Even Falkenberg Langås

• Zafar, Muhammad Hamza; Langås, Even Falkenberg & Sanfilippo, Filippo (2024). Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of-the-art review. Robotics and Computer-Integrated Manufacturing. ISSN 0736-5845. 89. doi: 10.1016/j.rcim.2024.102769.
• Langås, Even Falkenberg; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2023). Harnessing digital twins for human-robot teaming in industry 5.0: Exploring the ethical and philosophical implications. In Yu, Wen (Eds.), 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE conference proceedings. ISSN 978-1-6654-3065-4. p. 1788–1793. doi: https:/doi.org/10.1109/SSCI52147.2023.10372069. Full text in Research Archive
• Hua, Tuan; Langås, Even Falkenberg; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2023). From rigid to hybrid/soft robots: Exploration of ethical and philosophical aspects in shifting from caged robots to human-robot teaming. In Yu, Wen (Eds.), 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE conference proceedings. ISSN 978-1-6654-3065-4. p. 1794–1799. doi: %2010.1109/SSCI52147.2023.10372032. Full text in Research Archive
• Zafar, Muhammad Hamza; Langås, Even Falkenberg & Sanfilippo, Filippo (2023). Empowering human-robot interaction using sEMG sensor: Hybrid deep learning model for accurate hand gesture recognition. Results in Engineering (RINENG). ISSN 2590-1230. 20. doi: 10.1016/j.rineng.2023.101639. Full text in Research Archive Show summary

  In this paper, a novel approach using a Henry Gas Solubility-based Stacked Convolutional Neural Network (HGS-SCNN) for hand gesture recognition using surface electromyography (sEMG) sensors is proposed. The stacked architecture of the CNN model helps to capture both low-level and high-level features, enabling effective representation learning. To begin, we generated a dataset comprising 600 samples of hand gestures. Next, we applied the Discrete Wavelet Transform (DWT) technique to extract features from the filtered sEMG signal. This step allowed us to capture both spatial and frequency information, enhancing the discriminative power of the extracted features. Extensive experiments are conducted to evaluate the performance of the proposed HGS-SCNN model. In addition, the obtained results are compared with state-of-the-art techniques, namely AOA-SCNN, GWO-SCNN, and WOA-SCNN. The comparative analysis demonstrates that the HGS-SCNN outperforms these existing methods, achieving an impressive accuracy of 99.3%. The experimental results validate the effectiveness of our proposed approach in accurately detecting hand gestures. The combination of DWT-based feature extraction and the HGS-SCNN model offers robust and reliable hand gesture recognition, thereby opening new possibilities for intuitive human-machine interaction and applications requiring gesture-based control.

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