Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

Table 1

Review of the different techniques from the literature that are most-related to the proposed research.

Study	No. of subjects	No. of activities	No. of features	No. of positions	Sensor position	Sensor type	Classifiers	Average of classification accuracy
[7]	10	7	11	2	Wrist and ankle	Accelerometer	PNN and K-PNN	96%
[8]	10	7	5	3	Hip, thigh, and ankle	Accelerometer	SVM, regularized LR, and Adaboost	78.2%
[9]	15	18	4	3	Wrist, waist, and thigh	Accelerometer	Decision tree	93.8%
[10]	4	5	12	4	Left thigh, right arm, ankle, and abdomen	Accelerometer	SVM, AMM, HNN	81% avg. per subject
[11]	30	6	24	1	Waist	Accelerometer and gyroscope	RF, SVM, NB, J48, NN, K-NN, Rpart, JRip, Bagging, and Adaboost	99.8% avg. per activity
[11]	18	1	9	1	Chest	Accelerometer	NB, SVM, RF, J48, NN, K-NN, Rpart, JRip, Bagging, and Adaboost	99.9% avg. per activity
[12]	10	11	8	8	Arms, thigh, waist, and chest	Accelerometer and electromyography	ANN	97.4%
[13]	10	30	12	1	Arm	Accelerometer, gyroscope, magnetometer, and electromyography	LDA and QDA	71.6%
[14]	19	13	19	4	Chest, ankle, hip, and wrist	Accelerometer and gyroscope	k-NN	99.13%
[15]	10	12	14	1	Wrist	Accelerometer	DT, SVM, k-NN, MLP, and NB	96.87%
[16]	30	6	17	1	Waist	Accelerometer and gyroscope	SVM and RF	99.22%
[16]	31	6	17	1	Waist	Accelerometer and gyroscope	SVM and RF	95.33%
[17]	30	6	5	1	Waist	Accelerometer and gyroscope	Multiple HMMs, MOT, and k-NN	92.6%
[18]	4	4	—	14	Upper body, leg, and hip	Inertial sensors and accelerometers	DL (NMF + SAE)	99.9%
[19]	10	12	—	3	Chest, right wrist, and left ankle	Accelerometer, ECG, gyroscope and magnetometer	Hierarchical classification method HCM	97.2%
Ours	18	12	14	7	Right and left thighs, right and left shins, and right and left feet and an EMG on the thigh	Accelerometer, gyroscope and EMG	Neural networks, naive Bayes, random forest, (k-NN), SVM, and decision trees	99.8%