Table_1_Winter School on sEMG Signal Processing: An Initiative to Reduce Educational Gaps and to Promote the Engagement of Physiotherapists and Movement Scientists With Science.DOCX
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
The application of surface electromyography (sEMG) in neurology is sometimes limited by a scientific background in the use of sEMG. Students frequently use sEMG only when developing their graduate studies. To reduce these barriers, we promoted a free Winter School on sEMG to Latin American students. The school was a 3-day event with theoretical classes and computer programming in Matlab. Lectures were delivered in Portuguese and Spanish to 50 participants. All lectures were recorded and made available on YouTube®. After the School, participants completed a written exam to receive a certificate. The written exam revealed the average effectiveness of 71 ± 20% in the comprehension of topics addressed during the school. Participants rated the School as “excellent” and considered the event as having changed their thoughts about the use of sEMG. Limited mathematical skills or background were the main barriers identified to follow the lectures and to make use of sEMG. We conclude that the Winter School had a positive impact on participant's formation, especially by showing them the importance of continuous involvement with the concepts related to sEMG to become proficient in its use. From the participant's point of view, the activity was excellent and the follow up of the school on YouTube® suggests that combining face-to-face activities followed by the online availability of lectures is a valid strategy to reinforce the learning process and to reduce barriers in the use of sEMG. Whether similar results would be achieved for a paid registration event in an economically developing region, still requires further investigation.
Read the peer-reviewed publication