ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training

2025-08-03

AI Summary

Existing seated pose estimation methods suffer from occlusion, privacy leakage, user discomfort, and strong dependence on specific chair designs. To address these limitations, this paper proposes a wearable-free, chair-agnostic, pressure-driven framework for full-body seated pose estimation. Our method explicitly incorporates chair geometry into the inference process—a first in this domain—and introduces a physics-based pressure data augmentation pipeline to mitigate the scarcity of real-world annotated data. We further design a two-stage generative model that directly regresses 3D human joint poses from generic pressure-sensing mat inputs. Evaluated across eight users and four distinct chair types in cross-combination settings, our approach achieves a mean joint position error of only 89.4 mm under unseen-user–unseen-chair conditions, demonstrating substantial improvements in generalizability, privacy preservation, and deployment flexibility.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: State Estimation
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Prolonged seated activity is increasingly common in modern environments, raising concerns around musculoskeletal health, ergonomics, and the design of responsive interactive systems. Existing posture sensing methods such as vision-based or wearable approaches face limitations including occlusion, privacy concerns, user discomfort, and restricted deployment flexibility. We introduce ChairPose, the first full body, wearable free seated pose estimation system that relies solely on pressure sensing and operates independently of chair geometry. ChairPose employs a two stage generative model trained on pressure maps captured from a thin, chair agnostic sensing mattress. Unlike prior approaches, our method explicitly incorporates chair morphology into the inference process, enabling accurate, occlusion free, and privacy preserving pose estimation. To support generalization across diverse users and chairs, we introduce a physics driven data augmentation pipeline that simulates realistic variations in posture and seating conditions. Evaluated across eight users and four distinct chairs, ChairPose achieves a mean per joint position error of 89.4 mm when both the user and the chair are unseen, demonstrating robust generalization to novel real world generalizability. ChairPose expands the design space for posture aware interactive systems, with potential applications in ergonomics, healthcare, and adaptive user interfaces.

Problem

• Estimates seated pose without wearables using pressure sensing
• Overcomes occlusion and privacy issues in posture sensing
• Generalizes across diverse users and chair geometries

Innovation

• Pressure-based sensing for occlusion-free pose estimation
• Chair morphology integrated into inference process
• Physics-driven data augmentation for robust generalization