Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling?

Figures & data

Figure 1. Inertial measurement unit and force plate traces from a single delivery.

Table 1. The mean, standard deviation, and range for ball release speed and ground reaction forces across intensity zones for all participants.

	Low			Medium			High
Variable	Mean	SD	Range	Mean	SD	Range	Mean	SD	Range
Ball release speed (km·h-1)	104.46	5.96	31.79	107.53	5.56	31.79	112.8	6.32	30.04
Peak_y (BW)	2.6	0.71	4.31	2.73	0.73	4.31	3.08	0.87	4.93
Peak_ z (BW)	5.05	1.19	6.75	5.24	1.28	6.75	5.76	1.63	7.89
Impulse_y (BW·s)	0.05	0.04	0.22	0.05	0.04	0.22	0.06	0.04	0.23
Impulse_z (BW·s)	0.4	0.08	0.71	0.39	0.08	0.71	0.39	0.08	0.45
Loading rate_y (BW·s^−1)	24.66	9.12	51.09	26.63	9.26	51.09	29.91	10.95	57.36
Loading rate_z (BW·s^−1)	47.45	16.2	99.92	50.95	16.11	99.92	55.83	19.89	97.18
Average contact time (s)	0.37	0.09	0.6	0.36	0.07	0.47	0.35	0.08	0.39

Table 2. Predicted peak force values.

	MAPE				RMSE (BW)
	Upper back		Wrist		Upper back		Wrist
Model	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)
Accelerometer only
RF	24.9	27.5*	26.8	25.7	1.36	0.78	1.47	0.77
XGB	25.2	28.9	28.9	25.2	1.49	0.84	1.68	0.79
LSVM	22.1	25.2	22.1	26.7	1.30	0.75	1.27	0.79
Accelerometer and gyroscope
RF	23.6	24.8	25.5	26.1	1.29	0.71	1.41	0.78
XGB	23.7	25.8	27.0	27.5	1.38	0.76	1.52	0.83
LSVM	22.4	24.1	23.1	27.4	1.31	0.73	1.34	0.82

Key: LSVM = Linear support vector machine; MAPE = Mean absolute percentage error; RF = Random forest; RMSE = Root mean square error; XGB = Gradient boosting. * = Significantly different from the corresponding model and axes in the accelerometer and gyroscope dataset.

Table 3. Predicted impulse values.

	MAPE				RMSE (BW·s)
	Upper back		Wrist		Upper back		Wrist
Model	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)
Accelerometer only
RF	18.5	280	16.6*	288	0.07	0.04	0.06	0.04
XGB	19.6	280	18.1	233	0.08	0.04	0.07	0.04
LSVM	17.1^	296	20.2	221	0.07	0.05	0.08	0.04
Accelerometer and gyroscope
RF	17.1~	255	16.4	249	0.07	0.04	0.06	0.04
XGB	21.6^	220	18.0	286	0.09	0.04	0.08	0.04
LSVM	16.2^~	228	19.3	215	0.06	0.04	0.07	0.04

Key: LSVM = Linear support vector machine; MAPE = Mean absolute percentage error; RF = Random forest; RMSE = Root mean square error; XGB = Gradient boosting. ^ = Significantly different from the corresponding model and axes when using the wrist IMU. ~ = Significantly different from XGB using the same IMU and dataset. * = Significantly different from LSVM using the same IMU and dataset.

Table 4. Predicted loading rate values.

	MAPE				RMSE (BW·s^−1)
	Upper back		Wrist		Upper back		Wrist
Model	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)	Vertical (Z)	Horizontal (Y)
Accelerometer only
RF	35.1^	37.3	39.9*	38.5	17.1	9.51	19.7	9.92
XGB	37.5	40.9	38.7	41.6	19.2	10.95	20.9	11.32
LSVM	36.3	42.0*	33.3	39.2	18.9	10.86	17.3	10.35
Accelerometer and gyroscope
RF	33.6^	33.6^~	39.2	39.5	16.7	8.88	19.7	10.14
XGB	35.2	38.2^	37.9	43.9	18.8	10.18	19.2	11.42
LSVM	32.6	36.9	36	40.8	16.8	9.7	18.1	10.57

Key: LSVM = Linear support vector machine; MAPE = Mean absolute percentage error; RF = Random forest; RMSE = Root mean square error; XGB = Gradient boosting. * = Significantly different from the corresponding model and axes in the accelerometer and gyroscope dataset. ^ = Significantly different from the corresponding model and axes when using the wrist IMU. ~ = Significantly different from XGB using the same IMU and dataset. * = Significantly different from LSVM using the same IMU and dataset.