- Designing effective WiFi feature templates (e.g., angle of arrival, Doppler frequency shift) to capture human behaviour information while suppressing noise caused by various factors such as multipath effect.
- Developing state-of-the-art cross-modality learning algorithms that can leverage other potential sensing modalities (e.g., 3D skeleton extracted from videos) to provide supervision information for representation learning.
- Developing practical WiFi(-vision) based HAR systems that can be generalised to different environments in real-world scenarios. The student can begin with existing public datasets (e.g., the MM-FI dataset), but will later need to collect their own dataset that includes real-world challenges (with multiple subjects, in less controlled environments). Additional new RF sensing modalities (such as mmWave) can also be collected and incorporated into modelling.
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PhD Studentship in WiFi-Vision Cross-modality Learning for Human Activity Recognition - Coventry, United Kingdom - University of Warwick
Description
Human activity recognition (HAR) is a compelling topic in the fields of ubiquitous computing, with numerous applications including human behaviour understanding, smart healthcare, human-computer-interaction (HCI), and more. Among various sensing technologies, Radio Frequency (RF) signals such as WiFi can be applied in a less intrusive manner, offering significant potential in smart home environments. However, due to multipath propagation, WiFi signals often contain a high level of noise from physical environments that need to be suppressed before developing HAR models.
In this PhD project, the student will investigate the following three main directions:
Please contact Dr Yu Guan ) in the first instance.
Funding Details
Home fees paid and an annual stipend at UKRI rate for 3.5 years. EU/International applicants are welcome to apply but will be required to cover the difference between home and EU/international fees.
UKRI rate for 3.5 years
