SelfBACK is a five-year project funded by the European Union Horizon 2020 programme. It began in January 2016 with the aim of developing a monitoring system to assist patients in deciding and reinforcing appropriate self management plans, in order to self-mange low back pain (LBP). The selfBACK system is a predictive case-based reasoning system. It is the first of its kind, utilising background data about the patient, patient self-monitoring of pain and functional ability, along with continuous recording of the patient's physical activity and sleep through use of a wristband. The decision support will be conveyed to the patient via a smartphone app. The advice will be tailored to each patient based on the symptom state, symptom progression, the patients goal-setting, and a range of patient characteristics, including information from the physical activity-detecting wristband worn by the patient. With selfBACK, the patient will be equipped with a tool that is far beyond the state-of-the-art to facilitate, improve and reinforce self-management of non-specific low back pain. The second part of the project will evaluate the effectiveness of selfBACK in a randomized controlled trial using pain-related disability as primary outcome. Partners in the selfBACK consortium have expertise in the field of low back pain, healthcare innovation, app development, computer science and artifical intelligence. Norwegian University of Science and Technology take the lead in this project and, as well as Robert Gordon University, other partners include Syddansk University, Det Nationale Forskningscenter for Arbejdsmiljø and Trade eXansion (Denmark), University of Glasgow and Health Leads (Netherlands).

Recent Submissions

  • CSDM: SelfBACK: self-management of low back pain. [Project website] 

    School of Computing and Digital Media (Robert Gordon University http://www.comp.rgu.ac.uk/selfback/, 2016-01-01)
    SCHOOL OF COMPUTING SCIENCE AND DIGITAL MEDIA 2016. CSDM: SelfBACK: self-management of low back pain. Aberdeen: Robert Gordon University [online]. Available from: http://www.comp.rgu.ac.uk/selfback/
  • SelfBACK: a decision support system to improve self-management of non-specific low back pain. [Project website] 

    SelfBACK Project (SelfBACK http://www.selfback.eu/, 2016-01-01)
    SELFBACK PROJECT. 2016. SelfBACK: a decision support system to improve self-management of non-specific low back pain. Norway: SelfBACK [online]. Available from: http://www.selfback.eu/
    This is the official project website for the SelfBack project. The project will run from Jan 2016 – Dec 2020. The aim of the project is to improve self-management of non-specific low back pain through the development of a ...
  • Accuracy of physical activity recognition from a wrist-worn sensor. 

    Cooper, Kay; Sani, Sadiq; Corrigan, Liam; MacDonald, Haley; Prentice, Chris; Vareta, Rob; Massie, Stewart; Wiratunga, Nirmalie (Physiotherapy UK http://www.physiotherapyuk.org.uk/, 2017-11-10)
    COOPER, K., SANI, S., CORRIGAN, L., MACDONALD, H., PRENTICE, C., VARETA, R., MASSIE, S. and WIRATUNGA, N. 2017. Accuracy of physical activity recognition from a wrist-worn sensor. Presented at the Physiotherapy UK conference and trade exhibition 2017: transform lives, maximise independence and empower populations, 10-11 November 2017, Birmingham, UK.
    The EU-funded project 'selfBACK' (http://www.selfback.eu) will utilise continuous objective monitoring of physical activity (PA) by a wrist-mounted wearable, combined with self-monitoring of symptoms and case-based reasoning. ...
  • A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. 

    Martin, Kyle; Wiratunga, Nirmalie; Sani, Sadiq; Massie, Stewart; Clos, Jérémie (ICCBR (Organisers), 2017-06-26)
    MARTIN, K., WIRATUNGA, N., SANI, S., MASSIE, S. and CLOS, J. 2017. A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. Presented at the 25th international case-based reasoning conference (ICCBR 2017): case-based reasoning and deep learning workshop (CBRDL 2017), 26 June 2017, Trondheim, Norway.
    The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the differences between their features to ...
  • Learning deep features for kNN-based human activity recognition. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart (ICCBR (Organisers), 2017-06-26)
    SANI, S., WIRATUNGA, N. and MASSIE, S. 2017. Learning deep features for kNN-based human activity recognition. Presented at the 25th international case-based reasoning conference (ICCBR 2017): case-based reasoning and deep learning workshop (CBRDL 2017), 26-28 June 2017, Trondheim, Norway.
    A CBR approach to Human Activity Recognition (HAR) uses the kNN algorithm to classify sensor data into different activity classes. Different feature representation approaches have been proposed for sensor data for the ...
  • kNN sampling for personalised human recognition. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay (Springer https://doi.org/10.1007/978-3-319-61030-6_23, 2017-06-21)
    SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2017. kNN sampling for personalised human recognition. Lecture notes in computer science, 10339, case-based reasoning research and development: proceedings of the 25th international case-based reasoning conference (ICCBR 2017), 26-28 June 2017, Trondheim, Norway. Cham: Springer [online], pages 330-344. Available from: https://doi.org/10.1007/978-3-319-61030-6_23
    The need to adhere to recommended physical activity guidelines for a variety of chronic disorders calls for high precision Human Activity Recognition (HAR) systems. In the SelfBACK system, HAR is used to monitor activity ...
  • Learning deep and shallow features for human activity recognition. 

    Sani, Sadiq; Massie, Stewart; Wiratunga, Nirmalie; Cooper, Kay (Springer https://doi.org/10.1007/978-3-319-63558-3_40, 2017-07-19)
    SANI, S., MASSIE, S., WIRATUNGA, N. and COOPER, K. 2017. Learning deep and shallow features for human activity recognition. Lecture notes in artificial intelligence, 10412: proceedings of the 10th international knowledge science engineering and management conference (KESEM 2017), 19-20 August 2017, Melbourne, Australia. Cham: Springer [online], pages 469-482. Available from: https://doi.org/10.1007/978-3-319-63558-3_40
    selfBACK is an mHealth decision support system used by patients for the self-management of Lower Back Pain. It uses Human Activity Recognition from wearable sensors to monitor user activity in order to measure their adherence ...
  • SelfBACK: Activity recognition for self-management of low back pain. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay (Springer https://dx.doi.org/10.1007/978-3-319-47175-4, 2016-11-05)
    SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2016. SelfBACK: Activity recognition for self-management of low back pain. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (AI-2016), 13-15 December 2016, Cambridge, UK. Cham: Springer [online], pages 281-294. Available from: http://dx.doi.org/10.1007/978-3-319-47175-4
    Low back pain (LBP) is the most significant contributor to years lived with disability in Europe and results in significant financial cost to European economies. Guidelines for the management of LBP have self-management ...