University of Hertfordshire

Study of Gross Muscle Fatigue During Human-Robot Interactions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Study of Gross Muscle Fatigue During Human-Robot Interactions. / Thacham-Poyil, Azeemsha; Amirabdollahian, Farshid; Steuber, Volker.

The Tenth International Conference on Advances in Computer-Human Interactions. ed. / Roy Oberhauser; Jaehyun Park; Steffen Gerhard Scholz; Paul Rosenthal; Ljilja (Lilia) Ruzic Kascak. IARIA, 2017. p. 187-192.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Thacham-Poyil, A, Amirabdollahian, F & Steuber, V 2017, Study of Gross Muscle Fatigue During Human-Robot Interactions. in R Oberhauser, J Park, SG Scholz, P Rosenthal & LL Ruzic Kascak (eds), The Tenth International Conference on Advances in Computer-Human Interactions. IARIA, pp. 187-192, The Tenth International Conference on Advances in Computer-Human Interactions, Nice, France, 19-23 March.

APA

Thacham-Poyil, A., Amirabdollahian, F., & Steuber, V. (2017). Study of Gross Muscle Fatigue During Human-Robot Interactions. In R. Oberhauser, J. Park, S. G. Scholz, P. Rosenthal, & L. . L. Ruzic Kascak (Eds.), The Tenth International Conference on Advances in Computer-Human Interactions (pp. 187-192). IARIA.

Vancouver

Thacham-Poyil A, Amirabdollahian F, Steuber V. Study of Gross Muscle Fatigue During Human-Robot Interactions. In Oberhauser R, Park J, Scholz SG, Rosenthal P, Ruzic Kascak LL, editors, The Tenth International Conference on Advances in Computer-Human Interactions. IARIA. 2017. p. 187-192.

Author

Thacham-Poyil, Azeemsha; Amirabdollahian, Farshid; Steuber, Volker / Study of Gross Muscle Fatigue During Human-Robot Interactions.

The Tenth International Conference on Advances in Computer-Human Interactions. ed. / Roy Oberhauser; Jaehyun Park; Steffen Gerhard Scholz; Paul Rosenthal; Ljilja (Lilia) Ruzic Kascak. IARIA, 2017. p. 187-192.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bibtex

@inbook{ed467f8d88ec4d96ab6fd838831ad732,
title = "Study of Gross Muscle Fatigue During Human-Robot Interactions",
keywords = "Robotic Rehabilitation, Upper Limb Training, Fatigue Detection, Electromyogram, kinematic fatigue indicator",
author = "Azeemsha Thacham-Poyil and Farshid Amirabdollahian and Volker Steuber",
note = "Azeemsha Thacham Poyil, Farshid Amirabdollahian, and Volker Steuber, 'Study of Gross Muscle Fatigue During Human-Robot Interactions'. In Proceedings of the 10th International Conference on Advances in Computer-Human Interactions', Nice, France, 19 -23 March 2017, ISBN: 978-1-61208-538-8. Available online at:http://www.thinkmind.org/index.php?view=article&articleid=achi_2017_9_10_20028. Copyright © IARIA, 2017.",
year = "2017",
month = "3",
isbn = "978-1-61208-538-8",
pages = "187--192",
editor = "Roy Oberhauser and Jaehyun Park and Scholz, {Steffen Gerhard} and Paul Rosenthal and {Ruzic Kascak}, {Ljilja (Lilia)}",
booktitle = "The Tenth International Conference on Advances in Computer-Human Interactions",
publisher = "IARIA",

}

RIS

TY - CHAP

T1 - Study of Gross Muscle Fatigue During Human-Robot Interactions

AU - Thacham-Poyil,Azeemsha

AU - Amirabdollahian,Farshid

AU - Steuber,Volker

N1 - Azeemsha Thacham Poyil, Farshid Amirabdollahian, and Volker Steuber, 'Study of Gross Muscle Fatigue During Human-Robot Interactions'. In Proceedings of the 10th International Conference on Advances in Computer-Human Interactions', Nice, France, 19 -23 March 2017, ISBN: 978-1-61208-538-8. Available online at:http://www.thinkmind.org/index.php?view=article&articleid=achi_2017_9_10_20028. Copyright © IARIA, 2017.

PY - 2017/3/19

Y1 - 2017/3/19

N2 - This study explores the utility of Electromyogram(EMG) signals in the context of upper-limb exercises duringhuman-robot interaction considering muscle fatigue of the participant.We hypothesise that the Electromyogram features frommuscles and kinematic measurements from the robotic sensorscan be used as indicators of fatigue and there is a potentialto identify the muscle contribution during the activity wherethe Electromyogram data is correlated with the kinematic data.Electromyogram measurements were taken from four upper limbmuscles of 10 healthy individuals. HapticMaster robot in activeassisted mode together with a virtual environment was used toguide the participants for moving the robotic arm in a prescribedpath in a horizontal plane consisting of four segments. Theexperiments were conducted until the participants reached a stateof fatigue or until a defined maximum number of 6 trials werereached. Comparing the first and last trials indicated that themuscle fatigue had caused an increase in the average power anda decrease in the median frequency of EMG, which was morevisible in Trapezius (TRP) and Anterior Deltoid (DLT) musclesin most of the analysed cases compared to Biceps Brachii (BB)and Triceps Brachii (TB) muscles. As the muscles came to astate of fatigue, the kinematic position also showed an increasein tracking error between the first and last trials. The ’near-thebody’segment movements (S1 and S4 segments) were found tohave less increase of tracking error compared to the ’away-frombody’movements (S2 and S3 segments). A further analysis onthis proved that the tracking error observed was mainly due tofatigue building up over the number of trials when performing’away-from-body’ movements, and not a bi-product of perceptionerrors. We identify that Deltoid and Trapezius muscles werefatigued more. These EMG fatigue indications can be mapped tokinematic indications of fatigue mainly in the segments S2 and S3,which required away from body movements because of the roleof these two muscles in lifting the arm to the shoulder heightin order to perform the activity. Our extracted features haveshown the potential to identify the fatigued muscles as expected.The study also showed that the Electromyogram and kinematicfeatures have a potential to be used to highlight the extent ofmuscle involvement.

AB - This study explores the utility of Electromyogram(EMG) signals in the context of upper-limb exercises duringhuman-robot interaction considering muscle fatigue of the participant.We hypothesise that the Electromyogram features frommuscles and kinematic measurements from the robotic sensorscan be used as indicators of fatigue and there is a potentialto identify the muscle contribution during the activity wherethe Electromyogram data is correlated with the kinematic data.Electromyogram measurements were taken from four upper limbmuscles of 10 healthy individuals. HapticMaster robot in activeassisted mode together with a virtual environment was used toguide the participants for moving the robotic arm in a prescribedpath in a horizontal plane consisting of four segments. Theexperiments were conducted until the participants reached a stateof fatigue or until a defined maximum number of 6 trials werereached. Comparing the first and last trials indicated that themuscle fatigue had caused an increase in the average power anda decrease in the median frequency of EMG, which was morevisible in Trapezius (TRP) and Anterior Deltoid (DLT) musclesin most of the analysed cases compared to Biceps Brachii (BB)and Triceps Brachii (TB) muscles. As the muscles came to astate of fatigue, the kinematic position also showed an increasein tracking error between the first and last trials. The ’near-thebody’segment movements (S1 and S4 segments) were found tohave less increase of tracking error compared to the ’away-frombody’movements (S2 and S3 segments). A further analysis onthis proved that the tracking error observed was mainly due tofatigue building up over the number of trials when performing’away-from-body’ movements, and not a bi-product of perceptionerrors. We identify that Deltoid and Trapezius muscles werefatigued more. These EMG fatigue indications can be mapped tokinematic indications of fatigue mainly in the segments S2 and S3,which required away from body movements because of the roleof these two muscles in lifting the arm to the shoulder heightin order to perform the activity. Our extracted features haveshown the potential to identify the fatigued muscles as expected.The study also showed that the Electromyogram and kinematicfeatures have a potential to be used to highlight the extent ofmuscle involvement.

KW - Robotic Rehabilitation

KW - Upper Limb Training

KW - Fatigue Detection

KW - Electromyogram

KW - kinematic fatigue indicator

M3 - Conference contribution

SN - 978-1-61208-538-8

SP - 187

EP - 192

BT - The Tenth International Conference on Advances in Computer-Human Interactions

PB - IARIA

ER -