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Intelligent data processing to support self-management and responsive care (2022)
Thesis
Gupta, P. Intelligent data processing to support self-management and responsive care. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/7563714

This research is situated in the area of ambient intelligent systems for assisted living. The motivation for the research was to understand how ambient intelligent systems could be used to support people with learning disabilities in providing more p... Read More about Intelligent data processing to support self-management and responsive care.

Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods (2020)
Journal Article
Caleb-Solly, P., Gupta, P., & McClatchey, R. (2020). Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods. Neural Computing and Applications, 32(16), 12351 - 12362. https://doi.org/10.1007/s00521-020-04737-6

© 2020, The Author(s). This paper investigates the utility of unsupervised machine learning and data visualisation for tracking changes in user activity over time. This is done through analysing unlabelled data generated from passive and ambient smar... Read More about Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods.

A framework for semi-supervised adaptive learning for activity recognition in healthcare applications (2018)
Conference Proceeding
Gupta, P., & Caleb-Solly, P. (2018). A framework for semi-supervised adaptive learning for activity recognition in healthcare applications. In Engineering Applications of Neural Networks. , (3-15). https://doi.org/10.1007/978-3-319-98204-5_1

With the growing popularity of the Internet of Things and connected home products, potential healthcare applications in a smart-home context for assisted living are becoming increasingly apparent. However, challenges in performing real-time human act... Read More about A framework for semi-supervised adaptive learning for activity recognition in healthcare applications.