Demonstrating hitonavi- μ
Hamada Rizk, Yuma Okochi, Hirozumi Yamaguchi · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · 2022
In this paper, we demonstrate a brand new design of a wearable device that enables privacy-preserving human activity recognition based on light-weight compact-size LiDAR. The device scans and represents the surrounding environment in 3D point clouds form. The system further processes this representation to define discriminative features that facilitate recognizing human activity on edge. These features are extracted using Spatio-temporal probabilistic clustering and fisher vector representations and then used to train a classification model for activity recognition purposes. Implementation and evaluation of the proposed system confirm its efficient ability to identify human activities.