Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

被引：7

作者：

Hayashi, Sora ^{[1
]}

Saho, Kenshi ^{[1
,2
]}

Shioiri, Keitaro ^{[2
]}

Fujimoto, Masahiro ^{[3
]}

Masugi, Masao ^{[1
]}

机构：

[1] Ritsumeikan Univ, Grad Sch Sci & Engn, Shiga 5258577, Japan

[2] Toyama Prefectural Univ, Grad Sch Engn, Toyama 9390398, Japan

[3] Natl Inst Adv Ind Sci & Technol, Human Augmentat Res Ctr, Chiba 2770882, Japan

来源：

SENSORS | 2021年 / 21卷 / 11期

关键词：

Doppler radar; gait classification; machine learning; LSTM; HUMAN ACTIVITY CLASSIFICATION; IDENTIFICATION; PARAMETERS; SIGNATURES; WALKING; FALLERS;

D O I：

10.3390/s21113643

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

To develop a daily monitoring system for early detection of fall risk of elderly people during walking, this study presents a highly accurate micro-Doppler radar (MDR)-based gait classification method for the young and elderly adults. Our method utilizes a time-series of velocity corresponding to leg motion during walking extracted from the MDR spectrogram (time-velocity distribution) in an experimental study involving 300 participants. The extracted time-series was inputted to a long short-term memory recurrent neural network to classify the gaits of young and elderly participant groups. We achieved a classification accuracy of 94.9%, which is significantly higher than that of a previously presented velocity-parameter-based classification method.

引用

页数：11

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