BIO
Gwenn Englebienne is assistant professor at the Human Media Interaction group of the University of Twente. His interests are in the understanding and automatic perception of human behaviour. This ranges from the recognition of the activity of elderly using simple binary sensors installed in their house, to the evaluation of crowds' reactions to performances and to recognizing and adapting to the behaviour of people interacting with a social robot.
As humans, we exhibit very complex and varied behaviour, which is adapted to our physical environment, our social environment, the context we are in, etc. Computers are only beginning to scratch the surface of this exciting world, yet as they become more pervasive in our environment, correctly assessing the humans' context and social situation is becoming crucial to the interfacing between humans and computers. In my research, I focus on using machine learning and computer vision techniques to accurately recognizing this context.
publications
Jump to: 2024 | 2023 | 2022 | 2021 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014
2024
Automated Question-Answer Generation for Evaluating RAG-based Chatbots (2024)In 1st Workshop on Patient-Oriented Language Processing, CL4Health 2024 at LREC-COLING 2024 - Workshop Proceedings (pp. 204-214). European Language Resources Association (ELRA). González Torres, J. J., Bîndilă, M. B., Hofstee, S., Szondy, D., Nguyen, Q. H., Wang, S. & Englebienne, G.https://aclanthology.org/2024.cl4health-1.25.pdf
2023
Toward Standard Guidelines to Design the Sense of Embodiment in Teleoperation Applications: A Review and Toolbox (2023)Human-computer interaction, 38(5-6), 322-351. Falcone, S., Englebienne, G., van Erp, J. B. F. & Heylen, D. K. J.https://doi.org/10.1080/07370024.2022.2039147Out of Sight,...: How Asymmetry in Video-Conference Affects Social Interaction (2023)In ICMI '23: Proceedings of the 25th International Conference on Multimodal Interaction (pp. 465-469). ACM Publishing. Sallaberry, C., Englebienne, G., van Erp, J. & Evers, V.https://doi.org/10.1145/3577190.3614168A multidisciplinary investigation to unravel the complexity of the sense of embodiment in teleoperation (2023)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Falcone, S.https://doi.org/10.3990/1.9789036558815A GNN-based Architecture for Group Detection from spatio-temporal Trajectory Data (2023)In Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (pp. 327-339) (Lecture Notes in Computer Science; Vol. 13876). Nasri, M., Fang, Z., Baratchi, M., Englebienne, G., Wang, S., Koutamanis, A. & Rieffe, C.https://doi.org/10.1007/978-3-031-30047-9_26Feature Attribution Explanations for Spiking Neural Networks (2023)In Proceedings - 2023 IEEE 5th International Conference on Cognitive Machine Intelligence, CogMI 2023 (pp. 59-68). IEEE. Nguyen, E., Nauta, M., Englebienne, G. & Seifert, C.https://doi.org/10.1109/CogMI58952.2023.00018
2022
EMG-based Feedback Modulation for Increased Transparency in Teleoperation (2022)In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE. Schoot Uiterkamp, L., Porcini, F., Englebienne, G., Frisoli, A. & Dresscher, D.https://doi.org/10.1109/IROS47612.2022.9981162Channel Contribution In Deep Learning Based Automatic Sleep Scoring – How Many Channels Do We Need? (2022)IEEE transactions on neural systems and rehabilitation engineering, 31, 494-505. Lu, C., Pathak, S., Englebienne, G. & Seifert, C.https://doi.org/10.1109/TNSRE.2022.3227040Pupil diameter as implicit measure to estimate sense of embodiment (2022)In Proceedings of the 44th Annual Meeting of the Cognitive Science Society (Proceedings of the Annual Meeting of the Cognitive Science Society; Vol. 44). The Cognitive Science Society. Falcone, S., Zhang, L., Pradhan, S., Englebienne, G., Brouwer, A.-M., Cocu, I., Stuldreher, I., Heuvel, M., de Vries, P., Gijsbertse, K., Heylen, D. & van Erp, J.https://escholarship.org/uc/item/8rt963b9It’s Complicated: The Relationship between User Trust, Model Accuracy and Explanations in AI (2022)ACM Transactions on Computer-Human Interaction, 29(4), 1-33. Papenmeier, A., Kern, D., Englebienne, G. & Seifert, C.https://doi.org/10.1145/3495013Assessing the Pupil Dilation as Implicit Measure of the Sense of Embodiment in Two User Studies (2022)[Contribution to conference › Abstract] 12th International Conference on Methods and Techniques in Behavioral Research, MB 2022. Falcone, S., Englebienne, G., Heylen, D. K. J. & van Erp, J. B. F.What Comes After Telepresence? Embodiment, Social Presence and Transporting One's Functional and Social Self (2022)In 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings (pp. 2067-2072) (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2022). IEEE. van Erp, J. B. F., Sallaberry, C., Brekelmans, C., Dresscher, D., Ter Haar, F., Englebienne, G., van Bruggen, J., de Greeff, J., Pereira, L. F. S., Toet, A., Hoeba, N., Lieftink, R., Falcone, S. & Brug, T.https://doi.org/10.1109/SMC53654.2022.9945544
2021
Individual Action and Group Activity Recognition in Soccer Videos from a Static Panoramic Camera (2021)In ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (pp. 594-601) (International Conference on Pattern Recognition Applications and Methods; Vol. 1). SCITEPRESS. Gerats, B., Bouma, H., Uijens, W., Englebienne, G. & Spreeuwers, L.https://doi.org/10.5220/0010303505940601Towards Analyzing and Predicting the Experience of Live Performances with Wearable Sensing (2021)IEEE transactions on affective computing, 12(1), 269 - 276. Gedik, E., Cabrera-Quiros, L., Martella, C., Englebienne, G. & Hung, H.https://doi.org/10.1109/TAFFC.2018.2875987
2019
Detecting Perceived Appropriateness of a Robot's Social Positioning Behavior from Non-Verbal Cues: a Dataset (2019)[Dataset Types › Dataset]. 4TU.Centre for Research Data. Vroon, J. H., Englebienne, G. & Evers, V.https://doi.org/10.4121/uuid:b76c3a6f-f7d5-418e-874a-d6140853e1faDetecting perceived appropriateness of a robot's social positioning behavior from non-verbal cues (2019)In Proceedings - 2019 IEEE 1st International Conference on Cognitive Machine Intelligence, CogMI 2019 (pp. 216-225). Article 8998780. IEEE. Vroon, J., Englebienne, G. & Evers, V.https://doi.org/10.1109/CogMI48466.2019.00039Machine-based mapping of innovation portfolio dynamics: how to detect success and failure traps (2019)[Contribution to conference › Other] 27th Annual High Technology Small Firms Conference, HTSF 2019 (Unpublished). de Visser, M., Miao, S., Visscher, K., Englebienne, G. & Sools, A. A. M.Fast and discriminative semantic embedding (2019)In IWCS 2019 - Proceedings of the 13th International Conference on Computational Semantics - Long Papers (pp. 235-246). Association for Computational Linguistics (ACL). Koopman, R., Wang, S. & Englebienne, G.How model accuracy and explanation fidelity influence user trust in AI (2019)[Contribution to conference › Paper] IJCAI Workshop on Explainable Artificial Intelligence (XAI) 2019. Papenmeier, A., Englebienne, G. & Seifert, C.Non-Parametric Subject Prediction (2019)In Digital Libraries for Open Knowledge: 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Oslo, Norway, September 9-12, 2019, Proceedings (pp. 312-326) (Lecture Notes in Computer Science; Vol. 11799). Springer. Wang, S., Koopman, R. & Englebienne, G.https://doi.org/10.1007/978-3-030-30760-8_27
2018
Responsive social positioning behaviour: for semi-autonomous telepresence robots (2018)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Vroon, J. H.https://doi.org/10.3990/1.9789036546195The MULAI Corpus: Multimodal Recordings of Spontaneous Laughter in Dyadic Interaction (2018)In Proceedings of Laughter Workshop 2018 (pp. 58-63). Jansen, M.-P., Heylen, D. K. J., Truong, K. P., Nazareth, D. S. & Englebienne, G.Automatic recognition of engagement and emotion in a group of children (2018)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Kim, J.https://doi.org/10.3990/1.9789036545839Continuous measuring of the indoor walking speed of older adults living alone (2018)Journal of ambient intelligence and humanized computing, 9(3), 589-599. Nait Aicha, A., Englebienne, G. & Kröse, B.https://doi.org/10.1007/s12652-017-0456-xDeep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry (2018)Sensors (Switzerland), 18(5). Article 1654. Nait Aicha, A., Englebienne, G., Van Schooten, K., Pijnappels, M. & Kröse, B.https://doi.org/10.3390/s18051654Learning spectro-temporal features with 3D CNNs for speech emotion recognition (2018)In 2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017 (pp. 383-388). IEEE. Kim, J., Truong, K. P., Englebienne, G. & Evers, V.https://doi.org/10.1109/ACII.2017.8273628“I would like to get close to you”: Making robot personal space invasion less intrusive with a social gaze cue (2018)In Universal Access in Human-Computer Interaction. Virtual, Augmented, and Intelligent Environments - 12th International Conference, UAHCI 2018, Held as Part of HCI International 2018, Proceedings (pp. 366-385) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10908 LNCS). Springer. Suvei, S. D., Vroon, J., Somoza Sanchéz, V. V., Bodenhagen, L., Englebienne, G., Krüger, N. & Evers, V.https://doi.org/10.1007/978-3-319-92052-8_29
2017
Learning to Recognize Human Activities Using Soft Labels (2017)IEEE transactions on pattern analysis and machine intelligence, 39(10), 1973-1984. Hu, N., Englebienne, G., Lou, Z. & Kröse, B. J. A.https://doi.org/10.1109/TPAMI.2016.2621761Machine-based mapping of innovation portfolios (2017)[Contribution to conference › Paper] 18th International CINet Conference 2017. de Visser, M., Miao, S., Englebienne, G., Sools, A. M. & Visscher, K.Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning (2017)[Working paper › Preprint]. Kim, J., Englebienne, G., Truong, K. P. & Evers, V.Blame my telepresence robot Joint effect of proxemics and attribution on interpersonal attraction (2017)In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 162-168). IEEE. van Houwelingen-Snippe, J., Vroon, J. H., Englebienne, G. & Haselager, W. F. G.https://doi.org/10.1109/ROMAN.2017.8172296Unsupervised visit detection in smart homes (2017)Pervasive and Mobile Computing, 34(1), 157-167. Nait Aicha, A., Englebienne, G. & Kröse, B.https://doi.org/10.1016/j.pmcj.2016.05.003Deep Temporal Models using Identity Skip-Connections for Speech Emotion Recognition (2017)In MM '17: Proceedings of the 2017 ACM on Multimedia Conference (pp. 1006-1013). Association for Computing Machinery. Kim, J., Englebienne, G., Truong, K. P. & Evers, V.https://doi.org/10.1145/3123266.3123353Learning spectral-temporal features with 3D CNNs for speech emotion recognition (2017)[Contribution to conference › Paper] 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017. Kim, J., Truong, K., Englebienne, G. & Evers, V.Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning (2017)In Interspeech 2017: 20-24 August 2017, Stockholm (pp. 1113-1117). International Speech Communication Association. Kim, J., Englebienne, G., Truong, K. P. & Evers, V.https://doi.org/10.21437/Interspeech.2017
2016
Human intent forecasting using intrinsic kinematic constraints (2016)[Contribution to conference › Paper] 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016. Hu, N., Bestick, A., Englebienne, G., Bajscy, R. & Kröse, B.https://doi.org/10.1109/IROS.2016.7759141Incorporating perception uncertainty in human-aware navigation: A comparative study (2016)[Contribution to conference › Paper] 2016 25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016. Talebpour, Z., Viswanathan, D., Ventura, R., Englebienne, G. & Martinoli, A.https://doi.org/10.1109/ROMAN.2016.7745175Responsive Social Agents: Feedback-Sensitive Behavior Generation for Social Interactions (2016)In Social Robotics: 8th International Conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings (pp. 126-137) (Lecture notes in computer science; Vol. 9979). Vroon, J. H., Englebienne, G. & Evers, V.https://doi.org/10.1007/978-3-319-47437-3_13Delta Features From Ambient Sensor Data are Good Predictors of Change in Functional Health (2016)IEEE journal of biomedical and health informatics, 21(4), 986-993. Robben, S., Englebienne, G. & Krose, B. J. A.https://doi.org/10.1109/JBHI.2016.2593980Mixture of Switching Linear Dynamics to Discover Behavior Patterns in Object Tracks (2016)IEEE transactions on pattern analysis and machine intelligence, 38(2), 322 - 334. Kooij, J. F. P., Englebienne, G. & Gavrila, D. M.https://doi.org/10.1109/TPAMI.2015.2443801
2015
Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home (2015)In Ambient Intelligence: 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings (pp. 219–235). Nait Aicha, A., Englebienne, G. & Kröse, B.https://doi.org/10.1007/978-3-319-26005-1_15Dynamics of social positioning patterns in group-robot interactions (2015)In Proceedings of the 24th IEEE International Sumposium on Robot and Human Interactive Communication (RoMan 2015) (pp. 394-399). IEEE. Vroon, J., Joosse, M., Lohse, M., Kolkmeier, J., Kim, J., Truong, K., Englebienne, G., Heylen, D. & Evers, V.https://doi.org/10.1109/ROMAN.2015.7333633TERESA: a socially intelligent semi-autonomous telepresence system (2015)[Contribution to conference › Paper] Workshop on Machine Learning for Social Robotics 2015. Shiarlis, K., Messias, J., van Someren, M., Whiteson, S., Kim, J., Vroon, J. H., Englebienne, G., Truong, K. P., Pérez-Higueras, N., Pérez-Hurtado, I., Ramon-Vigo, R., Caballero, F., Merino, L., Shen, J., Petridis, S., Pantic, M., Hedman, L., Scherlund, M., Koster, R. & Michel, H.https://staff.fnwi.uva.nl/s.a.whiteson/Shimon_Whiteson/ICRA_files/teresa_icra15ws-crc.pdfRARE: people detection in crowded passages by range image reconstruction (2015)Machine vision and applications, 26, 561–573. van Oosterhout, T., Englebienne, G. & Kröse, B.https://doi.org/10.1007/s00138-015-0678-xAriadne's thread - Interactive navigation in a world of networked information (2015)In CHI 2015: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 1833-1838). Association for Computing Machinery. Koopman, R., Wang, S., Scharnhorst, A. & Englebienne, G.https://doi.org/10.1145/2702613.2732781A hierarchical representation for human activity recognition with noisy labels (2015)[Contribution to conference › Paper] 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015. Hu, N., Englebienne, G., Lou, Z. & Kröse, B.How Was It?: Exploiting Smartphone Sensing to Measure Implicit Audience Responses to Live Performances (2015)In MM '15: Proceedings of the 23rd ACM international conference on Multimedia (pp. 201–210). Martella, C., Gedik, E., Cabrera-Quiros, L., Englebienne, G. & Hung, H.https://doi.org/10.1145/2733373.2806276Identifying multiple objects from their appearance in inaccurate detections (2015)Computer vision and image understanding, 136, 103-116. Kooij, J. F. P., Englebienne, G. & Gavrila, D. M.https://doi.org/10.1016/j.cviu.2015.03.012Latent Hierarchical Model for Activity Recognition (2015)IEEE transactions on robotics, 31(6), 1472 - 1482. Hu, N., Englebienne, G., Lou, Z. & Kröse, B.https://doi.org/10.1109/TRO.2015.2495002
2014
Behavior analysis of elderly using topic models (2014)Pervasive and Mobile Computing, 15, 181-199. Rieping, K., Englebienne, G. & Kröse, B.https://doi.org/10.1016/j.pmcj.2014.07.001Learning latent structure for activity recognition (2014)In 2014 IEEE International Conference on Robotics and Automation (ICRA). Hu, N., Englebienne, G., Lou, Z. & Kröse, B.https://doi.org/10.1109/ICRA.2014.6906983Modeling visit behaviour in smart homes using unsupervised learning (2014)[Contribution to conference › Paper] 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Nait Aicha, A., Englebienne, G. & Krose, B. J. A.A two-layered approach to recognize high-level human activities (2014)In 23rd IEEE International Symposium on Robot and Human Interactive Communication. Hu, N., Englebienne, G. & Kröse, B.https://doi.org/10.1109/ROMAN.2014.6926260Detecting conversing groups with a single worn accelerometer (2014)In ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction (pp. 84–91). Hung, H., Englebienne, G. & Cabrera-Quiros, L.https://doi.org/10.1145/2663204.2663228In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models (2014)IEEE pervasive computing, 13(3), 67 - 75. Ordoñez, F. J., Englebienne, G. & de Toledo, P.https://doi.org/10.1109/MPRV.2014.52Video surveillance for behaviour monitoring in home health care (2014)In Proceedings of Measuring Behavior 2014: Wageningen, The Netherlands, August 27‐29, 2014 (pp. 27-29). Measuring Behavior. Kröse, B. J. A., van Oosterhout, T. & Englebienne, G.https://hdl.handle.net/11245/1.437333