Even Falkenberg Langås

• Langås, Even Falkenberg; Aalerud, Atle; Nyberg, Svein Olav Glesaaen; Zafar, Muhammad Hamza; Hagen, Daniel & Sanfilippo, Filippo (2026). Predictive Human Trajectory Classification in Industrial Settings with Transfer Learning and Data Augmentation. IEEE Access. 14, p. 47071–47084. doi: 10.1109/ACCESS.2026.3677599. Full text in Research Archive Show summary

  The successful realisation of Industry 5.0 relies on seamless collaboration between humans and robots in shared workspaces. This paper investigates predictive human trajectory classification (HTC) in a factory setting to enhance safety, productivity, and adaptability in human-machine environments. Using a state-of-the-art light detection and ranging (LiDAR) system with reflective mirrors to extend the field of view, motion data are collected across a 26 m ×20 m indoor industrial floor. Human trajectories are classified to predict workstation transitions using a stacked long-short term memory (LSTM) network. To address the challenges of limited real-world data, transfer learning is applied by first training the model on synthetically generated trajectories. The paper compares a traditional stochastic method, specifically the Ornstein-Uhlenbeck (OU) process, with a modern learning based method, represented by a timeseries generative adversarial network (TimeGAN). Further on, the algorithm is fine-tuned on the real-world dataset. The comparison shows that in data-scarce scenarios, OU outperforms TimeGAN in most metrics. The LSTM model trained on OU-based data and real-world data shows an overall accuracy gain of 4.8 percentage points compared to training on real-world data only. It is able to classify the goal workstation three seconds in advance with an accuracy of 95.6%, demonstrating the potential for predictive models to improve workplace operations by reducing stall time, optimising workstation readiness, and reducing the need for traditional safety cages.

• Langås, Even Falkenberg; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2025). Exploring the synergy of human-robot teaming, digital twins, and machine learning in Industry 5.0: a step towards sustainable manufacturing. Journal of Intelligent Manufacturing. ISSN 0956-5515. 37, p. 99–1022. doi: 10.1007/s10845-025-02580-x. Full text in Research Archive Show summary

  Sustainable manufacturing remains a central objective of Industry 5.0. By successfully implementing harmonic human-robot teams in intelligent industrial systems, the efficiency and well-being of human workers can be increased. Achieving this requires a gradual approach from caged robots to advanced, seamless collaboration between humans and robots. Initially, that means transitioning to human-robot interaction (HRI) where there is an exchange of commands between the human and the robot. Further advancements within safety considerations, including collision avoidance through advanced machine vision, enable the exchange of workspace that defines human-robot collaboration (HRC). The next stage is physical HRC (pHRC) which requires safe and controlled exchange of forces through impedance and admittance control. Finally, this paper describes human-robot teaming (HRT), which is defined by the exchange of solutions as teammates. This is enabled by combining cutting-edge technologies such as digital twin (DT), advanced vision sensors, machine learning (ML) algorithms and mixed reality (MR) human–machine interfaces for operators. A key contribution of this work is reviewing the integration of HRT with DT and ML, highlighting how these technologies enable seamless perception, prediction, and decision-making in human-centric industrial systems. By reviewing these technologies, the paper highlights current challenges, limitations and research gaps within the field of HRT and suggests potential future possibilities for HRT, such as advanced disassembly of used goods for a more sustainable manufacturing industry.

• Zafar, Muhammad Hamza; Langås, Even Falkenberg; Aftab, Muhammad Faisal & Sanfilippo, Filippo (2024). Enhanced Intrusion Detection in Robot Operating Systems via Grid Search Based Multi-Head Attention Stacked Convolutional Network. IEEE International Conference on Automation Science and Engineering. ISSN 2161-8070. p. 3880–3885. doi: 10.1109/CASE59546.2024.10711785. Full text in Research Archive
• Langås, Even Falkenberg; Zafar, Muhammad Hamza; Nyberg, Svein Olav Glesaaen & Sanfilippo, Filippo (2024). Human Trajectory Simulation in Industrial Settings Using the Ornstein-Uhlenbeck Process and Deep Learning Based Classification. In NN, . (Eds.), International Conference on Automation, Robotics and Applications (ICARA). Institute of Electrical and Electronics Engineers (IEEE). ISSN 9798350394252. p. 427–432. doi: 10.1109/ICARA60736.2024.10553211. Full text in Research Archive Show summary

  This paper presents a novel method of simulating human trajectories using the Ornstein-Uhlenbeck (OU) process in addition to deep learning (DL) based classification. The OU process is a stochastic process and is used in this paper to simulate the movement of a person on a typical factory floor. This work aims at developing systems that increase machines' awareness of people and make predictions about their behaviour to improve efficiency and safety in industrial settings. Sequences of simulated 2D coordinates of people moving on the factory floor are generated. Successively, these synthetic data are used to classify the path that the human is following, using a stacked long short-term memory (LSTM) network and a stacked bidirectional LSTM (BiLSTM) network. The results from this study suggest that, for such applications, it should be possible to predict future movements in 2D for human-robot collaboration (HRC) and teaming (HRT).

• Zafar, Muhammad Hamza; Langås, Even Falkenberg & Sanfilippo, Filippo (2024). Real-Time Gesture-Based Control of a Quadruped Robot Using a Stacked Convolutional Bi-Long Short-Term Memory (Bi-LSTM) Neural Network. In NN, . (Eds.), International Conference on Automation, Robotics and Applications (ICARA). Institute of Electrical and Electronics Engineers (IEEE). ISSN 9798350394252. p. 81–86. doi: 10.1109/ICARA60736.2024.10553163. Full text in Research Archive
• Zafar, Muhammad Hamza; Langås, Even Falkenberg; Nyberg, Svein Olav Glesaaen & Sanfilippo, Filippo (2024). Multimodal Fusion of EEG and EMG Signals Using Self-Attention Multi-Temporal Convolutional Neural Networks for Enhanced Hand Gesture Recognition in Rehabilitation, International Conference on Omni-layer Intelligent Systems (COINS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350349597. p. 245–249. doi: 10.1109/COINS61597.2024.10622144. Full text in Research Archive Show summary

  In this work, we introduce an innovative approach to hand gesture recognition aimed at rehabilitation applications, utilising the synergistic potential of multimodal data fusion from electroencephalogram (EEG) and electromyogram (EMG) sensors. Our approach exploits the strength of Self-Attention Multi-Temporal Convolutional Networks (SAMTCN), which adeptly combine the distinct and complementary insights provided by EEG and EMG signals. The core of our methodology is the strategic application of self-attention mechanisms with multi-temporal convolutional architectures. This design choice allows our model to capture and analyse temporal patterns in multimodal data with unprecedented precision, significantly enhancing its ability to generalise to new, unseen data. The effectiveness of our approach is evidenced by the model's exceptional performance, achieving an accuracy of over 97% in recognising diverse hand gestures. This high level of accuracy highlights the model's potential to revolutionise how interactions are facilitated in rehabilitation contexts.

• Langås, Even Falkenberg; Bukhari, Halima Zahra; Hagen, Daniel; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2024). Inclusive Digital Twins with Edge Computing, Cloud Communication and Virtual Reality to Achieve Remote Human-Robot Interaction. In NN, . (Eds.), 12th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331517519. p. 323–329. doi: 10.1109/ICCMA63715.2024.10843896. Full text in Research Archive Show summary

  Digital twins, advanced robotics, edge computing, and immersive technologies have come together to create novel solutions that increase flexibility and operating efficiency. This work is motivated by the need to harness these advancements to develop a robust architecture that supports edge intelligence and real-time remote monitoring and control. The aim of this work is to define a comprehensive framework for digital twins of complex mechatronic systems. The framework enables seamless connection between the physical environment and a virtual representation accessible from remote locations. Key components of the framework include the Robot Operating System 2 (ROS 2) for robot control, Node-RED for data processing and edge communication, a Message Queuing Telemetry Transport (MQTT) broker for cloud-based communication, and a virtual reality (VR) application for immersive interaction. A case study is presented to demonstrate the framework’s capabilities. Through the VR application, users can interact with a digital twin of a mechatronic system consisting of a collaborative robot, a programmable logic controller (PLC), a conveyor belt and numerous sensors. The user interface lets the operator manipulate the robot and monitor sensor data in real-time. Latency is measured to validate the performance of the framework, resulting in a mean latency of around 116 ms.

• 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. 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). 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.

• 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 (Institute of Electrical and Electronics Engineers). ISSN 9781665430654. p. 1794–1799. doi: 10.1109/SSCI52147.2023.10372032. Full text in Research Archive Show summary

  This paper delves into the ethical, philosophical, and practical dimensions associated with the transition from caged robots to human-robot teaming (HRT). By exploring the evolving dynamics between humans and robots, this paper examines the ethical challenges, philosophical implications, and practical considerations that arise as collaboration and integration between humans and robots deepen. It emphasises the need for responsible design, implementation, and ethical frameworks to guide the development and deployment of human-robot teams. Particular focus is put into the ethical ramifications of choosing between rigid and soft actuators. The study underscores the significance of employing admittance and impedance control techniques to regulate interaction forces and compliance between humans and robots. By analysing the ethical implications of utilising soft actuators, the paper emphasises the potential advantages, such as enhanced safety and reduced risk of harm during close human-robot collaboration.

• 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 (Institute of Electrical and Electronics Engineers). ISSN 9781665430654. p. 1788–1793. doi: 10.1109/SSCI52147.2023.10372069. Full text in Research Archive Show summary

  In the era of Industry 5.0, the convergence of humans and robots in collaborative work environments has brought forth the concept of digital twins (DTs) of humans and robots. These virtual replicas, mirroring their physical counterparts, have become integral to the design and operation of complex systems. This paper aims to explore the ethical and philosophical implications associated with the design and use of DTs of humans and robots in human-robot collaboration (HRC), and even further in human-robot teaming (HRT). By examining the potential benefits, challenges, and risks, this research seeks to shed light on the responsible development and application of DTs in the context of Industry 5.0.

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