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Sensors
Volume 25
Issue 16
10.3390/s25164926
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Posture Estimation from Tactile Signals Using a Masked Forward Diffusion Model

by
Sanket Kachole
1,*,
Bhagyashri Nayak
1,
James Brouner
2,
Ying Liu
3,
Liucheng Guo
3 and
Dimitrios Makris
1
1
School of Computer Science and Mathematics, Kingston University, London KT1 2EE, UK
2
Department of Applied and Human Sciences, Kingston University, London KT1 2EE, UK
3
Tangi0 Ltd. (TG0), 73-75 Upper Richmond Rd, London WC1N 3BP, UK
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(16), 4926; https://doi.org/10.3390/s25164926 (registering DOI)
Submission received: 18 June 2025 / Revised: 20 July 2025 / Accepted: 25 July 2025 / Published: 9 August 2025
(This article belongs to the Section Physical Sensors)
Review Reports Versions Notes

Abstract

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.
Keywords: tactile pressure maps; posture estimation; convolutional-transformer neural network; diffusion models tactile pressure maps; posture estimation; convolutional-transformer neural network; diffusion models

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MDPI and ACS Style

Kachole, S.; Nayak, B.; Brouner, J.; Liu, Y.; Guo, L.; Makris, D. Posture Estimation from Tactile Signals Using a Masked Forward Diffusion Model. Sensors 2025, 25, 4926. https://doi.org/10.3390/s25164926

AMA Style

Kachole S, Nayak B, Brouner J, Liu Y, Guo L, Makris D. Posture Estimation from Tactile Signals Using a Masked Forward Diffusion Model. Sensors. 2025; 25(16):4926. https://doi.org/10.3390/s25164926

Chicago/Turabian Style

Kachole, Sanket, Bhagyashri Nayak, James Brouner, Ying Liu, Liucheng Guo, and Dimitrios Makris. 2025. "Posture Estimation from Tactile Signals Using a Masked Forward Diffusion Model" Sensors 25, no. 16: 4926. https://doi.org/10.3390/s25164926

APA Style

Kachole, S., Nayak, B., Brouner, J., Liu, Y., Guo, L., & Makris, D. (2025). Posture Estimation from Tactile Signals Using a Masked Forward Diffusion Model. Sensors, 25(16), 4926. https://doi.org/10.3390/s25164926

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