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Liang T, Liu R, Yang L, Lin Y, Shi CJR, Xu H. Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar. Sensors (Basel) 2024; 24:648. [PMID: 38276339 PMCID: PMC10820484 DOI: 10.3390/s24020648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/05/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
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.
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Affiliation(s)
- Tingxuan Liang
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China; (T.L.)
| | - Ruizhi Liu
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China; (T.L.)
| | - Lei Yang
- ICLegend Micro, Shanghai 201203, China
| | - Yue Lin
- ICLegend Micro, Shanghai 201203, China
| | - C.-J. Richard Shi
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA;
| | - Hongtao Xu
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China; (T.L.)
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Zhao Z, Wang Y, Zou Q, Xu T, Tao F, Zhang J, Wang X, Shi CJR, Luo J, Xie Y. The spike gating flow: A hierarchical structure-based spiking neural network for online gesture recognition. Front Neurosci 2022; 16:923587. [PMID: 36408382 PMCID: PMC9667043 DOI: 10.3389/fnins.2022.923587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 10/03/2022] [Indexed: 01/25/2023] Open
Abstract
Action recognition is an exciting research avenue for artificial intelligence since it may be a game changer in emerging industrial fields such as robotic visions and automobiles. However, current deep learning (DL) faces major challenges for such applications because of the huge computational cost and inefficient learning. Hence, we developed a novel brain-inspired spiking neural network (SNN) based system titled spiking gating flow (SGF) for online action learning. The developed system consists of multiple SGF units which are assembled in a hierarchical manner. A single SGF unit contains three layers: a feature extraction layer, an event-driven layer, and a histogram-based training layer. To demonstrate the capability of the developed system, we employed a standard dynamic vision sensor (DVS) gesture classification as a benchmark. The results indicated that we can achieve 87.5% of accuracy which is comparable with DL, but at a smaller training/inference data number ratio of 1.5:1. Only a single training epoch is required during the learning process. Meanwhile, to the best of our knowledge, this is the highest accuracy among the non-backpropagation based SNNs. Finally, we conclude the few-shot learning (FSL) paradigm of the developed network: 1) a hierarchical structure-based network design involves prior human knowledge; 2) SNNs for content-based global dynamic feature detection.
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Affiliation(s)
- Zihao Zhao
- School of Microelectronics, Fudan University, Shanghai, China,Alibaba DAMO Academy, Shanghai, China
| | - Yanhong Wang
- School of Microelectronics, Fudan University, Shanghai, China,Alibaba DAMO Academy, Shanghai, China
| | - Qiaosha Zou
- School of Microelectronics, Fudan University, Shanghai, China
| | - Tie Xu
- Alibaba Group, Hangzhou, China
| | | | | | - Xiaoan Wang
- BrainUp Research Laboratory, Shanghai, China
| | - C.-J. Richard Shi
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Junwen Luo
- Alibaba DAMO Academy, Shanghai, China,BrainUp Research Laboratory, Shanghai, China,*Correspondence: Junwen Luo
| | - Yuan Xie
- Alibaba DAMO Academy, Shanghai, China
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