A decision support system for self management of low back pain

A knowledge-light approach to personalised and open-ended human activity recognition. (2020)
Journal Article
WIJEKOON, A., WIRATUNGA, N., SANI, S. and COOPER, K. 2020. A knowledge-light approach to personalised and open-ended human activity recognition. Knowledge-based systems [online], 192, article ID 105651. Available from: https://doi.org/10.1016/j.knosys.2020.105651

Human Activity Recognition (HAR) is a core component of clinical decision support systems that rely on activity monitoring for self-management of chronic conditions such as Musculoskeletal Disorders. Deployment success of such applications in part de... Read More about A knowledge-light approach to personalised and open-ended human activity recognition..

Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. (2020)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N. and COOPER, K. 2020. Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9206941. Available from: https://doi.org/10.1109/IJCNN48605.2020.9206941

Exercise adherence is a key component of digital behaviour change interventions for the self-management of musculoskeletal pain. Automated monitoring of exercise adherence requires sensors that can capture patients performing exercises and Machine Le... Read More about Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition..

Learning to compare with few data for personalised human activity recognition. (2020)
Conference Proceeding
WIRATUNGA, N., WIJEKOON, A. and COOPER, K. 2020. Learning to compare with few data for personalised human activity recognition. In Watson, I and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 3-14. Available from: https://doi.org/10.1007/978-3-030-58342-2_1

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist... Read More about Learning to compare with few data for personalised human activity recognition..

Learning deep and shallow features for human activity recognition. (2017)
Conference Proceeding
SANI, S., MASSIE, S., WIRATUNGA, N. and COOPER, K. 2017. Learning deep and shallow features for human activity recognition. In Li, G., Ge, Y, Zhang, Z., Jin, Z. and Blumenstein, M. (eds.) Knowledge science, engineering and management: proceedings of the 10th International knowledge science, engineering and management conference (KSEM 2017), 19-20 August 2017, Melbourne, Australia. Lecture notes in computer science, 10412. 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 to prescribed physical a... Read More about Learning deep and shallow features for human activity recognition..

kNN sampling for personalised human recognition. (2017)
Conference Proceeding
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2017. kNN sampling for personalised human recognition. In Aha, D.W. and Lieber, J. (eds.) Case-based reasoning research and development: proceedings of the 25th International case-based reasoning conference (ICCBR 2017), 26-28 June 2017, Trondheim, Norway. Lecture notes in computer science, 10339. 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 types and intensities to enab... Read More about kNN sampling for personalised human recognition..

Matching networks for personalised human activity recognition. (2018)
Conference Proceeding
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Matching networks for personalised human activity recognition. In Bichindaritz, I., Guttmann, C., Herrero, P., Koch, F., Koster, A., Lenz, R., López Ibáñez, B., Marling, C., Martin, C., Montagna, S., Montani, S., Reichert, M., Riaño, D., Schumacher, M.I., ten Teije, A. and Wiratunga, N. (eds.) Proceedings of the 1st Joint workshop on artificial intelligence in health, organized as part of the Federated AI meeting (FAIM 2018), co-located with the 17th International conference on autonomous agents and multiagent systems (AAMAS 2018), the 35th International conference on machine learning (ICML 2018), the 27th International joint conference on artificial intelligence (IJCAI 2018), and the 26th International conference on case-based reasoning (ICCBR 2018), 13-19 July 2018, Stockholm, Sweden. CEUR workshop proceedings, 2142. Aachen: CEUR-WS [online], pages 61-64. Available from: http://ceur-ws.org/Vol-2142/short4.pdf

Human Activity Recognition (HAR) has many important applications in health care which include management of chronic conditions and patient rehabilitation. An important consideration when training HAR models is whether to use training data from a gene... Read More about Matching networks for personalised human activity recognition..

Barriers and facilitators to patient uptake and utilisation of digital interventions for the self-management of low back pain: a systematic review of qualitative studies. (2020)
Journal Article
SVENDSEN, M.J., WOOD, K.W., KYLE, J., COOPER, K., RASMUSSEN, C.D.N., SANDAL, L.F., STOCHKENDAHL, M.J., MAIR, F.S. and NICHOLL, B.I. 2020. Barriers and facilitators to patient uptake and utilisation of digital intervientions for the self-management of low back pain: a systematic review of qualitative studies. BMJ open [online], 10(12), article ID e038800. Available from: https://doi.org/10.1136/bmjopen-2020-038800

Objectives: Low back pain (LBP) is a leading contributor to disability globally. Self-management is a core component of LBP management. We aimed to synthesise published qualitative literature concerning digital health interventions (DHIs) to support... Read More about Barriers and facilitators to patient uptake and utilisation of digital interventions for the self-management of low back pain: a systematic review of qualitative studies..