University of Twente Student Theses


Improvement of OpenSim model for the estimation of lumbar torques via EMG-driven modelling

Krishnakumar, Sanchana (2020) Improvement of OpenSim model for the estimation of lumbar torques via EMG-driven modelling.

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Abstract:Lower-back problems are one of the major causes of work-related injuries in manual material handling industries. Back support exoskeletons (BSE) are used to aid workers during manual handling activities. EMG controlled BSE utilize surface electromyography to control the exoskeleton. Control strategies of these BSE can be realized by understanding the underlying user movement mechanics with help of musculoskeletal modelling. Most full-body models in OpenSim do not include the object that is being lifted therefore making it difficult to account for external hand forces during lifting tasks. Objective: This paper aims to develop a method to account for accelerations and rotational torques from the external object to improve modelling. Methods: We proposed an indirect methodology to include external hand forces during inverse dynamics (ID)computations in OpenSim. The proposed methodology was tested for both symmetric and asymmetric box lifting tasks for 3 weight conditions (1.2, 6.2, 16.2kg)and L5-S1 torques were estimated using ID and EMG-driven modelling approaches. Results: The new methodology improved L5-S1 peak ID torque estimates and the largest increase was 23.8Nm for 16.2kg. ANOVA indicated significant differences between peak torque estimates between both methods (p<0.05). The resulting ID torques were used to calibrate the EMG driven model (CEINMS). CEINMS torques were also compared against the respective L5-S1 ID torques. Results indicated a good correlation (r2 > 0.89) and low RMSE (5.97-21Nm) between both ID and CEINMS estimates. The proposed methodology represents a valid approach to include external object forces to estimate realistic L5S1 joint torques during lifting activities via ID and EMG driven modelling approaches.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:50 technical science in general
Programme:Biomedical Engineering MSc (66226)
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