Table 2.

Summary table of anomaly detection techniques with the traditional approach

Techniques	Advantages	Limitations	Refs.
Rule-based	Application simplicity High inference speed Low computational cost Transparent and explainable process	Expert knowledge required Difficulty in capturing complex relationships Limited to a low number of postures and differentiated from each other	[49, 63, 65, 74]
Statistical	Interpretability of results They provide probability estimations for each class Reasonable training time	Assumption that the data follow a probability distribution Expert knowledge required Sensitive to irrelevant features Limited performance with complex data	[13–23, 28]
Intelligent	Ability to capture complex relationships Greater accuracy Adaptable to input changes Ability to ignore redundant features	Need for large data sets Increased training time and computational resources Difficulty in selecting hyperparameters Lack of interpretability of the decision making process	[5, 15, 17, 19, 24–32, 66, 68, 72, 75–85]