Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements

Luca Tagliapietra, Michele Vivian, Massimo Sartori, Dario Farina, Monica Reggiani

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

A main challenge in the development of robotic rehabilitation devices is how to understand patient's intentions and adapt to his/her current neuro-physiological capabilities. A promising approach is the use of electromyographic (EMG) signals which reflect the actual activation of the muscles during the movement and, thus, are a direct representation of user's movement intention. However, EMGs acquisition is a complex procedure, requiring trained therapists and, therefore, solutions based on EMG signals are not easily integrable in devices for home-rehabilitation. This work investigates the effectiveness of a subject- and task-specific EMG model in estimating EMG signals in cyclic plantar-dorsiflexion movements. Then, the outputs of this model are used to drive CEINMS toolbox, a state-of-the-art EMG-driven neuromusculoskeletal model able to predict joint torques and muscle forces. Preliminary results show that the proposed methodology preserves the accuracy of the estimates values.

Original languageEnglish
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherIEEE
Pages3611-3614
Number of pages4
Volume2015-November
ISBN (Electronic)9781424492718
DOIs
Publication statusPublished - 4 Nov 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: 25 Aug 201529 Aug 2015
Conference number: 37

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Abbreviated titleEMBC
CountryItaly
CityMilan
Period25/08/1529/08/15

Fingerprint

Patient rehabilitation
Rehabilitation
Equipment and Supplies
Muscles
Muscle
Torque
Robotics
Joints
Chemical activation
Drive

Cite this

Tagliapietra, L., Vivian, M., Sartori, M., Farina, D., & Reggiani, M. (2015). Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (Vol. 2015-November, pp. 3611-3614). [7319174] IEEE. https://doi.org/10.1109/EMBC.2015.7319174
Tagliapietra, Luca ; Vivian, Michele ; Sartori, Massimo ; Farina, Dario ; Reggiani, Monica. / Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November IEEE, 2015. pp. 3611-3614
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Tagliapietra, L, Vivian, M, Sartori, M, Farina, D & Reggiani, M 2015, Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. vol. 2015-November, 7319174, IEEE, pp. 3611-3614, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 25/08/15. https://doi.org/10.1109/EMBC.2015.7319174

Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements. / Tagliapietra, Luca; Vivian, Michele; Sartori, Massimo; Farina, Dario; Reggiani, Monica.

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November IEEE, 2015. p. 3611-3614 7319174.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Tagliapietra L, Vivian M, Sartori M, Farina D, Reggiani M. Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November. IEEE. 2015. p. 3611-3614. 7319174 https://doi.org/10.1109/EMBC.2015.7319174