Deep and Surface Sensor Modalities for Myo-intent Detection

Electromyography is the gold-standard among sensors for prosthetic control. However, stable and reliable myocontrol remains an unsolved problem in the community. Amid improvements currently under investigation, one focuses on alternative or complementary sensors. In this study, we compare different techniques, recording surface and deep muscle activity. Ten subjects were involved in an experiment in which three different modalities were attached on their forearm: force myography, electro-impedance tomography and ultrasound. They were asked to perform wrist and grasp movements. For the first time, we evaluate and compare in an offline analysis these three different modalities while recording several hand gestures

Use of artificial intelligence and neural networks for analysis and gesture detection in electrical impedance tomography

Artificial intelligence and neural networks are getting more and more relevant for several types of application. The field of prosthesis technology currently uses electromyography for controllable prosthesis. The precision of the control suffers from the use of EMG. More precise and more collected data with the help of EIT allows a much more precise analysis and control of the prosthesis. In this paper a neural network for gesture detection using EIT is developed and presented in a user-friendly way

Teaching low-power design with an FPGA-based hands-on and remote lab

This paper describes a project carried out at the Bonn-Rhein-Sieg University to teach low-power digital design structures in a laboratory. Low-power design has become one of the most essential constraints in digital systems, especially in portable devices. For this purpose, low-power design is a topic addressed in electrical and computer curricula, but it also requires applications in a laboratory. Therefore, this project focuses on preparing students for current trends in low-power design by examining realistic applications of its basic theories and principles, either hands-on or remotely. The laboratory's experiments use a field programmable gate array (FPGA) as a design platform for implementing the students' digital designs. This contribution reports on our first experiences in teaching low-power design in the lab. This paper focuses on the educational impact on students regarding the following: (1) overall objectives in the laboratory, (2) experimental exercises with low-power techniques, and (3) using educational FPGA boards and the EduPow board. The EduPow board is a developed hands-on board at the Bonn-Rhein-Sieg University that is relatively specific on using various signal image-processing applications to directly observe the power dissipation of a student's digital algorithm. Our assessment of the low-power design lab shows that the requirements and objectives of this project are fairly well satisfied. In addition, students' feedback indicates that using the EduPow board is more attractive and motivational in their work

Transmission of Vital Data into the German Electronic Health Record

Patient’s vital data will have an important role in future medicine. Best way to store this data is the patient’s electronic health record (EHR) in a structured form. In Germany, several laws, regulations and standards must be considered to store vital data in the health record, like SGB V, DIGAV and Medical Information Objects. In the near future, it will be possible to upload self-recorded vital data to the German EHR special smartphone apps. It will also be possible to automatically evaluate this data using machine learning

SkillsLab+ - Digitization of the Otoscopy Station through State of the Art Methods of Augmented Reality

The paper discusses SkillsLab+, a medical augmented reality application developed to digitize the practical course SkillsLab. This application provides medical students with a digital tool to learn and apply practical hand movements, initial treatment, and diagnostic techniques. The otoscopy station, one of the stations in SkillsLab+, allows students to learn the correct use of otoscope inserts and perform case discrimination. The digitization of this station provides several advantages over traditional teaching methods. The implementation of the application with the Unreal Engine on the Magic Leap One headset is discussed. The application is modular, allowing the addition of new stations with ease. Anatomically correct models and 3D models of diagnostic tools provide the necessary realism and precision for the application. Learning and exam modes have been included in the application to enhance the learning experience. The evaluation of the application was conducted using a questionnaire completed by 79 bachelor and master students from the fields of medical informatics and digital biomedicine and health sciences. On average, 54.25% of students had a positive response to the digital otoscopy station in the SkillsLab+ application, while 22.71% had a negative response. The questionnaire results indicated that the digitization of the otoscopy station through augmented reality in the SkillsLab+ application was well-received by the students. The SkillsLab+ application digitizes the practical course SkillsLab and offers several advantages over traditional teaching methods. It provides students with a digital tool to learn and apply practical hand movements, initial treatment, and diagnostic techniques. The otoscopy station in SkillsLab+ allows students to learn the correct use of otoscope inserts and perform case discrimination. The digitization of this station provides students with a digital tool that can be used anywhere, any number of times. The evaluation of the application using a questionnaire showed that the application was well received by the students