Hands-Free Human Activity Recognition Using Millimeter-Wave Sensors

被引：0

作者：

Kwon, Soo Min ^{[1
]}

Yang, Song ^{[1
]}

Liu, Jian ^{[1
]}

Yang, Xin ^{[1
]}

Saleh, Wesam ^{[1
]}

Patel, Shreya ^{[1
]}

Mathews, Christine ^{[1
]}

Chen, Yingying ^{[1
]}

机构：

[1] Rutgers State Univ, New Brunswick, NJ 08901 USA

来源：

2019 IEEE INTERNATIONAL SYMPOSIUM ON DYNAMIC SPECTRUM ACCESS NETWORKS (DYSPAN) | 2019年

基金：

美国国家科学基金会;

关键词：

hands-free; millimeter-wave; human activity recognition; pose estimation; machine learning;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this demo, we introduce a hands-free human activity recognition framework leveraging millimeter-wave (mmWave) sensors. Compared to other existing approaches, our network protects user privacy and can remodel a human skeleton performing the activity. Moreover, we show that our network can be achieved in one architecture, and be further optimized to have higher accuracy than those that can only get singular results (i.e. only get pose estimation or activity recognition). To demonstrate the practicality and robustness of our model, we will demonstrate our model in different settings (i.e. facing different backgrounds) and effectively show the accuracy of our network.

引用

页码：145 / 146

页数：2