About this Research Topic
Recent advances in human motion sensing technologies and machine learning have enhanced the potential of Artificial Intelligence to improve our quality of life, increase productivity and reshape multiple industries, including cultural and creative industries. In order to achieve this goal, humans must remain at the centre of Artificial Intelligence and AI should learn from humans and collaborate effectively with them. Human-Centred Artificial Intelligence (HAI) is expected to create new opportunities and challenges in the future, which cannot yet be foreseen. Any type of machine or object (e.g. robots, computers, autonomous vehicles, drones, Internet of Things, etc.) will have different layers of perception and sophisticated HAI algorithms, that will detect human intentions and behaviours and learn continuously from them. Thus, every single intelligent machine and object will be able to capture human motions, analyse them, detect poses and recognise gestures and activities, including facial expressions and gaze, enabling natural collaboration with humans.
HAI is currently at the centre of scientific debates and technological exhibitions. Developing and deploying intelligent machines is definitely both an economic challenge (e.g. flexibility, simplification, ergonomy) as well as a societal challenge (e.g. safety, transparency), not only from a factory perspective, but also for the real-world in general. This Research Topic will focus on the most recent advances in the area of HAI and on how machines understand humans and naturally interact and collaborate with them.
Contributions to the Research Topic are sought in all areas where humans interact with machines and objects through HAI, including but not limited to:
- Effective industrial human-robot collaboration
- Human collaboration with creative robotics
- HAI for motion capturing
- Gesture and activity recognition
- Deep learning for pose estimation and scene understanding
- AI-based sensori-motor human learning
- Human modelling using HAI
- HAI and robotics in an ageing society for well-being
- Movement-based interaction with autonomous vehicles
- Movement-based interaction in Creative and Cultural Industries
- Emotion and engagement recognition for HAI
- Human-centred adaptation and personalisation of AI
- Augmented worker capabilities and machines
- Ethical, transparent and comprehensive HAI
HAI is currently at the centre of scientific debates and technological exhibitions. Developing and deploying intelligent machines is definitely both an economic challenge (e.g. flexibility, simplification, ergonomy) as well as a societal challenge (e.g. safety, transparency), not only from a factory perspective, but also for the real-world in general. This Research Topic will focus on the most recent advances in the area of HAI and on how machines understand humans and naturally interact and collaborate with them.
Contributions to the Research Topic are sought in all areas where humans interact with machines and objects through HAI, including but not limited to:
- Effective industrial human-robot collaboration
- Human collaboration with creative robotics
- HAI for motion capturing
- Gesture and activity recognition
- Deep learning for pose estimation and scene understanding
- AI-based sensori-motor human learning
- Human modelling using HAI
- HAI and robotics in an ageing society for well-being
- Movement-based interaction with autonomous vehicles
- Movement-based interaction in Creative and Cultural Industries
- Emotion and engagement recognition for HAI
- Human-centred adaptation and personalisation of AI
- Augmented worker capabilities and machines
- Ethical, transparent and comprehensive HAI
Keywords: Human-Centered Artificial Intelligence, Human Robot Collaboration, Human Motion Analysis, Cultural and Creative Industries, Industry 4.0
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
