Multi-person global human mesh recovery (HMR) is crucial for understanding crowd dynamics and interactions. Traditional vision-based HMR methods sometimes face limitations in real-world scenarios due to mutual occlusions, insufficient lighting, and privacy concerns. Human-floor tactile interactions offer an occlusion-free and privacy-friendly alternative for capturing human motion. Existing research indicates that pressure signals acquired from tactile mats can effectively estimate human pose in single-person scenarios. However, when multiple individuals walk randomly on the mat simultaneously, how to distinguish intermingled pressure signals generated by different persons and subsequently acquire individual temporal pressure data remains a pending challenge for extending pressure-based HMR to the multi-person situation. In this paper, we present PressTrack-HMR, a top-down pipeline that recovers multi-person global human meshes solely from pressure signals. This pipeline leverages a tracking-by-detection strategy to first identify and segment each individual's pressure signal from the raw pressure data, and subsequently performs HMR for each extracted individual signal. Furthermore, we build a multi-person interaction pressure dataset MIP, which facilitates further research into pressure-based human motion analysis in multi-person scenarios. Experimental results demonstrate that our method excels in multi-person HMR using pressure data, with 89.2 MPJPE and 112.6 WA-MPJPE, and these showcase the potential of tactile mats for ubiquitous, privacy-preserving multi-person action recognition.

Long-term in-bed monitoring benefits automatic and real-time health management within healthcare, and the advancement of human shape reconstruction technologies further enhances the representation and visualization of users’ activity patterns. However, existing technologies are primarily based on visual cues, facing serious challenges in non-light-of-sight and privacy-sensitive in-bed scenes. Pressure-sensing bedsheets offer a promising solution for real-time motion reconstruction. Yet, limited exploration in model designs and data have hindered its further development. To tackle these issues, we propose a general framework that bridges gaps in data annotation and model design. Firstly, we introduce SMPLify-IB, an optimization method that overcomes the depth ambiguity issue in top-view scenarios through gravity constraints, enabling generating high-quality 3D human shape annotations for in-bed datasets. Then we present PI-HMR, a temporal-based human shape estimator to regress meshes from pressure sequences. By integrating multi-scale feature fusion with high-pressure distribution and spatial position priors, PIHMR outperforms SOTA methods with 17.01mm Mean-Per-Joint-Error decrease. This work provides a whole tool-chain to support the development of in-bed monitoring with pressure contact sensing.

Reconstructing accurate 3D human meshes in the world coordinate system from in-the-wild images remains challenging due to the lack of camera rotation information. While existing methods achieve promising results in the camera coordinate system by assuming zero camera rotation, this simplification leads to significant errors when transforming the reconstructed mesh to the world coordinate system. To address this challenge, we propose Mesh-Plug, a plug-and-play module that accurately transforms human meshes from camera coordinates to world coordinates. Our key innovation lies in a human-centered approach that leverages both RGB images and depth maps rendered from the initial mesh to estimate camera rotation parameters, eliminating the dependency on environmental cues. Specifically, we first train a camera rotation prediction module that focuses on the human body’s spatial configuration to estimate camera pitch angle. Then, by integrating the predicted camera parameters with the initial mesh, we design a mesh adjustment module that simultaneously refines the root joint orientation and body pose. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods on the benchmark datasets SPEC-SYN and SPEC-MTP.

Reconstructing human dynamics in in-bed scenarios is important in dangerous behaviors detection, pressure sore prevention and sleep quality monitoring. Compared to RGB cameras, which are susceptible to obstruction and privacy concerns, pressure-sensitive bedsheets are gaining increasing attention for such tasks due to their non-invasive and privacy-preserving advantages. However, recovering a 3D human mesh from a single pressure image is challenging due to pressure information ambiguity, resulting in a high degree of uncertainty. Diffusion models, leveraging their powerful fitting capabilities and editing abilities, are promising candidates for addressing this challenge. Therefore, we propose a Pressure Image-supported Diffusion framework for Human Mesh Reconstruction (PIDHMR), which includes a direct regression of the human body model parameters and a follow-up optimization using information automatically detected from pressure images. For regression, we improve the existing diffusion model structure and training objectives to meet the task requirements for generating 3D human shapes from pressure images. For optimization, we examine human-related information that can be used to improve the predicted human mesh from pressure images (i.e., contact area, joints positions, and temporal consistency), and design optimization losses to utilize these information to improve the spatial position, posture, and motion smoothness separately. Ultimately, PIDHMR is evaluated on the public temporal pressure dataset, TIP, and achieves 72.83mm joint position errors, outperforming the state-of-the-art method PIMesh, which achieves 79.17mm.

Since the invention of the electrocardiogram (ECG), significant advancements have been made in its acquisition methods. However, achieving both comfort during measurement process and standardization simultaneously remains a challenge. In this study, we present a novel signal routing method, by dividing the pathway into the on-body part and environmental part and having the connection of these two parts enabled by physical contact, we are able to move the rigid hardware away from the human body. Based on this signal routing method, we design and implement an ECG signal acquisition system capable of measuring standard leads ECG. The Pearson correlation coefficient between the ECG signals acquired by our system with dry electrodes and the standard system with silver/silver-chloride (Ag/AgCl) electrodes reaches up to 0.98 in limb leads and up to 0.94 in chest leads, which demonstrates that our system has the potential to acquire the standard leads ECG signal.

Pressure-sensitive smart textiles are widely applied in the fields of healthcare, sports monitoring, and intelligent homes. The integration of devices embedded with pressure sensing arrays is expected to enable comprehensive scene coverage and multi-device integration. However, the implementation of identity recognition, a fundamental function in this context, relies on extensive device-specific datasets due to variations in pressure distribution across different devices. To address this challenge, we propose a novel user identification method based on contrastive learning. We design two parallel branches to facilitate user identification on both new and existing devices respectively, employing supervised contrastive learning in the feature space to promote domain unification. When encountering new devices, extensive data collection efforts are not required; instead, user identification can be achieved using limited data consisting of only a few simple postures. Through experimentation with two 8-subject pressure datasets (BedPressure and ChrPressure), our proposed method demonstrates the capability to achieve user identification across 12 sitting scenarios using only a dataset containing 2 postures. Our average recognition accuracy reaches 79.05%, representing an improvement of 2.62% over the best baseline model.

SUMMARY: Tree Gibbs Sampler is a software for identifying motifs by simultaneously using the motif overrepresentation property and the motif evolutionary conservation property. It identifies motifs without depending on pre-aligned orthologous sequences, which makes it useful for the extraction of regulatory elements in multiple genomes of both closely related and distant species. AVAILABILITY: The Tree Gibbs Sampler software is freely downloadable at https://compbio.iupui.edu/xiaomanli/LiSoftware/retrieve.php?ID=tgs