Daily human activities, e.g., locomotion, exercises, and resting, are heavily guided by the tactile
interactions between human and the ground. In this work, leveraging such tactile interactions, we propose
a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input. We build
a low-cost, high-density, large-scale intelligent carpet, which enables the real-time recordings of
human-floor tactile interactions in a seamless manner. We collect a synchronized tactile and visual dataset
on various human activities. Employing state-of-the-art camera-based pose estimation model as supervision,
we design and implement a deep neural network model to infer 3D human poses using only the tactile information.
Our pipeline can be further scaled up to multi-person pose estimation. We evaluate our system and demonstrate
its potential applications in diverse fields.
IntelligentCarpet: Inferring 3D Human Pose from Tactile Signals
Y.Luo, Y.Li, M. Foshey, W. Shou, P. Sharma, T. Palacios, A. Torralba, W. Matusik CVPR 2021[Paper][Dataset][Code]