L
IEEE J Biomed Health Inform; 2017 Mar; 21(2):399-406. PubMed ID: 28113874
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults.
Cella A; De Luca A; Squeri V; Parodi S; Vallone F; Giorgeschi A; Senesi B; Zigoura E; Quispe Guerrero KL; Siri G; De Michieli L; Saglia J; Sanfilippo C; Pilotto A
PLoS One; 2020; 15(6):e0234904. PubMed ID: 32584912
[TBL] [Abstract][Full Text] [Related]
5. A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection.
Shao M; Zhou Z; Bin G; Bai Y; Wu S
Sensors (Basel); 2020 Jan; 20(3):. PubMed ID: 31979184
[TBL] [Abstract][Full Text] [Related]
6. [Impact of fall risk and fear of falling on mobility of independently living senior citizens transitioning to frailty: screening results concerning fall prevention in the community].
Anders J; Dapp U; Laub S; von Renteln-Kruse W
Z Gerontol Geriatr; 2007 Aug; 40(4):255-67. PubMed ID: 17701116
[TBL] [Abstract][Full Text] [Related]
7. Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects.
Melillo P; Jovic A; De Luca N; Pecchia L
Healthc Technol Lett; 2015 Aug; 2(4):89-94. PubMed ID: 26609412
[TBL] [Abstract][Full Text] [Related]
8. A Novel Wearable Device for Continuous Ambulatory ECG Recording: Proof of Concept and Assessment of Signal Quality.
Steinberg C; Philippon F; Sanchez M; Fortier-Poisson P; O'Hara G; Molin F; Sarrazin JF; Nault I; Blier L; Roy K; Plourde B; Champagne J
Biosensors (Basel); 2019 Jan; 9(1):. PubMed ID: 30669678
[TBL] [Abstract][Full Text] [Related]
9. Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers.
Aziz O; Park EJ; Mori G; Robinovitch SN
Gait Posture; 2014; 39(1):506-12. PubMed ID: 24148648
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Monitoring of autonomic response to sociocognitive tasks during treatment in children with Autism Spectrum Disorders by wearable technologies: A feasibility study.
Di Palma S; Tonacci A; Narzisi A; Domenici C; Pioggia G; Muratori F; Billeci L;
Comput Biol Med; 2017 Jun; 85():143-152. PubMed ID: 27080078
[TBL] [Abstract][Full Text] [Related]
13. Application for the wearable heart activity monitoring system: analysis of the autonomic function of HRV.
Yang HK; Lee JW; Lee KH; Lee YJ; Kim KS; Choi HJ; Kim DJ
Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():1258-61. PubMed ID: 19162895
[TBL] [Abstract][Full Text] [Related]
14. Wearable seismocardiography: towards a beat-by-beat assessment of cardiac mechanics in ambulant subjects.
Di Rienzo M; Vaini E; Castiglioni P; Merati G; Meriggi P; Parati G; Faini A; Rizzo F
Auton Neurosci; 2013 Nov; 178(1-2):50-9. PubMed ID: 23664242
[TBL] [Abstract][Full Text] [Related]
15. A Wearable Healthcare System With a 13.7 μA Noise Tolerant ECG Processor.
Izumi S; Yamashita K; Nakano M; Kawaguchi H; Kimura H; Marumoto K; Fuchikami T; Fujimori Y; Nakajima H; Shiga T; Yoshimoto M
IEEE Trans Biomed Circuits Syst; 2015 Oct; 9(5):733-42. PubMed ID: 25423655
[TBL] [Abstract][Full Text] [Related]
16. Detection of Near Falls Using Wearable Devices: A Systematic Review.
Pang I; Okubo Y; Sturnieks D; Lord SR; Brodie MA
J Geriatr Phys Ther; 2019; 42(1):48-56. PubMed ID: 29384813
[TBL] [Abstract][Full Text] [Related]
17. Depth-based human fall detection via shape features and improved extreme learning machine.
Ma X; Wang H; Xue B; Zhou M; Ji B; Li Y
IEEE J Biomed Health Inform; 2014 Nov; 18(6):1915-22. PubMed ID: 25375688
[TBL] [Abstract][Full Text] [Related]
18. Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis?
Mahdiani S; Jeyhani V; Peltokangas M; Vehkaoja A
Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():5948-51. PubMed ID: 26737646
[TBL] [Abstract][Full Text] [Related]
19. Classification between non-multiple fallers and multiple fallers using a triaxial accelerometry-based system.
Liu Y; Redmond SJ; Narayanan MR; Lovell NH
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():1499-502. PubMed ID: 22254604
[TBL] [Abstract][Full Text] [Related]
20. Prediction of foot clearance parameters as a precursor to forecasting the risk of tripping and falling.
Lai DT; Taylor SB; Begg RK
Hum Mov Sci; 2012 Apr; 31(2):271-83. PubMed ID: 21035220
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]