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Staff profile

Overview

Professor Dorothy Monekosso

Biography

Other roles

2022: PGR Director (Durham University)

2019: Chief Technical Officer (CTO), 0.5FTE, More Life (UK) Ltd: More Life is a health and well being company providing services under contract to NHS England and various local authorities. The services include weight management and smoking cessation among others. The company has been operating for 20+ year with 160+ staff.

Short Bio

Dorothy Monekosso (PhD) is Professor of Computer Science and Chief Technical Officer (CTO) at More Life UK Ltd. Dorothy holds a PhD (2000) in Spacecraft Engineering, a Master’s in Satellite Engineering from the Surrey Space Centre (University of Surrey) and Bachelor in Electronic Engineering. She began her career in space sector, developing on-board computers and control systems for spacecraft and satellites. Dorothy became interested in Artificial Intelligence during her PhD applying machine learning methods and techniques to spacecraft autonomy. On the basis of this work, she was awarded a Royal Academy of Engineering, Engineering Foresight Award. Her current research applies the same techniques to develop innovative healthcare technologies. In 2020, she was awarded an honorary fellowship of the British Computer Society for work in assistive and rehabilitation technologies.

Social Media

www.linkedin.com/in/prof-dorothy-monekosso

Projects

Digital Health Hub 

Insights - automated analysis using sentiment analysis 

More Life & Durham - Personalising weight management programmes (Innovate UK)

Healthy Lifestyle Programmes | MoreLife UK (more-life.co.uk)

MRC - IAA "Technology Supported Rehabilitation Impact Evaluation Study" Virtual Physiotherapist - physiotherapy at home

MRC impact acceleration accounts (previously confidence in concept) – UKRI

https://www.virtualphysioproject.com/

Grow MedTech Virtual Physiotherapist could improve stroke recovery - Grow MedTech

Digital Twins in Health

Smart homes - Supporting independent living

  •  Activty recognition, behaviour modelling, 
Supervision

If you're interested in undertaking a research project at PhD, Masters, or a 3rd/4th year undergraduate project under my supervision in assistive and rehabilitations technologies, human digital twins, applications of machine learning to healthcare technologies, and digital health please get in touch.

Research interests

  • Behaviour Analytics
  • Digital Twin Computing
  • Anomaly detection
  • Data analytics: applications of machine learning to decision support systems in health
  • Digital health: wearables, assistive and rehabilitation technologies, medical image analysis, clinical decision support
  • Smart Environments & Cities: supporting people to live independently

Esteem Indicators

Publications

Chapter in book

  • Multi-robot Teams for Environmental Monitoring
    Espina, M. V., Grech, R., De Jager, D., Remagnino, P., Iocchi, L., Marchetti, L., Nardi, D., Monekosso, D., Nicolescu, M., & King, C. (2011). Multi-robot Teams for Environmental Monitoring. In P. Remagnino, D. Monekosso, & L. Jain (Eds.), INNOVATIONS IN DEFENCE SUPPORT SYSTEMS - 3: INTELLIGENT PARADIGMS IN SECURITY (pp. 183-209).
  • The Analysis of Crowd Dynamics: From Observations to Modelling
    Zhan, B., Remagnino, P., Monekosso, D., & Velastin, S. (2009). The Analysis of Crowd Dynamics: From Observations to Modelling. In C. Mumford & L. Jain (Eds.), COMPUTATIONAL INTELLIGENCE: COLLABORATION, FUSION AND EMERGENCE (pp. 441-472).
  • Anomalous Behavior Detection: Supporting Independent Living
    Monekosso, D. N., & Remagnino, P. (2009). Anomalous Behavior Detection: Supporting Independent Living. In D. Monekosso, P. Remagnino, & Y. Kuno (Eds.), Intelligent Environments: Methods, Algorithms and Applications (pp. 33-48). Springer. https://doi.org/10.1007/978-1-84800-346-0_3
  • Intelligent Environments: Methods, Algorithms and Applications
    Monekosso, D. N., Remagnino, P., & Kuno, Y. (2009). Intelligent Environments: Methods, Algorithms and Applications. In D. Monekosso, P. Remagnino, & Y. Kuno (Eds.), Intelligent Environments: Methods, Algorithms and Applications (pp. 1-11). https://doi.org/10.1007/978-1-84800-346-0_1
  • Augmenting Professional Training, an Ambient Intelligence Approach
    Zhan, B., Monekosso, D. N., Remagnino, P., Velastin, S. A., & Rush, S. (2009). Augmenting Professional Training, an Ambient Intelligence Approach. In D. Monekosso, P. Remagnino, & Y. Kuno (Eds.), Intelligent Environments: Methods, Algorithms and Applications (pp. 105-121). Springer. https://doi.org/10.1007/978-1-84800-346-0_7

Conference Paper

  • Usability and safety for a virtual physiotherapy rehabilitation system: a pilot study
    Stephens, C. A., & Monekosso, D. (2024). Usability and safety for a virtual physiotherapy rehabilitation system: a pilot study. In PETRA ’24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (pp. 510-514). Association for Computing Machinery. https://doi.org/10.1145/3652037.3663911
  • Advanced VR Calibration for Upper Limb Rehabilitation: Making Immersive Environments Accessible
    Herrera, V., Reyes-Guzmán, A., Vallejo, D., Castro-Schez, J., Monekosso, D., Carlos, G.-M., & Albusac, J. (2024). Advanced VR Calibration for Upper Limb Rehabilitation: Making Immersive Environments Accessible. In Proceedings of the 26th International Conference on Enterprise Information Systems (pp. 378-389). SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012624600003690
  • Latent Bernoulli Autoencoder
    Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2020). Latent Bernoulli Autoencoder. In H. Daume & A. Singh (Eds.), Proceedings of Machine Learning Research.
  • Skin Identification Using Deep Convolutional Neural Network
    Oghaz, M. M. D., Argyriou, V., Monekosso, D., & Remagnino, P. (2020). Skin Identification Using Deep Convolutional Neural Network. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, D. Ushizima, S. Chai, S. Sueda, X. Lin, A. Lu, D. Thalmann, C. Wang, & P. Xu (Eds.), Lecture Notes in Computer Science (pp. 181-193). https://doi.org/10.1007/978-3-030-33720-9%5C_14
  • Single Image Ear Recognition Using Wavelet-Based Multi-Band PCA
    Zarachoff, M., Sheikh-Akbari, A., & Monekosso, D. (2019). Single Image Ear Recognition Using Wavelet-Based Multi-Band PCA. In European Signal Processing Conference.
  • Summarizing Videos with Attention
    Fajtl, J., Sokeh, H. S., Argyriou, V., Monekosso, D., & Remagnino, P. (2019). Summarizing Videos with Attention. In G. Carneiro & S. You (Eds.), Lecture Notes in Computer Science (pp. 39-54). https://doi.org/10.1007/978-3-030-21074-8%5C_4
  • Automatic recognition of physical exercises performed by stroke survivors to improve remote rehabilitation
    Schez-Sobrino, S., Monekosso, D. N., Remagnino, P., Vallejo, D., & Glez-Morcillo, C. (2019). Automatic recognition of physical exercises performed by stroke survivors to improve remote rehabilitation. Presented at 2019 INTERNATIONAL CONFERENCE ON MULTIMEDIA ANALYSIS AND PATTERN RECOGNITION (MAPR) IEEE; Vietnamese Assoc Pattern Recognit; VNU HCMC, Univ Informat Technol, Multimedia Commun Lab; VAST, Inst Informat Technol; HUST, MICA, Int Res Inst; ROSEN Grp; TMA Solu.
  • Superframes, A Temporal Video Segmentation
    Sokeh, H. S., Argyriou, V., Monekosso, D., & Remagnino, P. (2018). Superframes, A Temporal Video Segmentation. In International Conference on Pattern Recognition (pp. 566-571).
  • DEEP RESIDUAL NETWORK WITH SUBCLASS DISCRIMINANT ANALYSIS FOR CROWD BEHAVIOR RECOGNITION
    Mandal, B., Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2018). DEEP RESIDUAL NETWORK WITH SUBCLASS DISCRIMINANT ANALYSIS FOR CROWD BEHAVIOR RECOGNITION. In IEEE International Conference on Image Processing ICIP (pp. 938-942).
  • 2D Multi-Band PCA and its Application for Ear Recognition
    Zarachoff, M., Sheikh-Akbari, A., & Monekosso, D. (2018). 2D Multi-Band PCA and its Application for Ear Recognition. In IEEE International Conference on Imaging Systems and Techniques (pp. 324-328).
  • AMNet: Memorability Estimation with Attention
    Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2018). AMNet: Memorability Estimation with Attention. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 6363-6372). https://doi.org/10.1109/cvpr.2018.00666
  • Application of Single Image Super-Resolution in Human Ear Recognition Using Eigenvalues
    Zarachoff, M., Sheikh-Akbari, A., & Monekosso, D. (2018). Application of Single Image Super-Resolution in Human Ear Recognition Using Eigenvalues. In IEEE International Conference on Imaging Systems and Techniques (pp. 286-291).
  • Fuzzy-Rough based Decision System for Gait adopting Instance Selection
    Jhawar, A., Chan, C. S., Monekosso, D., & Remagnino, P. (2016). Fuzzy-Rough based Decision System for Gait adopting Instance Selection. In IEEE International Fuzzy Systems Conference Proceedings (pp. 1127-1133).
  • Telemetry assisted frame registration and background subtraction in low-altitude UAV videos
    Tzanidou, G., Climent-Perez, P., Hummel, G., Schmitt, M., Stuetz, P., Monekosso, D. N., & Remagnino, P. (2015). Telemetry assisted frame registration and background subtraction in low-altitude UAV videos. Presented at 2015 12TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE (AVSS) IEEE Signal Proc Soc; IEEE Comp Soc; Fraunhofer Inst Optronics, Syst Technol \& Image Exploitation; Karlsruhe Inst Technol.
  • Detecting Events in Crowded Scenes using Tracklet Plots
    Climent-Perez, P., Mauduit, A., Monekosso, D. N., & Remagnino, P. (2014). Detecting Events in Crowded Scenes using Tracklet Plots (S. Battiato & J. Braz, Eds.).
  • Telemetry-Based Search Window Correction for Airborne Tracking
    Climent-Perez, P., Lazaridis, G., Hummel, G., Russ, M., Monekosso, D. N., & Remagnino, P. (2014). Telemetry-Based Search Window Correction for Airborne Tracking. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, R. McMahan, J. Jerald, H. Zhang, S. Drucker, C. Kambhamettu, M. ElChoubassi, Z. Deng, & M. Carlson (Eds.), Lecture Notes in Computer Science (pp. 457-466).
  • Multi-view event detection in crowded scenes using tracklet plots
    Climent-Perez, P., Monekosso, D. N., & Remagnino, P. (2014). Multi-view event detection in crowded scenes using tracklet plots. In International Conference on Pattern Recognition (pp. 4370-4375). https://doi.org/10.1109/icpr.2014.748
  • Robot Teams: Sharing Visual Memories
    Grech, R., Florez-Revuelta, F., Monekosso, D. N., & Remagnino, P. (2014). Robot Teams: Sharing Visual Memories. In M. Hsieh & G. Chirikjian (Eds.), Springer Tracts in Advanced Robotics (pp. 369-381). https://doi.org/10.1007/978-3-642-55146-8%5C_26
  • The Visual Object Tracking VOT2013 challenge results
    Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Porikli, F., Cehovin, L., Nebehay, G., Fernandez, G., Vojir, T., Gatt, A., Khajenezhad, A., Salahledin, A., Soltani-Farani, A., Zarezade, A., Petrosino, A., Milton, A., Bozorgtabar, B., Li, B., Chan, C. S., … Niu, Z. (2013). The Visual Object Tracking VOT2013 challenge results. Presented at 2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) IEEE; CVF; IEEE Comp Soc; APRS; Australiasn Natl Univ; NICTA; FACE++; Natl Robot Engn Ctr; Google; Disney Res; nVIDIA; Raytheon BBN Technologies; Facebook; Adobe; Kitware; OMRON; SRI. https://doi.org/10.1109/iccvw.2013.20
  • Reactive Coordination and Adaptive Lattice Formation in Mobile Robotic Surveillance Swarms
    Mullen, R. J., Monekosso, D., Barman, S., & Remagnino, P. (2013). Reactive Coordination and Adaptive Lattice Formation in Mobile Robotic Surveillance Swarms. In A. Martinoli, F. Mondada, N. Correll, G. Mermoud, M. Egerstedt, M. Hsieh, L. Parker, & K. Stoy (Eds.), Springer Tracts in Advanced Robotics.
  • The ``Good'' Brother: Monitoring People Activity in Private Spaces
    Padilla-Lopez, J. R., Florez-Revuelta, F., Monekosso, D. N., & Remagnino, P. (2012). The ``Good’’ Brother: Monitoring People Activity in Private Spaces. In S. Omatu, J. Santana, S. Gonzalez, J. Molina, A. Bernardos, & J. Rodriguez (Eds.), Advances in Intelligent and Soft Computing.
  • A Vision-Based System for Object Identification and Information Retrieval in a Smart Home
    Grech, R., Monekosso, D., de Jager, D., & Remagnino, P. (2010). A Vision-Based System for Object Identification and Information Retrieval in a Smart Home. In B. DeRuyter, R. Wichert, D. Keyson, P. Markopoulos, N. Streitz, M. Divitini, N. Georgantas, & A. Gomez (Eds.), Lecture Notes in Computer Science.
  • A NOVEL SEGMENTATION METHOD FOR CROWDED SCENES
    Bloisi, D., Iocchi, L., Monekosso, D. N., & Remagnino, P. (2009). A NOVEL SEGMENTATION METHOD FOR CROWDED SCENES. Presented at VISAPP 2009: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOL 2 Inst Syst \& Technologies Informat, Control \& Commun; ACM SIGGRAPH.
  • Synthetic Training Data Generation for ADL Modeling
    Monekosso, D. N., & Remagnino, P. (2009). Synthetic Training Data Generation for ADL Modeling. In M. Schneider, A. Kroner, J. Alvarado, A. Higuera, J. Augusto, D. Cook, V. Ikonen, P. Cech, P. Mikulecky, A. Kameas, & V. Callaghan (Eds.), Ambient Intelligence and Smart Environments (pp. 137-144). https://doi.org/10.3233/978-1-60750-056-8-137
  • An Adaptive Tracker for Assisted Living
    Bloisi, D., Iocchi, L., Marchetti, L., Monekosso, D. N., & Remagnino, P. (2009). An Adaptive Tracker for Assisted Living. Presented at AVSS: 2009 6TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE IEEE; IEEE Comp Soc; IEEE Signal Proc Soc. https://doi.org/10.1109/avss.2009.96
  • A Multi-agent Architecture for Multi-robot Surveillance
    Vallejo, D., Remagnino, P., Monekosso, D. N., Jimenez, L., & Gonzalez, C. (2009). A Multi-agent Architecture for Multi-robot Surveillance. In N. Nguyen, R. Kowalczyk, & S. Chen (Eds.), Lecture Notes in Artificial Intelligence.
  • Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis
    Monekosso, D., & Remagnino, P. (2009). Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis. In M. Tscheligi, B. DeRuyter, P. Markopoulus, R. Wichert, T. Mirlacher, A. Meschtscherjakov, & W. Reitberger (Eds.), Lecture Notes in Computer Science.
  • Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns
    Mullen, R. J., Monekosso, D., Barman, S., Remagnino, P., & Wilkin, P. (2008). Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns. In M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stutzle, & A. Winfield (Eds.), Lecture Notes in Computer Science.
  • Detecting Activities for Assisted Living
    Monekosso, D., & Remagnino, P. (2008). Detecting Activities for Assisted Living. In M. Muhlhauser, A. Ferscha, & E. Aitenbichler (Eds.), Communications in Computer and Information Science (pp. 228-237). https://doi.org/10.1007/978-3-540-85379-4%5C_28
  • A Hierarchical Model-Based System for Discovering Atypical Behavior
    Monekosso, D. N. (2008). A Hierarchical Model-Based System for Discovering Atypical Behavior. Presented at 2008 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT, VOLS 1 AND 2 IEEE.

Journal Article

Supervision students

Strahinja Klem

Postgraduate Student