All Outputs (43)

Employing multi-modal sensors for personalised smart home health monitoring. (2022)
Thesis
FORBES, G. 2022. Employing multi-modal sensors for personalised smart home health monitoring. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2071646

Smart home systems are employed worldwide for a variety of automated monitoring tasks. FITsense is a system that performs personalised smart home health monitoring using sensor data. In this thesis, we expand upon this system by identifying the limit... Read More about Employing multi-modal sensors for personalised smart home health monitoring..

Visualisation to explain personal health trends in smart homes. (2021)
Presentation / Conference
FORBES, G., MASSIE, S. and CRAW, S. 2021. Visualisation to explain personal health trends in smart homes. Presented at 1st eXplainable artificial intelligence (XAI) in healthcare international workshop 2021 (XAI-Healthcare 2021), 16 June 2021, co-located with 19th Artificial intelligence in medicine (AIME) international conference 2021 (AIME 2021), 15-17 June 2021, [virtual conference]. Hosted on ArXiv [online], article 2109.15125. Available from: https://arxiv.org/abs/2109.15125

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical prof... Read More about Visualisation to explain personal health trends in smart homes..

Wifi-based human activity recognition using Raspberry Pi. (2020)
Conference Proceeding
FORBES, G., MASSIE, S. and CRAW, S. 2020. Wifi-based human activity recognition using Raspberry Pi. In Alamaniotis, M. and Pan, S. (eds.) Proceedings of Institute of Electrical and Electronics Engineers (IEEE) 32nd Tools with artificial intelligence international conference 2020 (ICTAI 2020), 9-11 Nov 2020, [virtual conference]. Piscataway: IEEE [online], pages 722-730. Available from: https://doi.org/10.1109/ICTAI50040.2020.00115

Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake... Read More about Wifi-based human activity recognition using Raspberry Pi..

Representing temporal dependencies in smart home activity recognition for health monitoring. (2020)
Conference Proceeding
FORBES, G., MASSIE, S., CRAW, S., FRASER, L. and HAMILTON, G. 2020. Representing temporal dependencies in smart home activity recognition for health monitoring. 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 9207480. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207480

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which m... Read More about Representing temporal dependencies in smart home activity recognition for health monitoring..

Representing temporal dependencies in human activity recognition. (2020)
Conference Proceeding
FORBES, G., MASSIE, S., CRAW, S., FRASER, L. and HAMILTON, G. 2019. Representing temporal dependencies in human activity recognition. In Kapetanakis, S. and Borck, H. (eds.) Proceedings of the 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19), 8-12 September 2019, Otzenhausen, Germany. CEUR workshop proceedings, 2567. Aachen: CEUR-WS [online], pages 29-38. Available from: http://ceur-ws.org/Vol-2567/paper3.pdf

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A pro... Read More about Representing temporal dependencies in human activity recognition..

Fall prediction using behavioural modelling from sensor data in smart homes. (2019)
Journal Article
FORBES, G., MASSIE, S. and CRAW, S. 2020. Fall prediction using behavioural modelling from sensor data in smart homes. Artificial intelligence review [online], 53(2), pages 1071-1091. Available from: https://doi.org/10.1007/s10462-019-09687-7

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however... Read More about Fall prediction using behavioural modelling from sensor data in smart homes..

Case based reasoning as a model for cognitive artificial intelligence. (2018)
Conference Proceeding
CRAW, S. and AAMODT, A. 2018. Case based reasoning as a model for cognitive artificial intelligence. 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 62-77. Available from: https://doi.org/10.1007/978-3-030-01081-2_5

Cognitive Systems understand the world through learning and experience. Case Based Reasoning (CBR) systems naturally capture knowledge as experiences in memory and they are able to learn new experiences to retain in their memory. CBR's retrieve and r... Read More about Case based reasoning as a model for cognitive artificial intelligence..

FITsense: employing multi-modal sensors in smart homes to predict falls. (2018)
Conference Proceeding
MASSIE, S., FORBES, G., CRAW, S., FRASER, L. and HAMILTON, G. 2018. FITsense: employing multi-modal sensors in smart homes to predict falls. 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 249-263. Available from: https://doi.org/10.1007/978-3-030-01081-2_17

As people live longer, the increasing average age of the population places additional strains on our health and social services. There are widely recognised benefits to both the individual and society from supporting people to live independently for... Read More about FITsense: employing multi-modal sensors in smart homes to predict falls..

Maintenance of case bases: current algorithms after fifty years. (2018)
Conference Proceeding
JUAREZ, J.M., CRAW, S., LOPEZ-DELGADO, J.R. and CAMPOS, M. 2018. Maintenance of case bases: current algorithms after fifty years. In Lang, J. (ed.) Proceedings of the 27th International joint conference on artificial intelligence and the 23rd European conference on artificial intelligence (IJCAI-ECAI 2018), 13-19 July 2018, Stockholm, Sweden. Freiburg: IJCAI [online], pages 5457-5463. Available from: https://doi.org/10.24963/ijcai.2018/770

Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from modelbased systems since it can learn incrementally as new data is available, storing new cases in its casebase. This mea... Read More about Maintenance of case bases: current algorithms after fifty years..

Monitoring health in smart homes using simple sensors. (2018)
Conference Proceeding
MASSIE, S., FORBES, G., CRAW, S., FRASER, L. and HAMILTON, G. 2018. Monitoring health in smart homes using simple sensors. In Bach, K., Bunescu, R., Farri, O., Guo, A., Hasan, S., Ibrahim, Z.M., Marling, C., Raffa, J., Rubin, J. and Wu, H. (eds.) Proceedings of the 3rd International workshop on knowledge discovery in healthcare data (KDH), co-located with the 27th International joint conference on artificial intelligence and the 23rd European conference on artificial intelligence (IJCAI-ECAI 2018), 13 July 2018, Stockholm, Sweden. CEUR workshop proceedings, 2148. Aachen: CEUR-WS [online], pages 33-37. Available from: http://ceur-ws.org/Vol-2148/paper05.pdf

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to bot... Read More about Monitoring health in smart homes using simple sensors..

Knowledge driven approaches to e-learning recommendation. (2018)
Thesis
MBIPOM, B. 2018. Knowledge driven approaches to e-learning recommendation. Robert Gordon University, PhD thesis.

Learners often have difficulty finding and retrieving relevant learning materials to support their learning goals because of two main challenges. The vocabulary learners use to describe their goals is different from that used by domain experts in tea... Read More about Knowledge driven approaches to e-learning recommendation..

An e-learning recommender that helps learners find the right materials. (2018)
Conference Proceeding
MBIPO, B., MASSIE, S. and CRAW, S. 2018. An e-learning recommender that helps learners find the right materials. In Zilberstein, S., McIlraith, S., Weinberger, K., Youngblood, G.M., Myers, K., Eaton, E. and Wollowski, M. (eds.) Proceedings of the 32nd Association for the Advancement of Artificial Intelligence (AAAI) Artificial intelligence conference (AAAI18), co-located with the 30th Innovative applications of artificial intelligence conference (IAAI18) and the 8th AAAI Educational advances in artificial intelligence (EAAI-18), 2-7 February 2018, New Orleans, Louisiana, USA. Palo Alto: AAAI Press [online], pages 7928-7933. Available from: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16253

Learning materials are increasingly available on the Web making them an excellent source of information for building e-Learning recommendation systems. However, learners often have difficulty finding the right materials to support their learning goal... Read More about An e-learning recommender that helps learners find the right materials..

Improving e-learning recommendation by using background knowledge. (2018)
Journal Article
MBIPOM, B., CRAW, S. and MASSIE, S. 2021. Improving e-learning recommendation by using background knowledge. Expert systems [online], 38(7): artificial intelligence/EDMA 2017, article e12265. Available from: https://doi.org/10.1111/exsy.12265

There is currently a large amount of e-Learning resources available to learners on the Web. However, learners often have difficulty finding and retrieving relevant materials to support their learning goals because they lack the domain knowledge to cr... Read More about Improving e-learning recommendation by using background knowledge..

Harnessing background knowledge for e-learning recommendation. (2016)
Conference Proceeding
MBIPOM, B., CRAW, S. and MASSIE, S. 2016. Harnessing background knowledge for e-learning recommendation. 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 3-17. Available from: https://dx.doi.org/10.1007/978-3-319-47175-4_1

The growing availability of good quality, learning-focused content on the Web makes it an excellent source of resources for e-learning systems. However, learners can find it hard to retrieve material well-aligned with their learning goals because of... Read More about Harnessing background knowledge for e-learning recommendation..

Music recommendation: audio neighbourhoods to discover music in the long tail. (2015)
Conference Proceeding
CRAW, S., HORSBURGH, B. and MASSIE, S. 2015. Music recommendation: audio neighbourhoods to discover music in the long tail. In Hüllermeier, E. and Minor, M. (eds.) Proceedings of the 23rd international conference on case-based reasoning research and development (ICCBR 2015), 28-30 September 2015, Frankfurt am Main, Germany. Lecture notes in computer science, 9343. Cham: Springer [online], pages 73-87. Available from: https://doi.org/10.1007/978-3-319-24586-7_6

Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already... Read More about Music recommendation: audio neighbourhoods to discover music in the long tail..

Case-base maintenance with multi-objective evolutionary algorithms. (2015)
Journal Article
LUPIANI, E., MASSIE, S., CRAW, S., JUAREZ, J.M. and PALMA, J. 2016. Case-base maintenance with multi-objective evolutionary algorithms. Journal of intelligent information systems [online], 46(2), pages 259-284. Available from: https://doi.org/10.1007/s10844-015-0378-z

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solvi... Read More about Case-base maintenance with multi-objective evolutionary algorithms..

Music recommenders: user evaluation without real users? (2015)
Conference Proceeding
CRAW, S., HORSBURGH, B. and MASSIE, S. 2015. Music recommenders: user evaluation without real users? In Yang, Q. and Woolridge, M. (eds.) Proceedings of the 24th International joint conference on artificial intelligence (IJCAI-15), 25-31 July 2015, Buenos Aires, Argentina. Palo Alto: AAAI Press [online], pages 1749-1755. Available from: https://www.ijcai.org/Proceedings/15/Papers/249.pdf

Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can be expensive, and may have difficu... Read More about Music recommenders: user evaluation without real users?.

Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. (2014)
Journal Article
HORSBURGH, B., CRAW, S. and MASSIE, S. 2015. Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artificial intelligence [online], 219, pages 25-39. Available from: https://doi.org/10.1016/j.artint.2014.11.004

Online recommender systems are an important tool that people use to find new music. To generate recommendations, many systems rely on tag representations of music. Such systems however suffer from tag sparsity, whereby tracks lack a strong tag repres... Read More about Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems..

Integrating content and semantic representations for music recommendation. (2013)
Thesis
HORSBURGH, B. 2013. Integrating content and semantic representations for music recommendation. Robert Gordon University, PhD thesis.

Music recommender systems are used by millions of people every day to discover new and exciting music. Central to making recommendations is the representation of each track, which may be used to calculate similarity. Content representations capture t... Read More about Integrating content and semantic representations for music recommendation..

A multi-objective evolutionary algorithm fitness function for case-base maintenance. (2013)
Conference Proceeding
LUPIANI, E., CRAW, S., MASSIE, S., JUAREZ, J.M. and PALMA, J.T. 2013. A multi-objective evolutionary algorithm fitness function for case-base maintenance. In Delany, S.J. and Ontañón, S. (eds.) Case-based reasoning research and development: proceedings of the 21st International conference on case-based reasoning (ICCBR 2013), 8-11 July 2013, Saratoga Springs, USA. Lecture notes in computer science, 7969. Berlin: Springer [online], pages 218-232. Available from: https://doi.org/10.1007/978-3-642-39056-2_16

Case-Base Maintenance (CBM) has two important goals. On the one hand, it aims to reduce the size of the case-base. On the other hand, it has to improve the accuracy of the CBR system. CBM can be represented as a multi-objective optimization problem t... Read More about A multi-objective evolutionary algorithm fitness function for case-base maintenance..