Table 3.

Detection of yoga using various IIoT approaches and their comparison.

References	Approaches	Descriptions
[13,14,15,85,86,88,89,90,91,92,93,94,95]	Sensor-based approach	Multiple sensors are used to detect yoga postures, including wearable, infrared sensors, RFID, and smart mat.
[96,97]	Vision-based approach	Relies on the camera for the input, which is further processed using intelligent approaches for the detection of the yoga postures.
[98]	Logistics regression	An extension of ordinary regression; it is a powerful and popular technique for supervised classification for modeling a dichotomous variable for an associated label.
[99]	Adaboost	An ensemble method to combine weak classifiers to create a powerful classifier. To attain high accuracy for the model, it continues to add learners until a robust classifier is reached.
[100,101,102]	Random forest	In RF, each tree is reliant on values from a random vector that was randomly sampled and had a uniform distribution across all of the forest trees.
[103]	Support vector machine (SVM)	It has two classifiers and is an SVM classifier. Nonetheless, a multiclass SVM is widely used because most issues involve multiple classes.
[3,104]	K-nearest neighbor (KNN)	KNN saves all potential examples and categorizes them according to their similarities. It is primarily used with the pattern recognition method.
[105]	Deep learning-based methods	Deep learning is essentially based on ANN and it can be compared to the human brain.
[106,107,108,109]	AutoEncoder	A rich and versatile framework for discovering the salient features of data in an unsupervised manner. Used to drive the learning of a deep illustration of the volumetric human body structure.
[103,110,111]	Convolutional neural networks (CNN)s	A great choice because they have proven to have a significant amount of potential for pose classification tasks. They can be trained directly on pictures or on key human skeleton joint locations.
[112]	Recurrent neural networks (RNNs)	RNNs are useful for processing sequential data since they preserve a neuron’s prior data. RNNs have difficulty remembering the initial steps necessary to forecast the current task when there are too many intermediate steps in a yoga asana.
[113]	Long short-term memory (LSTM)	A well-known RNN called an LSTM has the ability to naturally remember knowledge or data for sufficient lengths of time. The LSTM algorithm employs three gates: input, update, and forget. Resultantly, an LSTM will selectively ignore or recall the learned information.
[114,115,116,117,118]	Deep neural networks (DNNs)	DNNs have demonstrated exceptional performance on visual classification functions. DNNs can capture the complete context of every body joint since each joint regressor uses the entire image as a signal.
[119,120,121,122,123,124,125,126,127]	Hybrid approaches	Several algorithms make use of hybrid models. For example, SVM and Inception V3 are hybrid algorithms. Another study classified data using a hybrid 798 CNN–LSTM layer after extracting key points using OpenPose.