This study focuses on Hand Gesture Recognition (HGR), which is vital for
perceptual computing across various real-world contexts. The primary challenge
in the HGR domain lies in dealing with the individual variations inherent in
human hand morphology. To tackle this challenge, we introduce an innovative HGR
framework that combines data-level fusion and an Ensemble Tuner Multi-stream
CNN architecture. This approach effectively encodes spatiotemporal gesture
information from the skeleton modality into RGB images, thereby minimizing
noise while improving semantic gesture comprehension. Our framework operates in
real-time, significantly reducing hardware requirements and computational
complexity while maintaining competitive performance on benchmark datasets such
as SHREC2017, DHG1428, FPHA, LMDHG and CNR. This improvement in HGR
demonstrates robustness and paves the way for practical, real-time applications
that leverage resource-limited devices for human-machine interaction and
ambient intelligence.

本研究聚焦于手势识别，通过数据级融合和多流卷积神经网络架构的集成调节器，有效地解决了人手形态中的个体差异问题，将骨骼模式的时空手势信息编码到 RGB 图像中，从而提高了姿势理解的语义并降低了噪声，其在 SHREC2017、DHG1428、FPHA、LMDHG 和 CNR 等基准数据集上实现了实时运行，大幅减少硬件需求和计算复杂性，为资源受限设备的人机交互和环境智能提供了可靠性和创新性。