Human Activity and IdentityMulti-Task Recognition Based on Convolutional Neural Network Using Doppler Radar

Hou Chunping; Jiang Tianli; Lang Yue; Yang Yang

doi:10.3788/LOP57.021009

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 2 >Page 21009 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 2, 21009 (2020)

Human Activity and IdentityMulti-Task Recognition Based on Convolutional Neural Network Using Doppler Radar

Hou Chunping, Jiang Tianli, Lang Yue^*, and Yang Yang

Author Affiliations

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

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DOI: 10.3788/LOP57.021009 Cite this Article Set citation alerts

Hou Chunping, Jiang Tianli, Lang Yue, Yang Yang. Human Activity and IdentityMulti-Task Recognition Based on Convolutional Neural Network Using Doppler Radar[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21009 Copy Citation Text

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Fig. 1. Flow chart of building radar time-frequency image data set

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Radar time-frequency spectrograms of radar for six human activities. (a) Walking; (b) boxing; (c) crawling on the ground; (d) diving; (e) standing forward and jumping; (f) running

Fig. 2. Radar time-frequency spectrograms of radar for six human activities. (a) Walking; (b) boxing; (c) crawling on the ground; (d) diving; (e) standing forward and jumping; (f) running

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Fig. 3. Structural diagram of SE module

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Fig. 4. Multi-task recognition model structure based on CNN

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Fig. 5. Diagram of joint loss function generation

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Fig. 6. Loss and accuracy curves. (a) Loss curves; (b) accuracy curves

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Crossvalidationset No.	Recognitionrate of MR /%			Recognitionrate of SR /%
Crossvalidationset No.	HA	HD	HT	HA	HD	HT
1	99.94	98.78	98.74	99.70	95.99	95.72
2	99.94	98.80	98.77	99.88	96.70	96.93
3	99.97	98.90	98.89	99.90	97.91	97.84
4	100.00	99.93	99.93	99.96	99.37	99.34
Mean	99.96	99.10	99.08	99.86	97.49	97.38

Table 1. Results of multi-task and single-task recognitions

Model	Recognition rate /%
Model	HA	HD	HT
D-MSSE	99.98	99.62	99.61
D-MS	100	99.88	99.88
D-SE	99.99	90.47	90.47

Table 2. Recognition results of different network structures

Loss	Recognition rate /%
Loss	HA	HD	HT
L_E	99.99	99.74	99.73
L_E +L_M	99.99	99.86	99.86
L_E+L_C	100	99.87	99.86

Table 3. Recognition results of different loss optimization

L /s	Recognition rate /%
L /s	HA	HD	HT
0.2	97.99	78.59	77.68
0.4	99.75	89.25	89.13
0.6	99.91	92.47	92.44
0.8	99.98	99.73	99.71

Table 4. Recognition results under different STFT window lengths

N_SR /dB	Recognition rate /%
N_SR /dB	HA	HD	HT
-20	67.53	24.4	19.06
-10	96.91	77.63	76.49
0	99.91	90.8	90.75
10	99.99	99.5	99.49
+∞	100	99.93	99.93

Table 5. Recognition results under different N_SR

Data size	Recognition rate /%
Data size	HA	HD	HT
400	99.94	97.52	97.46
600	99.97	99.71	99.57
800	99.97	99.62	99.59
1000	99.99	99.76	99.75
1300	99.99	99.85	99.84
1500	100	99.93	99.93

Table 6. Recognition results under different training data sizes

Algorithm	Recognition rate /%
Algorithm	HA	HD	HT
PRGC	99.94	98.9	98.84
MRN	99.97	99.9	99.85.
MCNN	100	99.93	99.93

Table 7. Comparison of results of algorithms

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