## 2 Methods
### Slip Detection
#### 2.3.3 Optical Path Occlusion and Remaining Background Noise
An important advantage of the proposed method is its robustness against changes in the environment and robot arm movements achieved by removing the background of the camera-recorded scene. This feature proves crucial in everyday tasks that take place in diverse and dynamic environments. To further enhance the algorithm's performance, future improvements could focus on parameter optimization and the development of strategies to address challenges related to untextured and transparent objects. Conceptualization, A.A.P., L.N.S.A.S. and L.M. ; methodology, L.M. ; software, L.M. ; validation, L.M. ; formal analysis, L.M. ; investigation, L.M. ; resources, A.A.P. and L.N.S.A.S. ; data curation, L.M. ; writing--original draft preparation, L.M. ; writing--review and editing, A.A.P., L.N.S.A.S. and L.M. ; visualization, L.M. ; supervision, A.A.P. and L.N.S.A.S. ; project administration, L.N.S.A.S. ; funding acquisition, L.N.S.A.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the independent research fund Denmark under project 8022-00234B and the Erasmus+ programme. Not applicable. Data Availability Statement: Data are available on request by contacting the corresponding author and under the sources cited in this paper. The authors declare no conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} OF & Optical Flow \\ OpenCV & Open Computer Vision \\ DoF & Degrees of Freedom \\ TCI & Tongue-Computer Interface \\ RGBd & Red Green Blue Depth \\ DNN & Deep Neural Network \\ CNN & Convolutional Neural Network \\ MS-TCN & Multi-Scale Temporal Convolutional Network \\ LSTM & Long Short-Term Memory \\ MIT & Massachusetts Institute of Technology \\ \end{tabular} 

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