Our publications - ROBIN

• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2023). Toward cultures of rhythm in legged robots. In Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Ed.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press. ISSN 0000000000000. doi: https:/doi.org/10.1162/isal_a_00673. Full text in Research Archive
• Veenstra, Frank; Szorkovszky, Alexander & Glette, Kyrre (2023). Decentralized Control and Morphological Evolution of 2D Virtual Creatures . In Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Ed.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press. ISSN 0000000000000. doi: https:/doi.org/10.1162/isal_a_00656.
• Woll, Anita & Tørresen, Jim (2023). What is a Smart Hospital? A Review of the Literature. In Duffy, V.G.; Lehto, M.; Yih, Y. & Proctor, R.W. (Ed.), Human-Automation Interaction. Springer. ISSN 978-3-031-10780-1. p. 145–165.
• Sajadi, Seyed Mohammad Reza; Mathiassen, Kim; Brun, Henrik & Elle, Ole Jakob (2022). Design, Kinematic Modeling, and Validation of a Robotic-Assisted Transesophageal Echocardiography System, 2022 IEEE/SICE International Symposium on System Integration (SII 2022) : January 9-12, 2022 (Virtual Conference) Narvik, Norway. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-4540-5. doi: 10.1109/SII52469.2022.9708810.
• Mantelli, Mathias; Noori, Farzan Majeed; Pittol, Diego; Maffei, Renan; Tørresen, Jim & Kolberg, Mariana (2022). Semantic Temporal Object Search System Based on Heat Maps. Journal of Intelligent and Robotic Systems. ISSN 0921-0296. 106(69). doi: 10.1007/s10846-022-01760-8.
• Veenstra, Frank; Olsen, Martin Herring & Glette, Kyrre (2022). Effects of Encodings and Quality-diversity on Evolving 2D Virtual Creatures, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). ISSN 9781450392686. p. 164–167. doi: https:/doi.org/10.1145/3520304.3529053.
• Bordvik, David Andreas; Hou, Jie; Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2022). Monitoring In-Home Emergency Situation and Preserve Privacy using Multi-modal Sensing and Deep Learning, 2022 International Conference on Electronics, Information, and Communication (ICEIC 2022) . IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665409353. doi: 10.1109/ICEIC54506.2022.9748829. Full text in Research Archive Show summary

  Videos and images are commonly used in home monitoring systems. However, detecting emergencies in-home while preserving privacy is a challenging task concerning Human Activity Recognition (HAR). In recent years, HAR combined with deep learning has drawn much attention from the general public. Besides that, relying entirely on a single sensor modal-ity is not promising. In this paper, depth images and radar presence data were used to investigate if such sensor data can tackle the challenge of a system's ability to detect abnormal and normal situations while preserving privacy. The recurrence plots and wavelet transformations were used to make a two-dimensional representation of the presence radar data. Moreover, we fused data from both sensors using data-level, feature-level, and decision-level fusions. The decision-level fusion showed its superiority over the other two techniques. For the decision-level fusion, a combination of the depth images and presence data recurrence plots trained first on convolutional neural networks (CNN). The output was fed into support vector machines, which yielded the best accuracy of 99.98%.

• Lopez, Diego A.; Lopez, Manuel A.; Munoz, Dario S.; Santa, Jesus A.; Gomez, David F. & Barone, Dante Augusto Couto [Show all 8 contributors for this article] (2022). Controlling the UR3 Robotic Arm Using a Leap Motion: A Comparative Study. In Ribei, Paulo Rogério de Almeida; Cota, Vinícius Rosa; Barone, Dante Augusto Couto & de Oliveira, Alexandre César Muniz (Ed.), Computational Neuroscience: Conference Proceedings Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers. Springer Nature. ISSN 978-3-031-08443-0. p. 64–77. doi: https:/doi.org/10.1007/978-3-031-08443-0_5.
• Nergård, Katrine Linnea; Endestad, Tor & Tørresen, Jim (2022). Effect of Hand Dominance When Decoding Motor Imagery Grasping Tasks. In Ribei, Paulo Rogério de Almeida; Cota, Vinícius Rosa; Barone, Dante Augusto Couto & de Oliveira, Alexandre César Muniz (Ed.), Computational Neuroscience: Conference Proceedings Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers. Springer Nature. ISSN 978-3-031-08443-0. p. 233–249. doi: https:/doi.org/10.1007/978-3-031-08443-0_15.
• Norstein, Emma Stensby; Ellefsen, Kai Olav & Glette, Kyrre (2022). Open-Ended Search for Environments and Adapted Agents Using MAP-Elites, Applications of Evolutionary Computation. EvoApplications 2022.. Springer. ISSN 978-3-031-02461-0. p. 651–666. doi: 10.1007/978-3-031-02462-7_41.
• Potel, Pierre; Veenstra, Frank & Glette, Kyrre (2021). SoloJam Island : a platform for rhythmic experimentation in spatially segmented multi-agent systems. In Kantan, Prithvi Ravi; Paisa, Razvan & Willemsen, Silvin (Ed.), Proceedings of the 2nd Nordic Sound and Music Computing (NordicSMC) Conference. Aalborg Universitetsforlag. doi: 10.5281/zenodo.5717860. Full text in Research Archive
• Nordmoen, Jørgen; Nygaard, Tønnes Frostad; Samuelsen, Eivind & Glette, Kyrre (2021). On Restricting Real-Valued Genotypes in Evolutionary Algorithms. In Castillo, Pedro A. & Jiménez Laredo, Juan Luiz (Ed.), Applications of Evolutionary Computation. Springer. ISSN 978-3-030-72699-7. p. 3–16. doi: 10.1007/978-3-030-72699-7_1. Full text in Research Archive
• Tariverdi, Abbas; Venkiteswaran, Venkatasubramanian Kalpathy; Richter, Michiel; Elle, Ole Jakob; Tørresen, Jim & Mathiassen, Kim [Show all 7 contributors for this article] (2021). A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators. Frontiers in Robotics and AI. ISSN 2296-9144. doi: 10.3389/frobt.2021.631303. Full text in Research Archive
• Hosseinpoor, Sadegh; Mantelli, Mathias; Pittol, Diego; Kolberg, Mariana; Maffei, Renan & Prestes, Edson [Show all 7 contributors for this article] (2021). Traversability Analysis by Semantic Terrain Segmentation for Mobile Robots. In Xie, Xiaolan & Dauzère-Pérès, Stéphane (Ed.), Proceedings of the 17th International Conference on Automation Science and Engineering (CASE2021). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-7381-2502-2. p. 1407–1413. doi: 10.1109/CASE49439.2021.9551629.
• Khaksar, Weria; Neggers, Margot; Barakova, Emilia Ivanova & Tørresen, Jim (2021). Generation Differences in Perception of the Elderly Care Robot. In Caleb-Solly, Praminda; Ghafurian, Moojan; Johnson, Michelle & Wada, Kazuyoshi (Ed.), Proceedings of the 30th IEEE International Conference on Robot & Human Interactive Communication (ROMAN). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-0492-1. p. 551–558. doi: 10.1109/RO-MAN50785.2021.9515534.
• van Otterdijk, Marieke; Neggers, Margot; Tørresen, Jim & Barakova, Emilia Ivanova (2021). Preferences of Seniors for Robots Delivering a Message With Congruent Approaching Behavior, Proceedings of the 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) Virtual Conference, July 8-10, 2021. IEEE Robotics and Automation Society. ISSN 978-1-6654-4952-6. doi: 10.1109/ARSO51874.2021.9542833. Show summary

  Congruence of verbal and nonverbal behavior of a robot is essential to establish a seamless and natural interaction between humans and robots. We have investigated the preference and user experience of the senior participants that a robot approaches with expressive movement that is tailored or incongruent to the message the robot brings. Twelve elderly experienced three scenarios with different messages varying from good news, bad news, and functional news, accompanied by the corresponding approaching behavior and body posture. The user experience of these elderly was evaluated during these scenarios using a questionnaire and a semi-structured interview. The participants were interviewed about their experience, preference, and motivation for the different robot motion behaviors. The analysis showed no significant difference in user experiences in the good news and functional news scenarios. Also, no clear preference among the different robot behaviors was found. However, a significant preference for congruence in the sad news scenario was found with a clear preference for the sad approaching behavior.

• Nordmoen, Jørgen Halvorsen; Veenstra, Frank; Ellefsen, Kai Olav & Glette, Kyrre (2021). MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics. Frontiers in Robotics and AI. ISSN 2296-9144. 8. doi: 10.3389/frobt.2021.639173. Full text in Research Archive Show summary

  In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.

• Nygaard, Tønnes; Howard, David & Glette, Kyrre (2020). Real world morphological evolution is feasible. In Coello Coello, Carlos A. (Eds.), GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). ISSN 978-1-4503-7127-8. p. 1392–1394. doi: 10.1145/3377929.3398095. Show summary

  Evolutionary algorithms offer great promise for the automatic design of robot bodies, tailoring them to specific environments or tasks. Most research is done on simplified models or virtual robots in physics simulators, which do not capture the natural noise and richness of the real world. Very few of these virtual robots are built as physical robots, and the few that are will rarely be further improved in the actual environment they operate in, limiting the effectiveness of the automatic design process. We utilize our shape-shifting quadruped robot, which allows us to optimize the design in its real-world environment. The robot is able to change the length of its legs during operation, and is robust enough for complex experiments and tasks. We have co-evolved control and morphology in several different scenarios, and have seen that the algorithm is able to exploit the dynamic morphology solely through real-world experiments.

• Nordmoen, Jørgen Halvorsen; Veenstra, Frank; Ellefsen, Kai Olav & Glette, Kyrre (2020). Quality and Diversity in Evolutionary Modular Robotics, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2547-3. p. 2109–2116. doi: 10.1109/SSCI47803.2020.9308511.
• Barnes, Chloe M; Ekárt, Anikó; Ellefsen, Kai Olav; Glette, Kyrre; Lewis, Peter R. & Tørresen, Jim (2020). Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks, 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7278-1. p. 129–138. doi: 10.1109/ACSOS49614.2020.00034.
• Ionescu, Bogdan; Müller, Henning; Péteri, Renaud; Abacha, Asma Ben; Datla, Vivek & Hasan, Sadid A. [Show all 33 contributors for this article] (2020). Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12260, p. 311–341. doi: 10.1007/978-3-030-58219-7_22. Full text in Research Archive
• Uddin, Md Zia; Noori, Farzan Majeed & Tørresen, Jim (2020). In-Home Emergency Detection Using an Ambient Ultra-Wideband Radar Sensor and Deep Learning. In Noori, Farzan Majeed (Eds.), IEEE 10th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7314-6. p. 1089–1093. doi: 10.1109/UkrMW49653.2020.9252708. Show summary

  Human behavior analysis in indoor environments from ambient sensor data is one of the most challenging human-machine interaction research topics. In this paper, we present a deep learning-based novel approach for human posture recognition and emergency detection using an ultra-wideband radar sensor. The strength of this sensor is that it collects less privacy-related information than a regular camera. The sensor is installed on a mobile robot to observe a subject from a short distance. At first, raw data is collected from the sensor. Then, the data is used to train a Recurrent Neural Network (RNN) for modeling of different human activities and conditions, including normal and emergency conditions. Finally, the trained model is used for testing the model on new inputs. The experiments were performed using two datasets recorded in lab environments, and the proposed approach produced better recognition performance than the conventional ones. The proposed method can be applied in many prominent research fields (e.g., human-robot interaction) for different practical applications such as mobile robots for eldercare.

• Jakobsen, Petter; Garcia-Ceja, Enrique; Riegler, Michael; Stabell, Lena Antonsen; Nordgreen, Tine & Tørresen, Jim [Show all 8 contributors for this article] (2020). Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls. PLOS ONE. ISSN 1932-6203. 15:e0231995(8), p. 1–16. doi: 10.1371/journal.pone.0231995. Full text in Research Archive Show summary

  Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.

• Martin, Charles Patrick; Glette, Kyrre; Nygaard, Tønnes & Tørresen, Jim (2020). Understanding Musical Predictions With an Embodied Interface for Musical Machine Learning. Frontiers in Artificial Intelligence. ISSN 2624-8212. 3(6). doi: 10.3389/frai.2020.00006. Show summary

  Machine-learning models of music often exist outside the worlds of musical performance practice and abstracted from the physical gestures of musicians. In this work, we consider how a recurrent neural network (RNN) model of simple music gestures may be integrated into a physical instrument so that predictions are sonically and physically entwined with the performer's actions. We introduce EMPI, an embodied musical prediction interface that simplifies musical interaction and prediction to just one dimension of continuous input and output. The predictive model is a mixture density RNN trained to estimate the performer's next physical input action and the time at which this will occur. Predictions are represented sonically through synthesized audio, and physically with a motorized output indicator. We use EMPI to investigate how performers understand and exploit different predictive models to make music through a controlled study of performances with different models and levels of physical feedback. We show that while performers often favor a model trained on human-sourced data, they find different musical affordances in models trained on synthetic, and even random, data. Physical representation of predictions seemed to affect the length of performances. This work contributes new understandings of how musicians use generative ML models in real-time performance backed up by experimental evidence. We argue that a constrained musical interface can expose the affordances of embodied predictive interactions.

• Saplacan, Diana (2020). Situated Ability: A Case from Higher Education on Digital Learning Environments. In Antona, M. & Stepahnidis, C. (Ed.), Universal Access in Human-Computer Interaction, Applications and Practice. Springer Nature. ISSN 978-3-030-49107-9. p. 256–274. doi: 10.1007/978-3-030-49108-6_19. Show summary

  Universal Design (UD) is often associated with disability studies. However, UD is not about disabilities, but about designing for as many people as possible. Traditionally, disability studies are discussed through the lens of medical, relational, social, or socio-relational models. This paper proposes a new salutogenic approach instead, namely the concept of situated ability. Based on the work of Aaron Antonovsky and the salutogenic approach of ease/dis-ease model and his Sense-of-Coherence (SOC) theoretical construct, the paper proposes and discusses situated ability and the ability continuum. Situated ability is sugggested as a form of catalyzing discussions around social equity in a digital society. The proposed concept is supported with examples from an empirical qualitative study on Digital Learning Environments (DLE) used in Higher Education (HE). The empirical data was collected through interviews, and analyzed using Systematic Text Condensation (STC). The findings are discussed through the lens of the proposed concepts. Finally, the paper argues that a such perspective is needed for maintaining the dignity of others, i.e., the users who experience lower situated abilities when interacting with digital systems; for fostering salutogenic discussions about practice and design that enables users, without focusing on dedicated solutions for the lower abled, such as assistive technologies; and for social equity in a digital society.

• Woll, Anita & Tørresen, Jim (2020). Bridging the User Barriers of Home Telecare. Advances in Intelligent Systems and Computing. ISSN 2194-5357. 1069, p. 816–835. doi: 10.1007/978-3-030-32520-6_59.
• Ahmadi, Arash; Till, Katharina; Hafting, Yngve; Schuttpelz, Mark; Bjørås, Magnar & Glette, Kyrre [Show all 9 contributors for this article] (2019). Additive manufacturing of laminar flow cells for single-molecule experiments. Scientific Reports. ISSN 2045-2322. 9(1). doi: 10.1038/s41598-019-53151-z. Full text in Research Archive
• Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Ellefsen, Kai Olav; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2019). Experiences from Real-World Evolution with DyRET: Dynamic Robot for Embodied Testing, Nordic Artificial Intelligence Research and Development: Third Symposium of the Norwegian AI Society, NAIS 2019. Springer. ISSN 978-3-030-35664-4. p. 58–68. doi: https:/doi.org/10.1007/978-3-030-35664-4_6.
• Ellefsen, Kai Olav & Tørresen, Jim (2019). Self-adapting Goals Allow Transfer of Predictive Models to New Tasks, Nordic Artificial Intelligence Research and Development: Third Symposium of the Norwegian AI Society, NAIS 2019. Springer. ISSN 978-3-030-35664-4. p. 28–39. doi: https:/doi.org/10.1007/978-3-030-35664-4_3.
• Becker, Artur; Herrebrøden, Henrik; Gonzalez Sanchez, Victor Evaristo; Nymoen, Kristian; Dal Sasso Freitas, Carla Maria & Tørresen, Jim [Show all 7 contributors for this article] (2019). Functional Data Analysis of Rowing Technique Using Motion Capture Data. In Coleman, Grisha (Eds.), Proceedings of the 6th International Conference on Movement and Computing. ACM Publications. ISSN 978-1-4503-7654-9. doi: 10.1145/3347122.3347135. Show summary

  We present an approach to analyzing the motion capture data of rowers using bivariate functional principal component analysis (bfPCA). The method has been applied on data from six elite rowers rowing on an ergometer. The analyses of the upper and lower body coordination during the rowing cycle revealed significant differ- ences between the rowers, even though the data was normalized to account for differences in body dimensions. We make an argument for the use of bfPCA and other functional data analysis methods for the quantitative evaluation and description of technique in sports.

• St-Onge, David; Côté-Allard, Ulysse; Glette, Kyrre Harald; Benoit, Gosselin & Beltrame, Giovanni (2019). Engaging with Robotic Swarms: Commands from Expressive Motion. ACM Transactions on Human-Robot Interaction. ISSN 2573-9522. 8(2). doi: 10.1145/3323213. Full text in Research Archive Show summary

  In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

• Frogner, Joakim Ihle; Noori, Farzan Majeed; Halvorsen, Pål; Hicks, Steven; Garcia-Ceja, Enrique & Tørresen, Jim [Show all 7 contributors for this article] (2019). One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression. In Boll, Susanne; Lee, Jeannie T; Meyer, Jochen; Nag, Nitish & O’Connor, Noel E. (Ed.), HealthMedia '19: Proceedings of the 4th International Workshop on Multimedia for Personal Health & Health Care. Association for Computing Machinery (ACM). ISSN 978-1-4503-6914-5. p. 9–15. doi: 10.1145/3347444.3356238. Show summary

  Nowadays, it has become possible to measure different human activities using wearable devices. Besides measuring the number of daily steps or calories burned, these datasets have much more potential since different activity levels are also collected. Such data would be helpful in the field of psychology because it can relate to various mental health issues such as changes in mood and stress. In this paper, we present a machine learning approach to detect depression using a dataset with motor activity recordings of one group of people with depression and one group without, i.e., the condition group includes 23 unipolar and bipolar persons, and the control group includes 32 persons without depression. We use convolutional neural networks to classify the depressed and nondepressed patients. Moreover, different levels of depression were classified. Finally, we trained a model that predicts MontgomeryÅsberg Depression Rating Scale scores. We achieved an average F1-score of 0.70 for detecting the control and condition groups. The mean squared error for score prediction was approximately 4.0.

• Noori, Farzan Majeed; Kahlon, Smiti ; Lindner, Philip; Nordgreen, Tine; Tørresen, Jim & Riegler, Michael (2019). Heart Rate Prediction from Head Movement during Virtual Reality Treatment for Social Anxiety, Proceedings of Content Based Multimedia Information (CBMI 2019). IEEE conference proceedings. ISSN 978-1-7281-4673-7. doi: 10.1109/CBMI.2019.8877454.
• Casagrande, Flavia Dias; Tørresen, Jim & Zouganeli, Evi (2019). Prediction of the Next Sensor Event and Its Time of Occurrence in Smart Homes. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11730 LNCS, p. 462–473. doi: 10.1007/978-3-030-30490-4_37.
• Weber, Aline; Alegre, Lucas N.; Tørresen, Jim & Castro da Silva, Bruno (2019). Parameterized Melody Generation with Autoencoders and Temporally-Consistent Noise. In Visi, Federico (Eds.), Music Proceedings of the International Conference on New Interfaces for Musical Expression. Universidade Federal do Rio Grande do Sul. ISSN 2220-4792. p. 174–179. Full text in Research Archive
• Garcia, Rafael; Falcao, Alexandre Xavier; Telea, Alexandru C.; Castro da Silva, Bruno; Tørresen, Jim & Comba, Joao Luiz Dihl (2019). A Methodology for Neural Network Architectural Tuning Using Activation Occurrence Maps. In Jayne, Chrisina & Somogyvári, Zoltán (Ed.), 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-1985-4. doi: 10.1109/IJCNN.2019.8852223.
• Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2019). Robot-Care for the Older People: Ethically Justified or Not? Proceedings of the 9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-8129-9. p. 43–47. doi: 10.1109/DEVLRN.2019.8850706. Show summary

  With the growing popularity of robotics technology, robots have also been introduced to take care of older people. Robots can assist seniors in their daily life tasks, monitor their health or provide them companionship. Despite the benefits of robots, several ethical concerns arise in elderly-robot interaction. The old people might get lonely due to less human interaction, or they feel less control in their lives. The elderly might lose their personal liberty. In this paper, we try to highlight the ethical concerns and provide some preliminary suggestions which would be helpful to reduce the ethical concerns by using customized systems with proper guidelines and consultation with older people.

• Khaksar, Weria; Uddin, Md Zia & Tørresen, Jim (2019). Multi-Query Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps. International Journal of Applied Mathematics and Computer Science. ISSN 1641-876X. 29(4), p. 641–654. doi: 10.2478/amcs-2019-0047. Full text in Research Archive
• Khaksar, Weria; Uddin, Md Zia & Tørresen, Jim (2019). Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments. International Journal of Mechanical Engineering and Robotics Research (IJMERR). ISSN 2278-0149. 8(1), p. 6–11. doi: 10.18178/ijmerr.8.1.6-11.

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• Gurrin, Cathal; Schoeffmann, Klaus; Joho, Hideo; Dang Nguyen, Duc Tien; Riegler, Michael & Piras, Luca (2019). LSC '19: Proceedings of the ACM Workshop on Lifelog Search Challenge. Association for Computing Machinery (ACM). ISBN 978-1-4503-6781-3. 50 p.

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• Castro da Silva, Bruno & Tørresen, Jim (2022). How to write a scientific paper.
• Castro da Silva, Bruno & Tørresen, Jim (2022). Deep Reinforcement Learning.
• Maffei, Renan & Tørresen, Jim (2022). Mobile Robotics.
• Tørresen, Jim (2022). Introduction to Research Visit to University of Oslo in Norway.
• Tørresen, Jim (2022). Introduction to Research Visit to University of Oslo in Norway.
• Tørresen, Jim & Kwak, Dongho (2022). Tutorial: Rhythm in Development and Learning – Similarities and Differences Between Humans and Technology.
• Saplacan, Diana; Tørresen, Jim; Weng, Yueh-Hsuan & Li, Phoebe (2022). Tutorial: Robots and Society (RO-SO 2022) / Ethical perspectives and technical challenges and opportunities with care robots.
• Tørresen, Jim (2022). Tutorial: Ethical Challenges in Computational Intelligence Research.
• Tørresen, Jim & Nakasawa, Atsushi (2022). Tutorial: Ethical Considerations in User Modeling and Personalization (ECUMAP).
• Tørresen, Jim (2022). Keynote: Multi-Modal Sensing for Care Robots for Older People.
• Tørresen, Jim (2022). Tutorial: Ethical challenges for Autonomous and Multiagent Systems.
• Tørresen, Jim (2022). Research Ethics in AI and Robotics.
• Tørresen, Jim (2022). Ethical Perspectives of Robotics and AI – How to develop preferable system? .
• Tørresen, Jim (2022). Research on health-related treatment and care technology – A technical and ethical view.
• Sajadi, Seyed Mohammad Reza; Mathiassen, Kim; Brun, Henrik & Elle, Ole Jakob (2022). Design, Kinematic Modeling, and Validation of a Robotic-Assisted Transesophageal Echocardiography System. Show summary

  Transesophageal Echocardiography is a safe and minimally invasive tool to provide high-quality anatomic and hemodynamic information of the heart and its structures. However, the risk of exposure to a significant level of radiation during combined fluoroscopy and TEE-guided examinations and musculoskeletal disorders due to holding the probe for prolonged periods of time are two main shortcomings of using TEE manually. This paper presents the design, kinematic modeling, and validation of a novel 6 DOF robotic-assisted transesophageal echocardiography system to overcome the above mentioned shortcomings. The robotic system has been designed based on a commercially available TEE probe with 5 actuated Degrees of freedom on the TEE handle and an extra DOF to control the imaging plane. The constant curvature model is employed for the forward and inverse kinematic modeling of the robotic system. The kinematic model and the performance of the robotic system are validated with an experimental setup using a 6DOF OptiTrack real-time tracking system.

• Castro da Silva, Bruno & Tørresen, Jim (2021). How to Write a Scientific Paper.
• Castro da Silva, Bruno & Tørresen, Jim (2021). Deep Reinforcement Learning.
• Tørresen, Jim; Pettersen, Klas Henning & Goodwin, Morten (2021). NORA forklarer kunstig intelligens – Podcast #6 med Jim Tørresen . [Internet]. Podcast.
• da Silva, Bruno Castro & Tørresen, Jim (2021). How to write a scientific paper.
• da Silva, Bruno Castro & Tørresen, Jim (2021). Deep Neural Networks.
• da Silva, Bruno Castro & Tørresen, Jim (2021). Deep reinforcement learning.
• Tørresen, Jim; Saplacan, Diana; Lintvedt, Mona Naomi; Mahler, Tobias & Fosch-Villaronga, Eduard (2021). Tutorial: Ethical and Legal Assessments Related to Robots and Systems .
• Tørresen, Jim (2021). Tutorial: Intelligent System Research – AI Ethical Challenges and Opportunities.
• Glette, Kyrre; Nygaard, Tønnes Frostad & Vogt, Yngve (2019). Her er universitetets nest selvlærende robot. [Business/trade/industry journal]. Teknisk ukeblad.
• Nordmoen, Jørgen Halvorsen (2019). Evolutionary Robotics <3 Compute.
• Nygaard, Tønnes Frostad & Glette, Kyrre (2019). Her er universitetets mest avanserte, selvlærende robot. [Business/trade/industry journal]. Apollon.
• Tørresen, Jim (2019). Etikk blir viktigare for tilgang til pengesekken . [Business/trade/industry journal]. Forskningsetikk.
• Tørresen, Jim (2019). Folket bør få være med og styre KI-skuta. [Business/trade/industry journal]. Forskningsetikk.
• Tørresen, Jim (2019). Roboter kan slå salto – men bryr de seg om applausen? [Internet]. LO Ingeniør.
• Tørresen, Jim (2019). Intelligent Robots and Systems in Real-World Environment.
• Becker, Artur; Herrebrøden, Henrik; Gonzalez Sanchez, Victor Evaristo; Nymoen, Kristian; Dal Sasso Freitas, Carla Maria & Tørresen, Jim [Show all 7 contributors for this article] (2019). Functional Data Analysis of Rowing Technique Using Motion Capture Data. Show summary

  We present an approach to analyzing the motion capture data ofrowers using bivariate functional principal component analysis(bfPCA). The method has been applied on data from six elite rowersrowing on an ergometer. The analyses of the upper and lower bodycoordination during the rowing cycle revealed significant differences between the rowers, even though the data was normalized toaccount for differences in body dimensions. We make an argumentfor the use of bfPCA and other functional data analysis methods forthe quantitative evaluation and description of technique in sports.

• Tørresen, Jim; Glette, Kyrre & Ellefsen, Kai Olav (2019). Intelligent, Adaptive Robots in Real-World Scenarios.
• Tørresen, Jim; Glette, Kyrre & Ellefsen, Kai Olav (2019). Adaptive Robot Body and Control for Real-World Environments.
• Ellefsen, Kai Olav (2019). Hva Kan Roboter Lære av Biologisk Liv?
• Langhammer, Birgitta; Woll, Anita & Skjønberg, Jeppe (2019). The use of Technology to promote activity and communication by the municipality services, examples from an urban area.
• Rohlfing, Katharina J. & Tørresen, Jim (2019). Explainability: an interactive view.
• Comba, Joao Luiz Dihl & Tørresen, Jim (2019). Visual Data Analysis of Unstructured and Big Data.
• Castro da Silva, Bruno & Tørresen, Jim (2019). Deep Neural Networks.
• Castro da Silva, Bruno & Tørresen, Jim (2019). How to Write a Scientific Paper .
• Miura, Jun & Tørresen, Jim (2019). Intelligent Robot Technologies for Care and Lifestyle Support .
• Tørresen, Jim (2019). Hva er kunstig intelligens?
• Tørresen, Jim (2019). Artificial Intelligence and Applications in Health and Care .
• Ellefsen, Kai Olav & Tørresen, Jim (2019). Evolutionary Robotics: Automatic design of robot bodies and control.
• Tørresen, Jim (2019). Supporting Older People with Robots for Independent Living. Show summary

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• Tørresen, Jim (2019). AI for Robots Interacting with Humans.
• Tørresen, Jim (2019). Ethical Considerations for Healthcare Robots. Show summary

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• Tørresen, Jim (2019). Robots and Mobile Apps Solving Problems Better with Artificial Intelligence.
• Tørresen, Jim (2019). Supporting Older People at Home with a Mobile Robot for Independent Living.
• Tørresen, Jim (2019). Future and Ethical Perspectives of Robotics and AI.
• Tørresen, Jim (2019). Sensing Human State with Application in Older People Care and Mental Health Treatment.
• Tørresen, Jim (2019). Design and Control of Robots for Real-World Environment.
• Tørresen, Jim (2019). Introduction to Different Challenges in Ethical AI and Possible Ways of Addressing Them. Show summary

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• Tørresen, Jim (2019). Intelligent and Adaptive Robots in Real-World Environment. Show summary

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• Nordmoen, Jørgen; Glette, Kyrre & Ellefsen, Kai Olav (2021). Enhancing MAP-Elites to overcome challenges in Evolutionary Robotics. University of Oslo. ISSN 1501-7710.
• Eijsink, Henrik; Tørresen, Jim & Endestad, Tor (2021). Analyzing Brain Signals for Control. Universitetet i Oslo.
• Nergård, Katrine Linnea; Tørresen, Jim & Endestad, Tor (2021). Analyzing Brain Signals for Robot Control. Universitetet i Oslo.
• Orten, Mathias Kramer & Tørresen, Jim (2021). Machine Ethics for Autonomous systems – Learning verifiable rules from the environment. Universitetet i Oslo.
• Næss, Torgrim Rudland; Martin, Charles Patrick & Tørresen, Jim (2019). A Physical Intelligent Instrument using Recurrent Neural Networks. Universitetet i Oslo.
• Refsum, Vetle Kind; Khaksar, Weria & Tørresen, Jim (2019). Human aware navigation and reaction for a mobile service robot with a LIDAR unit. Universitetet i Oslo.
• Lappyová, Veronika; Khaksar, Weria & Tørresen, Jim (2019). Development and implementation of human detection and following method on a mobile robot. Universitetet i Oslo.

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