Using A Spiral Approach To Facilitating Engineering Research And Education In Real Industry Settings

Engineering research and education is often done in collaboration with industrial partners through the Industry as Laboratory (IaL), and Challenge-Based Learning (CBL) paradigms. However, its findings are not always adopted, despite the use of well-established and rigorous research methodologies. Academia employs oftentimes extensive and time consuming analyses, while industry operates in smaller cycles with tangible intermediate results. This can lead to the industry losing interest in the research. The Spiral Approach for Systems Engineering Research (SASER) is an approach that aims to mitigate that risk. This can have a twofold benefit in the industry remaining interested, but also the researcher staying motivated. To apply this approach in practice and receive feedback from a broader audience of people we created the SEFI 2023 workshop entitled: “Using a spiral approach to facilitate engineering research and education embedded in real industry settings”. This workshop has the objective of discussing best practices when conducting engineering education and research in collaboration with industry. To achieve the planned learning outcomes, the workshop activities will follow a cycle of learn=\u3eapply=\u3ereflect on provided specific case studies that are developed in order to allow the application of SASER. The workshop was attended by 8 participants that were split into 2 groups of 4 people (the 2nd group further decided to split further into a group of 3 and one individual). The results of the case studies and the reflection of the participants in the workshop indicate a clear potential for SASER and are promising for further research and development

Raw data collected for the study of: Characterization of forearm high-density electromyograms during wrist-hand tasks in individuals with Duchenne Muscular Dystrophy

This dataset contains all the raw data collected for the study: Characterization of forearm high-density electromyograms during wrist-hand tasks in individuals with Duchenne Muscular Dystrophy The data contain raw data from 8 healthy individuals and 3 individuals with Duchenne Muscular Dystrophy. High-density sEMG was recorded from the forearm of the participants during 7 gestures. Those were: 1) Hand Close 2) Hand Open 3) Index Point 4) Thumb Flexion 5) Thumb Extension 6) Wrist Flexion 7) Wrist Extensio

Towards the control of an active hand orthosis for people with Duchenne muscular dystrophy:Design and Validation of a wireless sEMG sleeve

Duchenne Muscular Dystrophy (DMD) is a progressive muscular disease. Active hand orthoses can greatly improve the quality of life of people with DMD. Surface Electromyography (sEMG) is commonly used for the control of active devices. The interfacing between the human and the sensor is regularly done by an adhesive skin interface (sticker). This can cause discomfort, especially during daily use. For forearm sEMG measurements, a sleeve design can solve this problem. The design presented here aims to make sEMG more comfortable, yet functional for daily use. In order to achieve that, we designed a simple, low-cost sEMG sleeve using a commercial ankle brace. 6 equidistant cuts around the circumference of the sleeve were made in order for the sEMG sensors (Delsys Trigno, Delsys Inc.) to be placed. Those are held in place by a number of 3D printed plastic casings mounted with plastic snap buttons. The buttons are used to make the attachment of the casings fast and easy. A preliminary evaluation of the sleeve has been carried out with 6 healthy subjects, using a library of 6 and 9 gestures and a simple artificial neural network (ANN) classifier. The performance was evaluated, in terms of classification time, training time and accuracy (offline) and selection time, completion time, completion rate and accuracy (online). The results show that the performance of the sleeve is not significantly different than the adhesive skin interface. We conclude, that the EMG-sleeve is a better alternative than the current methods for sensor skin interfacing, while having similar performance during a classification task