An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms

Table 1

Literature review.

Sr no	Paper	Type of sensors	No. of postures	Classifiers/software	Accuracy (%)
1	[3]	A film-type 8 × 8 FSR	5	(CNN), (DT), (SVM), (MLR), (NN), and (NB)	95.3
2	[11]	Inverse piezoresistive nanocomposite sensors	3	Three-layer BP neural network	98.75
3	[9]	Pressure sensor of film type	7	(CNN), (ANN)	97.5
4	[10]	An array of six flex sensors	7	Artificial neural network	97.8
5	[12]	16 sensors with 16 matrices	4	k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM	99.03
6	[31]	Accelerometer, gyroscope, and magnetometer sensors	5	Naive Bayes, SVM, and KNN	99.90