Human-robot interaction (HRI) is a multidisciplinary field that studies how humans and robots can communicate and collaborate effectively and naturally. Gesture recognition is one of the key components of HRI, as it enables humans to use intuitive and expressive body motions to convey commands, intentions, and emotions to robots. However, most of the existing gesture recognition methods rely on vision-based sensors, such as cameras, that have limitations in terms of occlusion, illumination, privacy, and computational cost.

The aim of this PhD project is to develop novel methods for inertial sensor-based gesture recognition for HRI.

The project will involve the following objectives:

• Review the state-of-the-art methods and challenges of inertial sensor-based gesture recognition for HRI.

• Develop new methods for gesture segmentation, classification, and generation using inertial sensors, such as accelerometers and gyroscopes, worn on the human body.

• Evaluate the performance and usability of the proposed methods on various HRI scenarios and tasks, such as navigation, manipulation, social interaction, and entertainment.

• Investigate the human factors and ethical issues of using inertial sensors for gesture recognition for HRI.

The successful candidate will have a strong background in computer science, engineering, or mathematics, with good programming skills in Python or C++, and good knowledge of machine learning. Experience in inertial sensor data processing, machine learning, or human-robot interaction is desirable but not essential. The candidate will be supervised by Dr. Lucas Fonseca and Prof Praminda Caleb-Solly  from the School of Computer Science, and will have access to the state-of-the-art facilities and resources of the CHART research group.

Human-machine interfaces (HMIs) are systems that enable humans to interact with machines, such as computers, robots, or assistive devices, using various modalities, such as speech, touch, or gesture. HMIs have many applications, such as entertainment, education, health, and industry. However, most of the existing HMIs are based on small and predefined set of actions, which limit the naturalness and expressiveness of the human-machine interaction. In addition, they often don't consider the user's limitations.

The aim of this PhD project is to explore the use of human movement as a strategy for designing and evaluating novel HMIs that can adapt to the user’s preferences, context, and goals. 

The project will involve the following objectives:

• Review the state-of-the-art methods and challenges of using human movement for HMIs.

• Develop new methods for capturing, analyzing, and synthesizing human movement data using various sensors, such as inertial sensors, motion capture systems, or cameras.

• Design and implement novel HMIs that use human movement as an input or output modality for various tasks and domains, such as gaming, education, or rehabilitation.

• Evaluate the usability and user experience of the proposed HMIs using quantitative and qualitative methods.

The successful candidate will have a strong background in computer science, engineering, or design, with good programming skills in Python or C++. Experience in human movement analysis, machine learning, or human-computer interaction is desirable but not essential. The candidate will be supervised by Dr. Lucas Fonseca and Prof Praminda Caleb-Solly  from the School of Computer Science, and will have access to the state-of-the-art facilities and resources of the CHART research group.