FSR-Based Smart System for Detection of Wheelchair Sitting Postures Using Machine Learning Algorithms and Techniques

Table 2

Previous work comparison.

Sr #	Reference #	Type of sensors	No. of postures	Classifiers/software	Accuracy	Limitations
1	[23]	Pressure sensor	13	(DT-CART), (RF), (KNN), (LR), (LDA), and (NB)	98.93%	Subjects for the dataset are minimal
2	[13]	Wisat mat	5	Wisat algorithms in MATLAB	81%	Only 17 datasets are available
3	[8]	3 pressure sensors, 1 sonar	4	KNN	76.05%	Less accuracy and classifier comparison is significantly less
4	[14]	12 pressure sensors	5	Decision tree (J48), (SVM), (MLP), Naive Bayes, and (-NN)	99.47%	Can record up to only 12 pressure points simultaneously
5	[15, 26]	A pressure sensor	4	Support vector machine (SVM)	89.6%	Multiple data collected by only using 10 subjects
6	[10]	16 sensors with 16 matrix	4	-nearest neighbors (-NN), random forest (RF), decision tree (DT) support vector machines (SVM), and LightGBM	99.03%	Not proposed a system through which it can correct wrong user posture