Fall detection with a single Doppler radar sensor and LSTM recurrent neural network

被引：0

作者：

Imamura, Takayuki ^{[1
]}

Moshnyaga, Vasily G. ^{[2
,3
]}

Hashimoto, Koji ^{[2
,3
]}

机构：

[1] Fukuoka Univ, Grad Sch Engn, Fukuoka 8140180, Japan

[2] Fukuoka Univ, Dept Elect Engn, Fukuoka 8140180, Japan

[3] Fukuoka Univ, CS, Fukuoka 8140180, Japan

来源：

2022 IEEE 65TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS 2022) | 2022年

关键词：

Doppler radar; sensor; machine learning; Long Short-Term Memory; RNN; fall detection; SIGNATURES;

D O I：

10.1109/MWSCAS54063.2022.9859430

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Falls are a serious health concern and a main cause of injuries among elders living independently at home. In this paper, we describe a new system for real-time automatic fall detection. In contrast to related formulations, the system employs a single continuous-wave micro-Doppler radar sensor to monitor a subject and Long Short-Term Memory based recurrent neural network (RNN) to identify falls from the time-frequency characteristics of the sensor's returns. It does not require extra hardware or big data set to classify abrupt and slow falls from non-fall actions with superior detection accuracy.

引用

页数：4

共 50 条

[1] Fall Detection on a single Doppler Radar Sensor by using Convolutional Neural Networks
Yoshino, Haruka
Moshnyaga, Vasily G.
Hashimoto, Koji
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), 2019, : 2889 - 2892
[2] Doppler Radar Sensor Positioning in a Fall Detection System
Liu, Liang
Popescu, Mihail
Ho, K. C.
Skubic, Marjorie
Rantz, Marilyn
[J]. 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2012, : 256 - 259
[3] Range-Doppler Radar Sensor Fusion for Fall Detection
Erol, Baris
Amin, Moeness G.
Boashash, Boualem
[J]. 2017 IEEE RADAR CONFERENCE (RADARCONF), 2017, : 819 - 824
[4] Cooperative Fall Detection Using Doppler Radar and Array Sensor
Hong, Jihoon
Tomii, Shoichiro
Ohtsuki, Tomoaki
[J]. 2013 IEEE 24TH INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2013, : 3492 - 3496
[5] An Automatic Fall Detection Framework Using Data Fusion of Doppler Radar and Motion Sensor Network
Liu, Liang
Popescu, Mihail
Skubic, Marjorie
Rantz, Marilyn
[J]. 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2014, : 5940 - 5943
[6] A Recurrent Neural Network Approach to Pulse Radar Detection
Sailaja, Anangi
Sahoo, Ajit Kumar
Panda, Ganapati
Baghel, Vikas
[J]. 2009 ANNUAL IEEE INDIA CONFERENCE (INDICON 2009), 2009, : 57 - +
[7] FMCW radar2radar Interference Detection with a Recurrent Neural Network
Hille, Julian
Auge, Daniel
Grassmann, Cyprian
Knoll, Alois
[J]. 2022 IEEE RADAR CONFERENCE (RADARCONF'22), 2022,
[8] Contactless Fall Detection using Doppler Radar
Hanifi, Khadija
Karsligil, M. Elif
[J]. 2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
[9] Deep Convolutional Bidirectional LSTM Recurrent Neural Network for Epileptic Seizure Detection
Abdelhameed, Ahmed M.
Daoud, Hisham G.
Bayoumi, Magdy
[J]. 2018 16TH IEEE INTERNATIONAL NEW CIRCUITS AND SYSTEMS CONFERENCE (NEWCAS), 2018, : 139 - 143
[10] LSTM Recurrent Neural Network (RNN) for Anomaly Detection in Cellular Mobile Networks
Al Mamun, S. M. Abdullah
Beyaz, Mehmet
[J]. MACHINE LEARNING FOR NETWORKING, 2019, 11407 : 222 - 237

← 1 2 3 4 5 →