Impact of Sensor-Axis Combinations on Machine Learning Accuracy for Human Activity Recognition Using Accelerometer Data in Clinical Settings

Takahiro Yamane; Moeka Kimura; Mizuki Morita

doi:10.5334/paah.441

Figures & Tables

Axis combinations investigated in this study. The ActiGraph GT9X Link is equipped with an accelerometer, a gyroscope, and a magnetometer. The relationship between axis combination and activity classification accuracy was evaluated.

Waveform data depicting sensor readings from two distinct body locations. The nondominant wrist data are represented by the red line; the chest data are denoted by the green line. Labels (0–9) correspond to specific physical activities: 0, lying in a supine position; 1, standing; 2, sitting; 3, eating; 4, brushing teeth; 5, using the restroom; 6, walking; 7, ascending or descending the stairs; 8, running; and 9, other movements. **(a)** x-axis acceleration measurements. **(b)** x-axis angular velocity recordings. **(c)** x-axis magnetic field intensity observations.

Table 1

Evaluation results of the nondominant wrist classifier.

		ACC	GYR	MAG	ACC_GYR	ACC_MAG	GYR_MAG	ACC_GYR_MAG
Lying in the supine position	Precision	0.9462	0.7696	0.5148	0.9510	0.9511	0.8287	0.9446
	Recall	0.9635	0.6889	0.5942	0.9686	0.9791	0.8250	0.9737
	F-value	0.9496	0.6947	0.5402	0.9556	0.9614	0.8183	0.9552
Standing	Precision	0.9176	0.7698	0.5038	0.9298	0.9316	0.8843	0.9350
	Recall	0.9353	0.8410	0.5641	0.9485	0.9404	0.8331	0.9459
	F-value	0.9186	0.7890	0.4975	0.9272	0.9261	0.8192	0.9306
Sitting	Precision	0.8087	0.6978	0.4160	0.8578	0.7625	0.8150	0.9171
	Recall	0.8026	0.7425	0.4949	0.8282	0.7618	0.8115	0.8690
	F-value	0.7812	0.6984	0.4269	0.8258	0.7509	0.7894	0.8769
Eating	Precision	0.8594	0.6457	0.7353	0.8571	0.8506	0.7169	0.8650
	Recall	0.8829	0.7930	0.7226	0.9093	0.8904	0.8225	0.9068
	F-value	0.8576	0.7053	0.6982	0.8684	0.8543	0.7525	0.8712
Brushing teeth	Precision	0.7800	0.8805	0.4199	0.8439	0.7945	0.8287	0.8433
	Recall	0.7442	0.8515	0.3921	0.7876	0.7393	0.8410	0.7979
	F-value	0.7524	0.8486	0.3640	0.8013	0.7545	0.8316	0.8104
Using the restroom	Precision	0.6773	0.5862	0.7384	0.7043	0.7441	0.7764	0.7904
	Recall	0.6484	0.4075	0.7173	0.6544	0.7782	0.6681	0.7634
	F-value	0.6444	0.4646	0.7127	0.6640	0.7376	0.6921	0.7588
Walking	Precision	0.8781	0.7993	0.7427	0.8761	0.9170	0.8436	0.9295
	Recall	0.8310	0.7620	0.8097	0.8632	0.9033	0.8465	0.9010
	F-value	0.8241	0.7421	0.7675	0.8413	0.9017	0.8314	0.9039
Ascending or descending the stairs	Precision	0.8634	0.7314	0.8118	0.8721	0.9105	0.8326	0.8979
	Recall	0.8418	0.7237	0.8527	0.8333	0.8947	0.8324	0.8920
	F-value	0.8374	0.7114	0.8287	0.8413	0.8976	0.8269	0.8890
Running	Precision	0.9974	0.9005	0.8461	0.9974	0.9974	0.9247	0.9974
	Recall	0.9897	0.9032	0.8729	0.9897	0.9897	0.9034	0.9897
	F-value	0.9926	0.8835	0.8549	0.9926	0.9926	0.8968	0.9926
Other movements	Precision	0.6256	0.5614	0.5704	0.6604	0.7210	0.6962	0.7316
	Recall	0.6574	0.5713	0.4783	0.6944	0.7322	0.6539	0.7423
	F-value	0.6256	0.5547	0.4903	0.6624	0.7038	0.6586	0.7192

F-value comparison of different accelerometer axes combinations with respect to each activity. **(a)** Accelerometer attached to the nondominant wrist. **(b)** Accelerometer attached to the chest.

Table 2

Evaluation results of the chest classifier.

		ACC	GYR	MAG	ACC_GYR	ACC_MAG	GYR_MAG	ACC_GYR_MAG
Lying in the supine position	Precision	0.9922	0.6341	0.8043	0.9922	0.9922	0.7997	0.9956
	Recall	1.0000	0.6380	0.7923	1.0000	1.0000	0.8154	1.0000
	F-value	0.9956	0.6282	0.7700	0.9956	0.9956	0.7855	0.9976
Standing	Precision	0.5640	0.5938	0.8219	0.6455	0.8345	0.7981	0.8528
	Recall	0.5951	0.6212	0.8756	0.6874	0.8641	0.8459	0.8675
	F-value	0.5434	0.5647	0.8139	0.6333	0.8118	0.7905	0.8094
Sitting	Precision	0.5480	0.5538	0.7834	0.6019	0.7496	0.7120	0.8119
	Recall	0.5410	0.5660	0.7846	0.6103	0.7821	0.6949	0.7667
	F-value	0.5032	0.5070	0.7295	0.5624	0.7555	0.6691	0.7591
Eating	Precision	0.6444	0.6970	0.6323	0.7315	0.8037	0.8095	0.8442
	Recall	0.6884	0.7562	0.5881	0.7408	0.7516	0.7933	0.8161
	F-value	0.6513	0.7069	0.5875	0.7230	0.7558	0.7774	0.8187
Brushing teeth	Precision	0.9271	0.8518	0.7378	0.9112	0.9282	0.8897	0.9386
	Recall	0.8731	0.8466	0.7342	0.8825	0.8968	0.8904	0.9171
	F-value	0.8861	0.8353	0.6917	0.8820	0.8998	0.8801	0.9224
Using the restroom	Precision	0.6606	0.5777	0.8377	0.7006	0.8543	0.8405	0.8567
	Recall	0.5841	0.4117	0.8074	0.6178	0.8768	0.8068	0.8569
	F-value	0.5876	0.4627	0.8028	0.6337	0.8491	0.8096	0.8420
Walking	Precision	0.9371	0.8527	0.8053	0.9711	0.9606	0.8876	0.9761
	Recall	0.9454	0.7020	0.9082	0.9354	0.9547	0.9099	0.9593
	F-value	0.9358	0.7219	0.8486	0.9362	0.9541	0.8912	0.9661
Ascending or descending the stairs	Precision	0.9518	0.7427	0.9302	0.9383	0.9642	0.8965	0.9532
	Recall	0.9331	0.8711	0.9078	0.9601	0.9679	0.9117	0.9785
	F-value	0.9360	0.7785	0.9144	0.9422	0.9643	0.8993	0.9641
Running	Precision	0.9956	0.9906	0.9263	0.9952	1.0000	0.9906	1.0000
	Recall	0.9750	0.9947	0.9374	0.9861	0.9750	0.9974	0.9889
	F-value	0.9776	0.9923	0.9283	0.9888	0.9800	0.9937	0.9933
Other movements	Precision	0.6476	0.6179	0.6795	0.7025	0.7815	0.7213	0.7917
	Recall	0.6389	0.5684	0.5762	0.6980	0.7577	0.6548	0.7777
	F-value	0.6186	0.5741	0.6081	0.6886	0.7581	0.6725	0.7739

Confusion matrices comparing anticipated and actual activities, based on data from the nondominant wrist-mounted sensor. The vertical entries represent the actual activities performed, and horizontal entries depict the activities inferred by the activity recognition classifier. **(a)** Gyroscope. The orange highlights denote errors in distinguishing between using the restroom, sitting, eating, and other movements. **(b)** Magnetometer. The purple highlights denote errors in distinguishing between brushing teeth, lying in a supine position, standing, sitting, and eating. **(c)** Gyroscope and magnetometer. The orange and purple highlights signify enhanced recognition precision when contrasted with (a) and (b), respectively.

Confusion matrices comparing anticipated and actual activities based on data from the chest-mounted sensor. **(a)** Accelerometer. The green highlight denotes errors in distinguishing between standing and sitting. **(b)** Gyroscope. The blue highlight denotes errors in distinguishing between lying in the supine position, standing, and sitting. The orange highlight denotes errors in distinguishing between using the restroom, sitting, eating, and other movements. The red highlight denotes errors in distinguishing between walking and ascending or descending the stairs. **(c)** Magnetometer. The light blue highlight denotes errors in distinguishing between eating, brushing teeth, and using the restroom. **(d)** Accelerometer and gyroscope. The blue and red highlights signify enhanced recognition precision when contrasted with (b). **(e)** Accelerometer and magnetometer. The green highlight signifies enhanced recognition precision when contrasted with (a). **(f)** Gyroscope and magnetometer. The orange and light blue highlights signify enhanced recognition precision when contrasted with (b) and (c), respectively.

Impact of Sensor-Axis Combinations on Machine Learning Accuracy for Human Activity Recognition Using Accelerometer Data in Clinical Settings

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