Literature DB >> 10661695

Comparison between using spectral analysis of electrogoniometer data and observational analysis to quantify repetitive motion and ergonomic changes in cyclical industrial work.

T Y Yen1, R G Radwin.   

Abstract

Spectral analysis of continuously measured joint angles using an electrogoniometer was considered as a potentially efficient method for quantifying exposure to physical stress in repetitive manual work. The method was previously demonstrated in the laboratory but has not yet been tested extensively in the field. Spectral analysis was compared against observational analysis, consisting of time-and-motion study and posture classification. Six industrial jobs were selected: (1) press operation, (2) large parts hanging, (3) product packaging, (4) small parts hanging, (5) parts counting and sorting and (6) construction vehicle operation. The posture angle data were synchronized with activities on the video using an interactive multimedia video data acquisition system. Motion for every joint was analyzed using both spectral analysis and observational analysis. Joint angles for the wrist, elbow and shoulder were directly measured using electrogoniometers. Visual posture classification involved determining joint angles from a frozen videotape image sampled three times per s. Repetitiveness was quantified for observational analysis using time study to measure the frequency that specific motions repeat, while spectral analysis measured repetitiveness as the frequency where spectral peaks occurred. Spectral analysis agreed closely with observational analysis. Correlation between the repetition frequencies obtained using time study and spectral analysis was 0.97, with no statistically significant difference observed. Average sustained posture was quantified as the mean, and posture deviation as the RMS angle of joint motion. No statistically significant differences between data obtained using posture classification or spectral analysis were observed for either posture deviation or sustained posture. Since posture classification was very limited in resolution and often contained measurement errors caused by poor joint visibility, the correlation between the postural classification and spectral analysis was 0.77 for sustained posture and 0.53 for posture deviation. When considering only large motions that exceeded the posture classification angle precision, the correlation between postural classification and spectral analysis was 0.81 for sustained posture and 0.81 for posture deviation. Spectral analysis of electrogoniometer data were, therefore, an efficient method for analyzing repetitive manual work that obtained equivalent results, and was more precise than observational analysis.

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Year:  2000        PMID: 10661695     DOI: 10.1080/001401300184684

Source DB:  PubMed          Journal:  Ergonomics        ISSN: 0014-0139            Impact factor:   2.778


  6 in total

1.  The accuracy of conventional 2D video for quantifying upper limb kinematics in repetitive motion occupational tasks.

Authors:  Chia-Hsiung Chen; David P Azari; Yu Hen Hu; Mary J Lindstrom; Darryl Thelen; Thomas Y Yen; Robert G Radwin
Journal:  Ergonomics       Date:  2015-06-18       Impact factor: 2.778

2.  A hand speed-duty cycle equation for estimating the ACGIH hand activity level rating.

Authors:  Oguz Akkas; David P Azari; Chia-Hsiung Eric Chen; Yu Hen Hu; Sheryl S Ulin; Thomas J Armstrong; David Rempel; Robert G Radwin
Journal:  Ergonomics       Date:  2014-10-24       Impact factor: 2.778

3.  Automated video exposure assessment of repetitive hand activity level for a load transfer task.

Authors:  Chia-Hsiung Chen; Yu Hen Hu; Thomas Y Yen; Robert G Radwin
Journal:  Hum Factors       Date:  2013-04       Impact factor: 2.888

4.  Universal goniometer and electro-goniometer intra-examiner reliability in measuring the knee range of motion during active knee extension test in patients with chronic low back pain with short hamstring muscle.

Authors:  MohammadBagher Shamsi; Maryam Mirzaei; Seyyed Saeed Khabiri
Journal:  BMC Sports Sci Med Rehabil       Date:  2019-03-22

5.  New generation of wearable goniometers for motion capture systems.

Authors:  Alessandro Tognetti; Federico Lorussi; Gabriele Dalle Mura; Nicola Carbonaro; Maria Pacelli; Rita Paradiso; Danilo De Rossi
Journal:  J Neuroeng Rehabil       Date:  2014-04-11       Impact factor: 4.262

6.  Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life.

Authors:  Alessandro Tognetti; Federico Lorussi; Nicola Carbonaro; Danilo de Rossi
Journal:  Sensors (Basel)       Date:  2015-11-11       Impact factor: 3.576

  6 in total

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