Micro-gestures provide a natural, efficient, and privacy-preserving input modality; however, existing techniques often depend on environmental conditions, limiting their robustness and applicability in real-world settings. In this work, we present three portable prototypes- - finger-cot, finger-worn, and surface-based-that integrate compact pressure array sensors to support environment-independent micro-gesture interaction. We further propose a deep learning-based recognition model that accurately classifies 14 micro-gestures by analyzing temporal pressure patterns. Building upon these components, we introduce PortInput, a real-time interactive system that enables robust micro-gesture tracking and detection. We conducted two user studies with augmented reality (AR) head-mounted displays (HMDs). The first study evaluates input performance under both sitting and walking conditions, while the second compares PortInput with a commercial pressure-based ring device. The results show that PortInput improves usability and user experience, achieves comparable accuracy, and enables faster input with lower perceived workload. Overall, PortInput have potential to offer efficient, robust, and comfortable input across diverse application scenarios-ranging from AR/Virtual Reality (VR) headsets to smart homes and in-car systems-even in noisy or cluttered environments. This work provides a foundation for integrating pressure array sensors into ring-based or other portable devices, advancing always-available micro-gesture interaction for ubiquitous computing environments.

The models currently used for Polarimetric Synthetic Aperture Radar (PolSAR) image classification tasks have problems such as complex network structures, poor distinction of detailed features, and fixed loss weights during the training process. In response to these problems, this paper proposes a PolSAR image classification method based on knowledge distillation using adaptive learning and channel amplification features. Firstly, this paper builds a knowledge distillation framework for PolSAR. Using a teacher network trained in advance that can acquire global knowledge to guide the student. This framework reduces the computational complexity and improves the classification accuracy of the student. Then, an adaptive loss weight learning mechanism is designed, which sets the weight of the Kullback-Leibler divergence loss during training into a learnable mode. The weight can be automatically adjusted according to the actual training situation of the student. Finally, a scheme for channel amplification to enhance features is proposed. This scheme obtains channel weights based on the student’s feature map information. These weights are amplified, strengthening the network’s ability to obtain feature information. Compared with the five PolSAR image classification algorithms, the method proposed in this paper uses lower computational complexity to obtain higher classification accuracy on the Flevoland, San Francisco, and Xi’an datasets.

Current virtual reality (VR) system takes gesture interaction based on camera, handle and touch screen as one of the mainstream interaction methods, which can provide accurate gesture input for it. However, limited by application forms and the volume of devices, these methods cannot extend the interaction area to such surfaces as walls and tables. To address the above challenge, we propose AudioGest, a portable, plug-and-play system that detects the audio signal generated by finger tapping and sliding on the surface through a set of microphone devices without extensive calibration. First, an audio synthesis-recognition pipeline based on micro-contact dynamics simulation is constructed to generate modal audio synthesis from different materials and physical properties. Then the accuracy and effectiveness of the synthetic audio are verified by mixing the synthetic audio with real audio proportionally as the training sets. Finally, a series of desktop office applications are developed to demonstrate the application potential of AudioGest's scalability and versatility in VR scenarios.

Hand-eye coordination techniques find widespread utility in augmented reality and virtual reality headsets, as they retain the speed and intuitiveness of eye gaze while leveraging the precision of hand gestures. However, in contrast to obvious interactive gestures, users prefer less noticeable interactions in public settings due to concerns about social acceptance. To address this, we propose GazeRing, a multimodal interaction technique that combines eye gaze with a smart ring, enabling private and subtle hand-eye coordination while allowing users’ hands complete freedom of movement. Specifically, we design a pressure-sensitive ring that supports sliding interactions in eight directions to facilitate efficient 3D object manipulation. Additionally, we introduce two control modes for the ring: finger-tap and finger-slide, to accommodate diverse usage scenarios. Through user studies involving object selection and translation tasks under two eye-tracking accuracy conditions, with two degrees of occlusion, GazeRing demonstrates significant advantages over existing techniques that do not require obvious hand gestures (e.g., gaze-only and gaze-speech interactions). Our GazeRing technique achieves private and subtle interactions, potentially improving the user experience in public settings. A demo video can be found at zhimin-wang.github.io/GazeRing.html.

Gesture interaction is currently a main interaction technology in the field of mixed reality. However, long-term and large-scale gesture in mid-air will lead to muscle fatigue and privacy problems, which cannot meet the comfort requirements of continuous interaction and inevitably hinder the development of mixed reality systems. To solve this problem, we propose NailRing, an intelligent ring to recognize fingertip micro-gestures using a micro-close-focus camera on a fingertip bracket. Such fingertip physiological characteristics as the changes in fingertip color distribution and muscle shape changes caused by fingertip pressure have been studied. According to the recognition principle, ten types of micro-gestures have been designed and used for contact interaction and one-hand interaction respectively. The accuracy of gesture recognition (cross-session $ F_{Macro}=98.3\%$; cross-person $ F_{Macro}=86.4\%$) in user studies verifies the performances of NailRing under different interaction conditions. Finally, the capability of NailRing in a series of potential application scenarios has also been discussed and analyzed.