Design and Implementation Strategy of Informative Training System for Tennis Physical Education

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Control group tennis six technical assessment test results T test (M±SD)

Test item	Pre-test	Post-test	T	P
A	43.50±13.85	44.87±11.34	-2.734	0.031*
B	47.06±10.52	48.57±10.07	-2.856	0.042*
C	41.33±9.06	42.84±10.44	-3.117	0.035*
D	42.25±9.73	43.38±9.86	-2.852	0.153
E	45.31±10.67	46.33±10.89	-3.007	0.027*
F	43.06±10.58	43.92±9.81	-2.537	0.055

Test results of AA-GCN model using fine-grained data sets

Category	True sample	Prediction sample	Correct classification	Accuracy rate	Precision rate	Recall rate	F1-Score
A	30	32	28	0.8889	0.9333	0.8750	0.9032
B	30	32	28		0.9333	0.8750	0.9032
C	30	32	28		0.9333	0.8750	0.9032
D	30	27	25		0.8333	0.9259	0.8772
E	30	28	24		0.8000	0.8571	0.8276
F	30	29	27		0.9000	0.9310	0.9152
Total	180	180	160		-	-	-

Test results of ST-GCN model without fine-grained data sets

Category	True sample	Prediction sample	Correct classification	Accuracy rate	Precision rate	Recall rate	F1-Score
A	30	20	20	0.6833	0.6667	1.0000	0.8000
B	30	36	21		0.7000	0.5833	0.6363
C	30	34	21		0.7000	0.6176	0.6562
D	30	28	24		0.8000	0.8571	0.8276
E	30	35	18		0.6000	0.5143	0.5539
F	30	27	19		0.6333	0.7037	0.6666
Total	180	180	123		-	-	-

Experimental group badminton technical movement evaluation test results T-test (M±SD)

Test item	Pre-test	Post-test	T	P
A	43.89±12.12	50.18±13.06	-4.835	0.000***
B	46.55±11.03	51.33±11.84	-5.966	0.000***
C	41.73±9.82	49.37±12.66	-6.308	0.000***
D	42.06±9.66	48.42±13.75	-4.342	0.000***
E	45.14±10.01	52.07±15.17	-6.121	0.000***
F	43.27±10.12	50.34±12.76	-5.384	0.000***

Results of AA-GCN model tests without fine-grained data sets

Category	True sample	Prediction sample	Correct classification	Accuracy rate	Precision rate	Recall rate	F1-Score
A	30	33	21	0.7222	0.7000	0.6364	0.6667
B	30	35	27		0.9000	0.7714	0.8308
C	30	31	21		0.7000	0.6774	0.6885
D	30	25	19		0.6333	0.7600	0.6909
E	30	31	26		0.8667	0.8387	0.8525
F	30	25	16		0.5333	0.6400	0.5818
Total	180	180	130		-	-	-

Results of AGCN model tests using fine-grained data sets

Category	True sample	Prediction sample	Correct classification	Accuracy rate	Precision rate	Recall rate	F1-Score
A	30	33	26	0.8278	0.8667	0.7879	0.8254
B	30	32	25		0.8333	0.7813	0.8065
C	30	31	26		0.8667	0.8387	0.8525
D	30	27	25		0.8333	0.9259	0.8772
E	30	28	21		0.7000	0.7500	0.7241
F	30	29	26		0.8667	0.8966	0.8814
Total	180	180	149		-	-	-

Results of AGCN model tests without fine-grained data sets

Category	True sample	Prediction sample	Correct classification	Accuracy rate	Precision rate	Recall rate	F1-Score
A	30	27	18	0.7056	0.6000	0.6667	0.6316
B	30	34	22		0.7333	0.6471	0.6875
C	30	33	24		0.8000	0.7273	0.7619
D	30	26	20		0.6667	0.7692	0.7143
E	30	35	22		0.7333	0.6286	0.6769
F	30	25	21		0.7000	0.8400	0.7636
Total	180	180	127		-	-	-

Comparison of technical assessment results between the two groups T-test (M±SD)

Test item	Control group	Experimental group	T	P
A	44.87±11.34	50.18±13.06	-3.951	0.000***
B	48.57±10.07	51.33±11.84	-4.872	0.000***
C	42.84±10.44	49.37±12.66	-5.334	0.000***
D	43.38±9.86	48.42±13.75	-5.007	0.000***
E	46.33±10.89	52.07±15.17	-5.671	0.000***
F	43.92±9.81	50.34±12.76	-4.021	0.000***

Test results of ST-GCN model using fine-grained data sets

Category	True sample	Prediction sample	Correct classification	Accuracy rate	Precision rate	Recall rate	F1-Score
A	30	33	25	0.7944	0.8333	0.7576	0.7936
B	30	30	23		0.7667	0.7667	0.7667
C	30	33	26		0.8667	0.7879	0.8254
D	30	27	21		0.7000	0.7778	0.7369
E	30	29	24		0.8000	0.8276	0.8136
F	30	28	24		0.8000	0.8571	0.8276
Total	180	180	143		-	-	-