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Outputs (17)

Improving kNN for human activity recognition with privileged learning using translation models. (2018)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., SANI, S., MASSIE, S. and COOPER, K. 2018. Improving kNN for human activity recognition with privileged learning using translation models. In Cox, M.T., Funk, P. and Begum, S. (eds.) Case-based reasoning research and development: proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Lecture notes in computer science, 11156. Cham: Springer [online], pages 448-463. Available from: https://doi.org/10.1007/978-3-030-01081-2_30

Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is preferred by consumers as it is more convenient and less intrusive. This presents a challenge to researchers, as... Read More about Improving kNN for human activity recognition with privileged learning using translation models..

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..

Study of similarity metrics for matching network-based personalised human activity recognition. (2018)
Presentation / Conference
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Study of similarity metrics for matching network-based personalised human activity recognition. In Minor, M. (ed.) Workshop proceedings for the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden, pages 91-95. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=91

Personalised Human Activity Recognition (HAR) models trained using data from the target user (subject-dependent) have been shown to be superior to non personalised models that are trained on data from a general population (subject-independent). Howev... Read More about Study of similarity metrics for matching network-based personalised human activity recognition..

Accuracy of physical activity recognition from a wrist-worn sensor. (2017)
Presentation / Conference
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 2017 Physiotherapy UK conference and trade exhibition: 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. Together these will prov... Read More about Accuracy of physical activity recognition from a wrist-worn sensor..

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..

SelfBACK: Activity recognition for self-management of low back pain. (2016)
Conference Proceeding
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 (SGAI 2016), 13-15 December 2016, Cambridge, UK. Cham: Springer [online], pages 281-294. Available from: https://doi.org/10.1007/978-3-319-47175-4_21

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 at their cornerstone, where pa... Read More about SelfBACK: Activity recognition for self-management of low back pain..