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Mamikoglu, Umut
Publications (2 of 2) Show all publications
Mamikoglu, U., Nikolakopoulos, G., Pauelsen, M., Varagnolo, D., Röijezon, U. & Gustafsson, T. (2016). Elbow Joint Angle Estimation by Using Integrated Surface Electromyography (ed.). In: (Ed.), 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016. Paper presented at Mediterranean Conference on Control and Automation : 21/06/2016 - 24/06/2016 (pp. 785-790). Piscataway, NJ: IEEE Communications Society, Article ID 7535891.
Open this publication in new window or tab >>Elbow Joint Angle Estimation by Using Integrated Surface Electromyography
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2016 (English)In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 785-790, article id 7535891Conference paper (Refereed)
Abstract [en]

Electromyography (EMG) signals represent the electrical activation of skeletal muscles and contain valuable information about muscular activity. Estimation of the joint movements by using surface EMG signals has great importance as a bio-inspired approach for the control of robotic limbs and prosthetics. However interpreting surface EMG measurements is challenging due to the nonlinearity and user dependency of the muscle dynamics. Hence it requires complex computational methods to map the EMG signals and corresponding limb motions. To solve this challenge we here propose to use an integrated EMG signal to identify the EMG-joint angle relation instead of using common EMG processing techniques. Then we estimate the joint angles for elbow flexion-extension movement by using an auto-regressive integrated moving average with exogenous input (ARIMAX) model, which takes integrated EMG measurements as input. The experiments showed that the suggested approach results in a 21.85% average increase in the estimation performance of the elbow joint angle compared to the standard EMG processing and identification.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2016
Series
Mediterranean Conference on Control and Automation, E-ISSN 2325-369X
Keywords
Information technology - Automatic control, Informationsteknik - Reglerteknik
National Category
Control Engineering Physiotherapy
Research subject
Control Engineering; Physiotherapy
Identifiers
urn:nbn:se:ltu:diva-35118 (URN)10.1109/MED.2016.7535891 (DOI)000391154900131 ()2-s2.0-84986193000 (Scopus ID)9862c70d-adec-409a-b5b4-43d7ec14fad7 (Local ID)978-1-4673-8345-5 (ISBN)9862c70d-adec-409a-b5b4-43d7ec14fad7 (Archive number)9862c70d-adec-409a-b5b4-43d7ec14fad7 (OAI)
Conference
Mediterranean Conference on Control and Automation : 21/06/2016 - 24/06/2016
Note

Godkänd; 2016; 20160419 (geonik)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Mamikoglu, U., Andrikopoulos, G., Nikolakopoulos, G., Röijezon, U., Pauelsen, M. & Gustafsson, T. (2016). Electromyography Based Joint Angle Estimation and Control of a Robotic Leg (ed.). In: (Ed.), 6th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2016): June 26-29, Singapore, 2016. Paper presented at IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics : 26/06/2016 - 29/06/2016 (pp. 182-187). Piscataway, NJ: IEEE Communications Society, Article ID 7523619.
Open this publication in new window or tab >>Electromyography Based Joint Angle Estimation and Control of a Robotic Leg
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2016 (English)In: 6th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2016): June 26-29, Singapore, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 182-187, article id 7523619Conference paper, Published paper (Refereed)
Abstract [en]

Musculoskeletal modeling based on Electromyography (EMG) has many applications in physiotherapy and biologically-inspired robotics. In this article, a novel methodology for the modeling of the dynamics of an antagonistic muscle pair that actuates the human ankle joint movements will be established. As it will be presented, the musculoskeletal model is based on a multi input single output (MISO) auto-regressive integrated moving average with exogenous input (ARIMAX) model, which takes the integrated EMG measurements as input and estimates the corresponding joint angles. Based on this methodology, a Pneumatic Artificial Muscle (PAM) robotic leg setup that mimics the flexion/extension movement of human ankle joint is controlled to replicate the human movement. The experimental results demonstrate the performance of EMG based joint angle estimation and control of the robotic leg with the proposed model.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2016
Series
Proceedings of the IEEE RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, ISSN 2155-1782
National Category
Control Engineering Physiotherapy
Research subject
Control Engineering; Physiotherapy
Identifiers
urn:nbn:se:ltu:diva-37878 (URN)10.1109/BIOROB.2016.7523619 (DOI)000392266900030 ()2-s2.0-84983379589 (Scopus ID)c0c5b0d8-35b7-412e-8529-e7298dca57f9 (Local ID)978-1-4673-8345-5 (ISBN)c0c5b0d8-35b7-412e-8529-e7298dca57f9 (Archive number)c0c5b0d8-35b7-412e-8529-e7298dca57f9 (OAI)
Conference
IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics : 26/06/2016 - 29/06/2016
Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-12-18Bibliographically approved
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