Sensor Systems and Sensing Technologies for Gesture Recognition
A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Intelligent Sensors".
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Editor
Interests: wearable sensors; brain–computer interface; motion tracking; gait analysis; sensory glove; biotechnologies
Special Issues, Collections and Topics in MDPI journals
Topical Collection Information
Dear Colleagues,
The rapid evolution of spatial computing, extended reality (XR), and smart environments has propelled Sensor Systems and Sensing Technologies for Gesture Recognition into a critical, highly timely domain. As architecture transitions from flat screens to immersive spaces, traditional inputs like keyboards and controllers are rapidly giving way to Natural User Interfaces (NUIs). This shift is accelerated by a massive global demand for intuitive human–computer interaction across consumer electronics, smart automotive cockpits, and touchless healthcare systems. However, bridging the gap between human intent and machine execution presents profound technical challenges, such as minimizing processing latency, lowering sensor power consumption for wearable devices, and overcoming environmental interferences like motion artifacts or variable lighting. These hurdles simultaneously open massive commercial opportunities, sparking breakthrough research into diverse modalities, ranging from flexible, skin-like wearable electronics to radar-based micro-motion tracking and edge-AI-driven computer vision.
Consequently, this field serves as a vital bridge between foundational theory and commercial application, holding immense value for both academia and practice. For researchers, gesture sensing is a multidisciplinary crucible where materials science, advanced signal processing, and tiny Machine Learning (tinyML) intersect to redefine how machines perceive human behavior. For industry practitioners, overcoming these sensing bottlenecks is the ultimate key to unlocking next-generation user experiences, improving device accessibility for individuals with physical disabilities, and establishing safer, hands-free automation in industrial environments. By advancing the capabilities of these sensor systems, the scientific and professional communities are not merely optimizing a feature; they are fundamentally rewriting the rules of human–machine symbiosis.
This Topical Collection aims to present and disseminate the most recent advances related to novel sensor systems, advanced sensing materials, intelligent algorithms tailored for gesture recognition, and more. We consider contributions addressing both wearable and contactless sensing modalities (including computer vision, radar, and flexible electronics), edge-AI and tinyML processing architectures, signal processing challenges, and the practical deployment of these technologies in fields such as extended reality (XR), smart automotive cockpits, touchless healthcare interfaces, and so ahead.
Topics of interest for publication include, but are not limited to, the following:
- Novel sensing materials and flexible/wearable electronics for gesture tracking;
- Vision-based gesture recognition utilizing RGB-D, infrared, and event-based cameras;
- Contactless sensing modalities, including Radar, LiDAR, ultrasonic, and RF-based tracking;
- Sensor fusion techniques for multi-modal gesture data acquisition;
- Advanced signal processing, noise reduction, and motion artifact mitigation in real-time tracking;
- Machine learning, deep learning, and TinyML architectures optimized for gesture classification;
- Low-power hardware design and edge-computing paradigms for efficient gesture processing;
- Gesture-based interfaces in Extended Reality (XR), Augmented Reality (AR), and Virtual Reality (VR);
- Human–Machine Interfaces (HMI) for smart automotive cockpits and automated environments;
- Touchless systems for healthcare, sterile environments, and assistive or rehabilitative technologies.
Prof. Dr. Giovanni Saggio
Collection Editor
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Keywords
- gesture recognition
- sensor systems
- wearable electronics
- contactless sensing
- human–machine interface (HMI)
- sensor fusion
- machine learning
- spatial computing
