Kachole Sanket, Nayak Bhagyashri, Brouner James, Liu Ying, Guo Liucheng, Makris Dimitrios
School of Computer Science and Mathematics, Kingston University, London KT1 2EE, UK.
Department of Applied and Human Sciences, Kingston University, London KT1 2EE, UK.
Sensors (Basel). 2025 Aug 9;25(16):4926. doi: 10.3390/s25164926.
Utilizing tactile sensors embedded in intelligent mats is an attractive non-intrusive approach for human motion analysis. Interpreting tactile pressure 2D maps for accurate posture estimation poses significant challenges, such as dealing with data sparsity, noise interference, and the complexity of mapping pressure signals. Our approach introduces a novel dual-diffusion signal enhancement (DDSE) architecture that leverages tactile pressure measurements from an intelligent pressure mat for precise prediction of 3D body joint positions, using a diffusion model to enhance pressure data quality and a convolutional-transformer neural network architecture for accurate pose estimation. Additionally, we collected the pressure-to-posture inference technology (PPIT) dataset that relates pressure signals organized as a 2D array to Motion Capture data, and our proposed method has been rigorously evaluated on it, demonstrating superior accuracy in comparison to state-of-the-art methods.
利用嵌入智能垫中的触觉传感器是一种用于人体运动分析的有吸引力的非侵入性方法。解释触觉压力二维图以进行准确的姿势估计面临重大挑战，例如处理数据稀疏性、噪声干扰以及映射压力信号的复杂性。我们的方法引入了一种新颖的双扩散信号增强（DDSE）架构，该架构利用来自智能压力垫的触觉压力测量来精确预测三维身体关节位置，使用扩散模型来提高压力数据质量，并使用卷积-Transformer神经网络架构进行准确的姿势估计。此外，我们收集了将组织为二维数组的压力信号与运动捕捉数据相关联的压力到姿势推理技术（PPIT）数据集，并且我们提出的方法已在该数据集上进行了严格评估，与现有最先进方法相比显示出更高的准确性。
