Name
Abstract | ||
|---|---|---|
Falls are one of the greatest threats to elderly health as they carry out their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be rendered. Radar is an effective non-intrusive sensing modality which is well suited for this purpose. It can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. In this paper, we use micro-Doppler features in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition for feature extraction and fall detection. The extracted features include the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, we use the extracted signal features for training and testing hidden Markov models and support vector machines indifferent falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICHI.2013.27 | ICHI |
Keywords | Field | DocType |
signal representation,narrowband pulse-doppler radar,accurate fall detection,medical signal detection,radar signal time-frequency features,doppler radar,geriatrics,radar signal,elderly health,human body motion,medical signal processing,fall detection,radar signal processing,signal feature,feature extraction,time-frequency signal representation,radar detection,support vector machine,time-varying doppler signatures,gait analysis,time-frequency representation,sequential modeling,microdoppler features,human body motions,accurate manner,principal component analysis,hidden markov models,support vector machines,human motion,hidden markov model,matching pursuit decomposition,time-frequency analysis,time frequency analysis | Radar,Doppler radar,Computer vision,Narrowband,Pattern recognition,Computer science,Support vector machine,Feature extraction,Time–frequency analysis,Artificial intelligence,Hidden Markov model,Principal component analysis | Conference |
Citations | PageRank | References |
8 | 0.66 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Wu | 1 | 8 | 0.66 |
| Xiaoxiao Dai | 2 | 15 | 2.91 |
| Yimin Zhang | 3 | 1536 | 130.17 |
| Bradley Davidson | 4 | 9 | 1.74 |
| Moeness Amin | 5 | 2909 | 287.79 |
| Jun Zhang | 6 | 408 | 54.35 |