Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology

Chris T.M. Baten; Hendrik J. Hamberg; Peter H. Veltink; Hermie J. Hermens

Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology

Chris T.M. Baten^*, Hendrik J. Hamberg, Peter H. Veltink, Hermie J. Hermens

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

A new calibration method is proposed for ambulatory systems for low back load exposure estimation based on surface EMG and kinematic data. The method uses an artificial neural network to learn the relation between compressive force in the intervertebral disc at L₄-L₅ (C) and Smoothed Rectified surface EMG signal (SRE) under full dynamic conditions. In vivo tests show that a accurate calibration is possible selecting a training set of 600 samples out of 2 minutes of calibration data. This offers load exposure estimation sensitive to unknown time-varying external loads, compensated for force-length and force-velocity relationships and compensated for inter-individual load handling differences.

Original language	English
Title of host publication	17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publisher	IEEE
Pages	829-830
Number of pages	2
Edition	1
Publication status	Published - 1995
Externally published	Yes

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publisher	Alliance for Engineering in Medicine and Biology
Number	17
ISSN (Print)	0589-1019

Cite this

Baten, C. T. M., Hamberg, H. J., Veltink, P. H., & Hermens, H. J. (1995). Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology. In 17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (1 ed., pp. 829-830). (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; No. 17). IEEE.

Baten, Chris T.M. ; Hamberg, Hendrik J. ; Veltink, Peter H. et al. / Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology. 17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 1. ed. IEEE, 1995. pp. 829-830 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; 17).

@inproceedings{353ede00dd2342318b517e431f48a029,

title = "Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology",

abstract = "A new calibration method is proposed for ambulatory systems for low back load exposure estimation based on surface EMG and kinematic data. The method uses an artificial neural network to learn the relation between compressive force in the intervertebral disc at L4-L5 (C) and Smoothed Rectified surface EMG signal (SRE) under full dynamic conditions. In vivo tests show that a accurate calibration is possible selecting a training set of 600 samples out of 2 minutes of calibration data. This offers load exposure estimation sensitive to unknown time-varying external loads, compensated for force-length and force-velocity relationships and compensated for inter-individual load handling differences.",

author = "Baten, {Chris T.M.} and Hamberg, {Hendrik J.} and Veltink, {Peter H.} and Hermens, {Hermie J.}",

year = "1995",

language = "English",

series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

publisher = "IEEE",

number = "17",

pages = "829--830",

booktitle = "17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

address = "United States",

edition = "1",

}

Baten, CTM, Hamberg, HJ, Veltink, PH & Hermens, HJ 1995, Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology. in 17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 1 edn, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, no. 17, IEEE, pp. 829-830.

Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology. / Baten, Chris T.M.; Hamberg, Hendrik J.; Veltink, Peter H. et al.
17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 1. ed. IEEE, 1995. p. 829-830 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; No. 17).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology

AU - Baten, Chris T.M.

AU - Hamberg, Hendrik J.

AU - Veltink, Peter H.

AU - Hermens, Hermie J.

PY - 1995

Y1 - 1995

N2 - A new calibration method is proposed for ambulatory systems for low back load exposure estimation based on surface EMG and kinematic data. The method uses an artificial neural network to learn the relation between compressive force in the intervertebral disc at L4-L5 (C) and Smoothed Rectified surface EMG signal (SRE) under full dynamic conditions. In vivo tests show that a accurate calibration is possible selecting a training set of 600 samples out of 2 minutes of calibration data. This offers load exposure estimation sensitive to unknown time-varying external loads, compensated for force-length and force-velocity relationships and compensated for inter-individual load handling differences.

AB - A new calibration method is proposed for ambulatory systems for low back load exposure estimation based on surface EMG and kinematic data. The method uses an artificial neural network to learn the relation between compressive force in the intervertebral disc at L4-L5 (C) and Smoothed Rectified surface EMG signal (SRE) under full dynamic conditions. In vivo tests show that a accurate calibration is possible selecting a training set of 600 samples out of 2 minutes of calibration data. This offers load exposure estimation sensitive to unknown time-varying external loads, compensated for force-length and force-velocity relationships and compensated for inter-individual load handling differences.

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M3 - Conference contribution

T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SP - 829

EP - 830

BT - 17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

PB - IEEE

ER -

Baten CTM, Hamberg HJ, Veltink PH, Hermens HJ. Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology. In 17th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 1 ed. IEEE. 1995. p. 829-830. (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; 17).

Calibration of low back load exposure estimation through surface EMG signals with the use of artificial neural network technology

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