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 has the 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.
Jiang et al. (Thu,) studied this question.