Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Setup
2.2. Description of the Experimental Procedure
2.3. Signal Processing Technique
2.3.1. Preliminary Data Processing
2.3.2. Learning and Inference
3. Results
4. Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Bioradar No. 1 | Bioradar No. 2 |
---|---|---|
Probing frequency | 24.107 GHz | 24.065 GHz |
VCO input | 0 V | 1.8 V |
Detecting signal band | 1–100 Hz | |
Gain | 15–30 dB | |
Radiated power density | <3 µW/cm2 | |
Beam aperture | 80°/34° | |
Size | 95 × 75 × 45 mm |
Male:Female | 2:3 |
---|---|
Age (Years) | 22–41 |
Height (cm) | 164–185 |
Body Mass Index (kg/m2) | 17.4–22.1 |
Movement Type | Number | ||
---|---|---|---|
Entering–exiting the premises | 175 | 25 | |
Whole body turning | 25 | ||
Arm movements | 25 | ||
Not fall activities | Sitting on the chair and standing from it | 25 | |
leaning | 25 | ||
squats | 25 | ||
lying down on the mat | 25 | ||
Falls | 175 | ||
All types of movements | 350 |
CNN Name | Test Dataset | Accuracy, % | Sensitivity, % | Specificity, % | Precision, % | F1-score, % |
---|---|---|---|---|---|---|
CNN1 | Bioradar 1 | 98.57 | 97.14 | 100 | 100 | 98.55 |
CNN2 | Bioradar 2 | 87.86 | 85.71 | 90.00 | 89.55 | 87.59 |
CNN2 | Bioradar 1 | 95.71 | 92.86 | 98.57 | 98.49 | 95.59 |
CNN1 | Bioradar 2 | 77.14 | 58.57 | 95.71 | 93.18 | 71.93 |
CNN12 | Bioradars 1&2 | 99.29 | 98.57 | 100 | 100 | 99.28 |
Ref. | Type of Sensors | Classifier | Amount of Channels | Number of Examinees | Accuracy, % |
---|---|---|---|---|---|
Martínez-Villaseñor (2019), [18] | Wearable, infrared sensors, cameras | RF, SVM, MLP, kNN | 14 | 17 | 95.0 |
Martínez-Villaseñor (2019), [18] | cameras | CNN | 2 | 17 | 95.1 |
Kwolek (2015), [36] | Kinect and Accelerometer | KNN and SVM | 2 | 5 | 95.8 |
Erol (2018), [37] | Radar | STFT, GPCA and KNN | 1 | 14 | 97.0 |
Jokanović (2017), [38] | Radar | Spectrogram and neural network | 1 | 3 | 97.1 |
Anishchenko (2018) [39] | Camera | CNN | 1 | 4 | 98.9 |
This work | Radars | CWT and CNN | 2 | 5 | 99.3 |
Kwolek (2014), [40] | Kinect | KNN | 1 | 30 | 100 |
Mastorakis (2014), [41] | Kinect | Threshold and Shape Features | 1 | 2 | 100 |
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Anishchenko, L.; Zhuravlev, A.; Chizh, M. Fall Detection Using Multiple Bioradars and Convolutional Neural Networks. Sensors 2019, 19, 5569. https://doi.org/10.3390/s19245569
Anishchenko L, Zhuravlev A, Chizh M. Fall Detection Using Multiple Bioradars and Convolutional Neural Networks. Sensors. 2019; 19(24):5569. https://doi.org/10.3390/s19245569
Chicago/Turabian StyleAnishchenko, Lesya, Andrey Zhuravlev, and Margarita Chizh. 2019. "Fall Detection Using Multiple Bioradars and Convolutional Neural Networks" Sensors 19, no. 24: 5569. https://doi.org/10.3390/s19245569
APA StyleAnishchenko, L., Zhuravlev, A., & Chizh, M. (2019). Fall Detection Using Multiple Bioradars and Convolutional Neural Networks. Sensors, 19(24), 5569. https://doi.org/10.3390/s19245569