GACSNet: A Lightweight Network for the Noninvasive Blood Glucose Detection

Figures & data

Figure 1. Group convolution and channel shuffling.

Figure 2. Architecture of channel shuffle.

Figure 3. Architecture of asymmetric convolution.

Figure 4. Process of acquisition.

Table 1. Information of blood glucose dataset

Datasets	<3.9	3.9–5	5–6	6–7	7–8	8–9	9–10	>10	(mmol/L)
Training set	2	20	267	83	9	5	2	16
Validation set	0	11	50	24	7	4	1	3
Test set	0	10	45	30	6	4	2	3

Figure 5. Process of data augmentation.

Table 2. Information of data augmentation

Datasets	<3.9	3.9–5	5–6	6–7	7–8	8–9	9–10	>10	(mmol/L)
Training set	2	20	267	83	9	5	2	16
Augmentation set	0	0	0	55	70	33	21	17
Final Training set	2	20	267	138	79	38	23	33

Figure 6. Architecture of Block A and B.

Figure 7. Architecture of GACSNet.

Table 3. Architecture of GACSNet

Stage	Input	Structure	Output
1	224 $\times$ 224 $\times$ 3	3 $\times$ 3 conv, s = 2 MaxPooling, s = 2	56 $\times$ 56 $\times$ 32
2	56 $\times$ 56 $\times$ 32	$\left[\begin{array}{c}1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 3\times 1\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=\left[1,2\right]\\ 1\times 3\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=\left[2,1\right]\\ 1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ \mathrm{C}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{a}\mathrm{t}\end{array}\right]$$\left[\begin{array}{c}1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 3\times 1\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 1\times 3\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ \mathrm{A}\mathrm{d}\mathrm{d}\end{array}\right]$	28 $\times$ 28 $\times$ 128
3	28 $\times$ 28 $\times$ 128	$\left[\begin{array}{c}1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 3\times 1\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=\left[1,2\right]\\ 1\times 3\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=\left[2,1\right]\\ 1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ \mathrm{C}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{a}\mathrm{t}\end{array}\right]$$\left[\begin{array}{c}1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 3\times 1\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 1\times 3\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ \mathrm{A}\mathrm{d}\mathrm{d}\end{array}\right]\times 3$	14 $\times$ 14 $\times$ 256
4	14 $\times$ 14 $\times$ 256	$\left[\begin{array}{c}1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 3\times 1\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=\left[1,2\right]\\ 1\times 3\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=\left[2,1\right]\\ 1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ \mathrm{C}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{a}\mathrm{t}\end{array}\right]$$\left[\begin{array}{c}1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 3\times 1\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 1\times 3\mathrm{D}\mathrm{W}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ 1\times 1\mathrm{G}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v},\mathrm{s}=1\\ \mathrm{A}\mathrm{d}\mathrm{d}\end{array}\right]$	7 $\times$ 7 $\times$ 512
5	7 $\times$ 7 $\times$ 512	GlobalAVGPooling, FC, Regression	1 $\times$ 1 $\times$ 1

Figure 8. MAPE of GACSNet.

Figure 9. CEGA of GACSNet.

Table 4. Performance comparison of the effect in negative region

Models	MAPE	CEGA Zone A	CEGA Zone B	Training time	Inference time
	(%)	(%)	(%)	(s)	(ms)
1	09.17	93	7	0173.69	1.35
2	09.19	94	6	1401.19	1.55
3	10.18	94	6	0353.81	1.63
4	08.64	94	6	3599.75	1.89

Table 5. Performance comparison of the GACSNet with others

Networks	MAPE	CEGA Zone A	CEGA Zone B	Training time	Inference time
	(%)	(%)	(%)	(s)	(ms)
GACSNet	09.17	93	7	173.69	1.35
ResNet-50	08.77	93	7	508.58	3.87
InceptionV3	10.62	90	10	979.96	6.87
MobileNetV2	09.38	92	8	421.13	3.31

Table 6. Performance of inference on embedded device

Networks	Model size	Inference time
	(MB)	(ms)
GACSNet	01.21	070.34
ShuffleNet	03.22	074.83
ResNet-50	76.86	097.45
InceptionV3	78.02	254.20
MobileNetV2	07.94	109.73

Figure 10. Performance analysis of the effect in negative region.

Figure 11. Performance analysis of the GACSNet with others.

Table 7. MAPE of different blood glucose regions

Networks	3.9–5	5–6	6–7	7–8	8–9	9–	>10	(mmol/L)
GACSNet	09.45	7.00	8.42	11.31	09.00	24.55	34.51	(%)
ShuffleNet	11.46	8.39	6.49	13.20	22.79	30.55	38.86	(%)
ResNet-50	05.13	6.98	7.39	08.69	12.38	24.09	42.44	(%)
InceptionV3	12.65	9.37	8.53	09.30	15.28	23.34	25.63	(%)
MobileNetV2	10.68	7.15	8.09	09.82	13.60	29.28	38.55	(%)