188 related articles for article (PubMed ID: 28667903)
1. Gait parameter and event estimation using smartphones.
Pepa L; Verdini F; Spalazzi L
Gait Posture; 2017 Sep; 57():217-223. PubMed ID: 28667903
[TBL] [Abstract][Full Text] [Related]
2. Validation of a smartphone-based measurement tool for the quantification of level walking.
Furrer M; Bichsel L; Niederer M; Baur H; Schmid S
Gait Posture; 2015 Sep; 42(3):289-94. PubMed ID: 26141906
[TBL] [Abstract][Full Text] [Related]
3. A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly.
Micó-Amigo ME; Kingma I; Ainsworth E; Walgaard S; Niessen M; van Lummel RC; van Dieën JH
J Neuroeng Rehabil; 2016 Apr; 13():38. PubMed ID: 27093956
[TBL] [Abstract][Full Text] [Related]
4. Heel and toe clearance estimation for gait analysis using wireless inertial sensors.
Mariani B; Rochat S; Büla CJ; Aminian K
IEEE Trans Biomed Eng; 2012 Nov; 59(11):3162-8. PubMed ID: 22955865
[TBL] [Abstract][Full Text] [Related]
5. Estimation of Temporal Gait Events from a Single Accelerometer Through the Scale-Space Filtering Idea.
González I; Fontecha J; Hervás R; Bravo J
J Med Syst; 2016 Dec; 40(12):251. PubMed ID: 27714561
[TBL] [Abstract][Full Text] [Related]
6. Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements.
Pacini Panebianco G; Bisi MC; Stagni R; Fantozzi S
Gait Posture; 2018 Oct; 66():76-82. PubMed ID: 30170137
[TBL] [Abstract][Full Text] [Related]
7. Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels.
Ebara T; Azuma R; Shoji N; Matsukawa T; Yamada Y; Akiyama T; Kurihara T; Yamada S
J Occup Health; 2017 Nov; 59(6):506-512. PubMed ID: 28835575
[TBL] [Abstract][Full Text] [Related]
8. Smartphone-based gait and balance accelerometry is sensitive to age and correlates with clinical and kinematic data.
Olsen S; Rashid U; Allerby C; Brown E; Leyser M; McDonnell G; Alder G; Barbado D; Shaikh N; Lord S; Niazi IK; Taylor D
Gait Posture; 2023 Feb; 100():57-64. PubMed ID: 36481647
[TBL] [Abstract][Full Text] [Related]
9. Development and validity of methods for the estimation of temporal gait parameters from heel-attached inertial sensors in younger and older adults.
Misu S; Asai T; Ono R; Sawa R; Tsutsumimoto K; Ando H; Doi T
Gait Posture; 2017 Sep; 57():295-298. PubMed ID: 28686998
[TBL] [Abstract][Full Text] [Related]
10. Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket.
Silsupadol P; Teja K; Lugade V
Gait Posture; 2017 Oct; 58():516-522. PubMed ID: 28961548
[TBL] [Abstract][Full Text] [Related]
11. Experimental evaluation of a smartphone based Step Length estimation.
Pepa L; Verdini F; Spalazzi L
Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():7647-50. PubMed ID: 26738063
[TBL] [Abstract][Full Text] [Related]
12. Smartphone-Based Assessment of Gait During Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environments.
Silsupadol P; Prupetkaew P; Kamnardsiri T; Lugade V
IEEE J Biomed Health Inform; 2020 Apr; 24(4):1188-1195. PubMed ID: 31329138
[TBL] [Abstract][Full Text] [Related]
13. Heel strike detection using split force-plate treadmill.
Rouhani H; Abe MO; Nakazawa K; Popovic MR; Masani K
Gait Posture; 2015 Mar; 41(3):863-6. PubMed ID: 25800003
[TBL] [Abstract][Full Text] [Related]
14. Validity and Reliability of Smartphone App for Evaluating Postural Adjustments during Step Initiation.
da Costa Moraes AA; Duarte MB; Ferreira EV; da Silva Almeida GC; da Rocha Santos EG; Pinto GHL; de Oliveira PR; Amorim CF; Cabral ADS; de Athayde Costa E Silva A; Souza GS; Callegari B
Sensors (Basel); 2022 Apr; 22(8):. PubMed ID: 35458920
[TBL] [Abstract][Full Text] [Related]
15. Design and Validation of a Biofeedback Device to Improve Heel-to-Toe Gait in Seniors.
Vadnerkar A; Figueiredo S; Mayo NE; Kearney RE
IEEE J Biomed Health Inform; 2018 Jan; 22(1):140-146. PubMed ID: 28186914
[TBL] [Abstract][Full Text] [Related]
16. Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study.
Finkbiner MJ; Gaina KM; McRandall MC; Wolf MM; Pardo VM; Reid K; Adams B; Galen SS
J Vis Exp; 2017 Mar; (121):. PubMed ID: 28362382
[TBL] [Abstract][Full Text] [Related]
17. Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson's disease and Huntington's disease subjects.
Trojaniello D; Ravaschio A; Hausdorff JM; Cereatti A
Gait Posture; 2015 Sep; 42(3):310-6. PubMed ID: 26163348
[TBL] [Abstract][Full Text] [Related]
18. Good agreement between smart device and inertial sensor-based gait parameters during a 6-min walk.
Proessl F; Swanson CW; Rudroff T; Fling BW; Tracy BL
Gait Posture; 2018 Jul; 64():63-67. PubMed ID: 29859414
[TBL] [Abstract][Full Text] [Related]
19. Comparison of inertial records during anticipatory postural adjustments obtained with devices of different masses.
Antunes da Costa Moraes A; Brito Duarte M; Veloso Ferreira E; Cristina da Silva Almeida G; Dos Santos Cabral A; de Athayde Costa E Silva A; Rosa Garcez D; Silva Souza G; Callegari B
PeerJ; 2023; 11():e15627. PubMed ID: 37456867
[TBL] [Abstract][Full Text] [Related]
20. Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke.
Isho T; Tashiro H; Usuda S
J Stroke Cerebrovasc Dis; 2015 Jun; 24(6):1305-11. PubMed ID: 25881773
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
