227 related articles for article (PubMed ID: 25978764)
1. The accuracy of conventional 2D video for quantifying upper limb kinematics in repetitive motion occupational tasks.
Chen CH; Azari DP; Hu YH; Lindstrom MJ; Thelen D; Yen TY; Radwin RG
Ergonomics; 2015; 58(12):2057-66. PubMed ID: 25978764
[TBL] [Abstract][Full Text] [Related]
2. Automated video exposure assessment of repetitive hand activity level for a load transfer task.
Chen CH; Hu YH; Yen TY; Radwin RG
Hum Factors; 2013 Apr; 55(2):298-308. PubMed ID: 23691826
[TBL] [Abstract][Full Text] [Related]
3. Measuring elemental time and duty cycle using automated video processing.
Akkas O; Lee CH; Hu YH; Yen TY; Radwin RG
Ergonomics; 2016 Nov; 59(11):1514-1525. PubMed ID: 26848051
[TBL] [Abstract][Full Text] [Related]
4. Visualizing stressful aspects of repetitive motion tasks and opportunities for ergonomic improvements using computer vision.
Greene RL; Azari DP; Hu YH; Radwin RG
Appl Ergon; 2017 Nov; 65():461-472. PubMed ID: 28284701
[TBL] [Abstract][Full Text] [Related]
5. The accuracy of a 2D video-based lifting monitor.
Wang X; Hu YH; Lu ML; Radwin RG
Ergonomics; 2019 Aug; 62(8):1043-1054. PubMed ID: 31092146
[TBL] [Abstract][Full Text] [Related]
6. TWO-DIMENSIONAL VIDEO ANALYSIS IS COMPARABLE TO 3D MOTION CAPTURE IN LOWER EXTREMITY MOVEMENT ASSESSMENT.
Schurr SA; Marshall AN; Resch JE; Saliba SA
Int J Sports Phys Ther; 2017 Apr; 12(2):163-172. PubMed ID: 28515970
[TBL] [Abstract][Full Text] [Related]
7. Markerless motion capture using appearance and inertial data.
Wong C; Zhang Z; Lo B; Yang GZ
Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():6907-10. PubMed ID: 25571584
[TBL] [Abstract][Full Text] [Related]
8. Effect of joystick stiffness, movement speed and movement direction on joystick and upper limb kinematics when using hydraulic-actuation joystick controls in heavy vehicles.
Oliver M; Tingley M; Rogers R; Rickards J; Biden E
Ergonomics; 2007 Jun; 50(6):837-58. PubMed ID: 17457745
[TBL] [Abstract][Full Text] [Related]
9. Measuring exertion time, duty cycle and hand activity level for industrial tasks using computer vision.
Akkas O; Lee CH; Hu YH; Harris Adamson C; Rempel D; Radwin RG
Ergonomics; 2017 Dec; 60(12):1730-1738. PubMed ID: 28640656
[TBL] [Abstract][Full Text] [Related]
10. Definition of anatomical zero positions for assessing shoulder pose with 3D motion capture during bilateral abduction of the arms.
Rettig O; Krautwurst B; Maier MW; Wolf SI
BMC Musculoskelet Disord; 2015 Dec; 16():383. PubMed ID: 26646907
[TBL] [Abstract][Full Text] [Related]
11. Use of accelerometers as an ergonomic assessment method for arm acceleration-a large-scale field trial.
Estill CF; MacDonald LA; Wenzl TB; Petersen MR
Ergonomics; 2000 Sep; 43(9):1430-45. PubMed ID: 11014762
[TBL] [Abstract][Full Text] [Related]
12. A hand speed-duty cycle equation for estimating the ACGIH hand activity level rating.
Akkas O; Azari DP; Chen CH; Hu YH; Ulin SS; Armstrong TJ; Rempel D; Radwin RG
Ergonomics; 2015; 58(2):184-94. PubMed ID: 25343278
[TBL] [Abstract][Full Text] [Related]
13. Towards the Use of 2D Video-Based Markerless Motion Capture to Measure and Parameterize Movement During Functional Capacity Evaluation.
Remedios SM; Fischer SL
J Occup Rehabil; 2021 Dec; 31(4):754-767. PubMed ID: 34515942
[TBL] [Abstract][Full Text] [Related]
14. Ubiquitous human upper-limb motion estimation using wearable sensors.
Zhang ZQ; Wong WC; Wu JK
IEEE Trans Inf Technol Biomed; 2011 Jul; 15(4):513-21. PubMed ID: 21659035
[TBL] [Abstract][Full Text] [Related]
15. Application of a symbolic motion structure representation algorithm to identify upper extremity kinematic changes during a repetitive task.
Whittaker RL; Park W; Dickerson CR
J Biomech; 2018 Apr; 72():235-240. PubMed ID: 29523349
[TBL] [Abstract][Full Text] [Related]
16. Evaluation of suitability of a micro-processing unit of motion analysis for upper limb tracking.
Barraza Madrigal JA; Cardiel E; Rogeli P; Leija Salas L; Muñoz Guerrero R
Med Eng Phys; 2016 Aug; 38(8):793-800. PubMed ID: 27185034
[TBL] [Abstract][Full Text] [Related]
17. Accuracy of Kinovea software in estimating body segment movements during falls captured on standard video: Effects of fall direction, camera perspective and video calibration technique.
Shishov N; Elabd K; Komisar V; Chong H; Robinovitch SN
PLoS One; 2021; 16(10):e0258923. PubMed ID: 34695159
[TBL] [Abstract][Full Text] [Related]
18. Risk of upper extremity biomechanical overload in automotive facility.
Sancini A; Capozzella A; Caciar T; Tomei F; Nardone N; Scala B; Fiaschetti M; Cetica C; Scimitto L; Gioffrrè P; Sinibaldi F; Di Pastena C; Corbosiero P; Schifano MP; Tomei G; Ciarrocca M
Biomed Environ Sci; 2013 Jan; 26(1):70-5. PubMed ID: 23294618
[TBL] [Abstract][Full Text] [Related]
19. Comparison of the observer, single-frame video and computer vision hand activity levels.
Radwin RG; Hu YH; Akkas O; Bao S; Harris-Adamson C; Lin JH; Meyers AR; Rempel D
Ergonomics; 2023 Aug; 66(8):1132-1141. PubMed ID: 36227226
[TBL] [Abstract][Full Text] [Related]
20. Reliability of distal upper extremity posture matching using slow-motion and frame-by-frame video methods.
Kociolek AM; Keir PJ
Hum Factors; 2010 Jun; 52(3):441-55. PubMed ID: 21077565
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
