DETECTING AND PREDICTING MUSCLE FATIGUE DURING TYPING BY SEMG SIGNAL PROCESSING AND ARTIFICIAL NEURAL NETWORKS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

GHOOCHANI ELHAM; RAHATI GHOOCHANI SAEED; RAVARI MOHAMMAD; HOSEYNI HOSSEIN ASGHAR

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF MEDICAL PHYSICS Year:2011 | Volume:8 | Issue:1 (30) Page(s): 31-40

Download Full-Text

Persian Verion

View:

1,696

Download:

Cites:

Information Journal Paper

Title

DETECTING AND PREDICTING MUSCLE FATIGUE DURING TYPING BY SEMG SIGNAL PROCESSING AND ARTIFICIAL NEURAL NETWORKS

Author(s)

GHOOCHANI ELHAM | RAHATI GHOOCHANI SAEED | RAVARI MOHAMMAD | HOSEYNI HOSSEIN ASGHAR | Issue Writer Certificate

Keywords

MUSCLE FATIGUEQ3

SEMGQ2

WAVELET COEFFICIENTSQ2

MLP NEURAL NETWORKQ2

Abstract

Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause MUSCLE FATIGUE. MUSCLE FATIGUE in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG) signals are used for detecting MUSCLE FATIGUE as a non-invasive method.Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to MUSCLE FATIGUE during typing. After signal analysis and feature extraction, detecting and predicting MUSCLE FATIGUE was performed by using the MLP artificial neural network.Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP NEURAL NETWORK can detect and predict MUSCLE FATIGUE during typing with 80.79%±1.04% accuracy.Conclusion: Intelligent classification and prediction of MUSCLE FATIGUE can have many applications in human factors engineering (ergonomics), rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

Cites

No record.

References

No record.

Cite

APA: Copy

GHOOCHANI, ELHAM, RAHATI GHOOCHANI, SAEED, RAVARI, MOHAMMAD, & HOSEYNI, HOSSEIN ASGHAR. (2011). DETECTING AND PREDICTING MUSCLE FATIGUE DURING TYPING BY SEMG SIGNAL PROCESSING AND ARTIFICIAL NEURAL NETWORKS. IRANIAN JOURNAL OF MEDICAL PHYSICS, 8(1 (30)), 31-40. SID. https://sid.ir/paper/96994/en

Vancouver: Copy

GHOOCHANI ELHAM, RAHATI GHOOCHANI SAEED, RAVARI MOHAMMAD, HOSEYNI HOSSEIN ASGHAR. DETECTING AND PREDICTING MUSCLE FATIGUE DURING TYPING BY SEMG SIGNAL PROCESSING AND ARTIFICIAL NEURAL NETWORKS. IRANIAN JOURNAL OF MEDICAL PHYSICS[Internet]. 2011;8(1 (30)):31-40. Available from: https://sid.ir/paper/96994/en

IEEE: Copy

ELHAM GHOOCHANI, SAEED RAHATI GHOOCHANI, MOHAMMAD RAVARI, and HOSSEIN ASGHAR HOSEYNI, “DETECTING AND PREDICTING MUSCLE FATIGUE DURING TYPING BY SEMG SIGNAL PROCESSING AND ARTIFICIAL NEURAL NETWORKS,” IRANIAN JOURNAL OF MEDICAL PHYSICS, vol. 8, no. 1 (30), pp. 31–40, 2011, [Online]. Available: https://sid.ir/paper/96994/en

Related Journal Papers