Tingxuan Liang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Tingxuan Liang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Hongtao Xu
Fudan University
1 shared publication - Lei Yang
ICLegend Micro, Shanghai 201203, China
1 shared publication - Richard Chuan-Jin Shi
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
1 shared publication - Ruizhi Liu
State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China
1 shared publication - Yue Lin
ICLegend Micro, Shanghai, China
1 shared publication
Latest Publications
- Open Access
Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar
Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.
Open AccessFall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar
Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.