scholarly journals Ergonomic Evaluation by Surface Electromyography: Case Study in The Automotive Industry

2019 ◽  
Vol 14 (5) ◽  
pp. 239-261
Author(s):  
Eugenio Andrés Díaz Merino ◽  
Lincoln Silva ◽  
Julia Marina Cunha ◽  
Ilandir Ferreira da Silva ◽  
Giselle Schmidt Alves Díaz Merino
Biofeedback ◽  
2010 ◽  
Vol 38 (4) ◽  
pp. 155-157
Author(s):  
Thomas R Caffrey ◽  
Robert Clasby

Abstract This case study reports on the use of surface electromyography (SEMG) evaluation in a work environment, including production, to show a relationship between muscle dysfunction and specific job tasks and their injury potential. The results show that SEMG can help identify discordant muscle activity as part of an ergonomic evaluation. Such an evaluation leads to improvement in muscle function through SEMG-guided worker/workplace retraining.


2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.


2021 ◽  
Vol 4 (2) ◽  
pp. 32
Author(s):  
Heather A. Feldner ◽  
Christina Papazian ◽  
Keshia M. Peters ◽  
Claire J. Creutzfeldt ◽  
Katherine M. Steele

Arm recovery varies greatly among stroke survivors. Wearable surface electromyography (sEMG) sensors have been used to track recovery in research; however, sEMG is rarely used within acute and subacute clinical settings. The purpose of this case study was to describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant’s perceptions of sEMG monitoring. Beginning three days post-stroke, one stroke survivor wore five wireless sEMG sensors on his involved arm for three to four hours, every one to three days. Muscle activity was tracked during routine care in the acute setting through discharge from inpatient rehabilitation. Three- and eight-month follow-up sessions were completed in the community. Activity logs were completed each session, and a semi-structured interview occurred at the final session. The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation. While barriers in equipment and signal quality still exist, capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.


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