As a convenient and natural way of human-computer interaction, gesture recognition
technology has broad research and application prospects in many fields, such as intelligent …

Millimeter-wave (mmWave) radar sensors are a promising modality for gesture recognition
as they can overcome several limitations of optic sensors typically used for gesture …

Radar sensing can penetrate non-conducting materials, such as glass, wood, and plastic,
which makes it appropriate for recognizing gestures in environments with poor visibility …

In many industrial or healthcare contexts, kee** track of the number of people is essential.
Radar systems, with their low overall cost and power consumption, enable privacy-friendly …

In-air handwriting necessitates consistent motion tracking, in contrast to millimeter-wave
(mmWave) radar-based simple gesture recognition techniques. However, during long …

Ultrasonic dynamic hand gesture recognition (U-DHGR) is a promising approach of human–
computer interaction (HCI) for a broad range of emerging applications. Cross-user …

Radar sensors offer power-efficient solutions for always-on smart devices, but processing
the data streams on resource-constrained embedded platforms remains challenging. This …

We develop Few-Shot Learning models trained to recognize five or ten different dynamic
hand gestures, respectively, which are arbitrarily interchangeable by providing the model …

Detecting Out-of-Distribution (OOD) gestures is vital for reliable radar-based gesture-
recognition systems. Traditional autoencoders often fall short in OOD detection because …

Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and
Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited …