In this project, the landing position of a sandbag is to be predicted with the help of AI. To do this, a data set is recorded with the ElaasticNode attached to the lower arm. This measures the acceleration with a sensor. The landing position is evaluated via a camera. A data set can be built from this. A neural network is then to be trained on this data set. This neural network will then be transferred to the ElasticNode using the ElasticAi.Creator and evaluated.

This results in the following points that can be worked on:

• Preparation and execution of the data set recording
• Finding the best model for landing position prediction
• Designing an extension board for the ElasticNode with a different sensor
• Local training on the ElasticNode to adapt to the respective user

Organisation:

Attendance at the kick-off meeting is mandatory for participation in this project.

This project is a continuation of the "Exoskeletons" project from previous semesters. This semester, the focus will be shifted from controlling an exoskeleton to a gaming controller.

Starting this semester, students will build an EEG processing pipeline, for example to navigate a robot through the maze (up, down, left and right) in a game. The goal of this semester is to realize a first pipeline which includes the following:

• Setting up the environment including the game for automated data set generation with the MentaLab EEG setup and a controller
• Optimization of pre-processing methods
• Initial training of deep neural networks

In the next semesters, the model will be transferred to our ElasticAI.hardware, which will act as a gaming contoller. The data sets will probably have to be automatically adjusted to achieve greater accuracy. The hardware as a gaming controller must be adapted to the operating system and the firmware of the device.

Attendance in the kickoff meeting is mandatory for participation in this project.