About this Research Topic
Benefiting from the rapid advancement of electronics in recent decades, the links between humans and machines grow ever closer. In the foreseeable future, our human-machine interaction (HMI) experiences are expected to change dramatically. E.g., from the conventional user-input-based devices, such as keyboards and touchpad, to sophisticated wearable electronic devices that can detect dexterous body motions and create virtual interaction with the users. Using microchips, a HMI that connects our minds to the digital world is looking more promising than ever. With the booming of artificial intelligence, neural interfaces, and nano-fabrications, the next generation of smart human-machine interfaces could once again revolutionize the way we interact with the world and benefit our life tremendously.
This research topic aims to highlight emerging technologies such as machine learning, neural interfaces, and electronics beyond CMOS that can create a seamless link between human and smart systems in the upcoming 5G connected and AI-empowered world. We also want to encourage destructive technologies that can impact the current commercial HMIs, such as haptic feedback, virtual reality, and augmented reality, towards a fully immersive wearable experience. We are interested to see efficient and elegant ways of information exchange between physiological signals from human bodies to electronic signals towards brain-machine interaction with novel applications such as advanced medical diagnostics and treatment for neurological disorders in personalized health care.
Areas of interest for this Research Topic may include (but are not limited to):
- Large scale neural stimulation and recording
- AI and edge computing for health care
- Memristor enabled neural network
- Neuromorphic hardware for smart sensing
- Body sensor networks and medical robots
- Bio-impedance system in HMI
- Material and devices for wearable electronics such as organic electronics, super-conductors, flexible and printable sensors
- Emerging technologies for neurological disorders
- Disruptive imaging diagnostic techniques for healthcare
- Immersive wearables, virtual reality (VR) and augmented reality (AR)
- Pervasive or non-contact health monitoring devices
This research topic aims to highlight emerging technologies such as machine learning, neural interfaces, and electronics beyond CMOS that can create a seamless link between human and smart systems in the upcoming 5G connected and AI-empowered world. We also want to encourage destructive technologies that can impact the current commercial HMIs, such as haptic feedback, virtual reality, and augmented reality, towards a fully immersive wearable experience. We are interested to see efficient and elegant ways of information exchange between physiological signals from human bodies to electronic signals towards brain-machine interaction with novel applications such as advanced medical diagnostics and treatment for neurological disorders in personalized health care.
Areas of interest for this Research Topic may include (but are not limited to):
- Large scale neural stimulation and recording
- AI and edge computing for health care
- Memristor enabled neural network
- Neuromorphic hardware for smart sensing
- Body sensor networks and medical robots
- Bio-impedance system in HMI
- Material and devices for wearable electronics such as organic electronics, super-conductors, flexible and printable sensors
- Emerging technologies for neurological disorders
- Disruptive imaging diagnostic techniques for healthcare
- Immersive wearables, virtual reality (VR) and augmented reality (AR)
- Pervasive or non-contact health monitoring devices
Keywords: human-machine interaction, brain-interface, immersive wearables, AI for health care, beyond CMOS
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.
