Identifying human activities in an automatic and non-intrusive manner is an important and emerging area of research. With the rapid development of the Internet of Things (IoT), combined with advances in machine/deep learning, technology-based solutions to automatically detect and model human activities are now becoming possible.
Recently, there has been a move toward edge computing as a way of reducing communication latency and network communication whilst preserving privacy. Various solutions have been developed to support modelling of human activities. In particular, deep learning algorithms have shown high performance, however, typically require large amounts of computation for training and inference, making them unsuitable for deployment on resource constrained edge devices. Devices in a resource-constrained environment become even more challenging when they are battery powered, such is the case with wearable applications, making them computationally intensive and power demanding.
This project will research the modelling of human activity recognition on computationally constrained devices. The project will develop architectures, techniques, tools, and approaches for on device modelling of human activities. This will include investigating new and novel sensing modalities (audio, vision, environment and health), sensor fusion, computational constraints reduction and model optimization, through federated and distributed learning. The performance of these solutions will be evaluated in real-world settings with diverse populations.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
- Sound understanding of subject area as evidenced by a comprehensive research proposal
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 65%
- Work experience relevant to the proposed project
- Publications - peer-reviewed