Table 1.

Summary of various deep learning algorithms for human activity identification.

Deep Learning Methods	Descriptions	Strengths	Weaknesses
Deep belief networks [38,39,51]	Deep belief networks have direct connection to the lower layer of the network and hierarchically extract features from data.	Uses feedback mechanism to extract relevant features through unsupervised adaption.	High computation complexity due to high parameters initialization.
Convolutional neural networks [8,14,37,40,42]	Uses interconnected network structures to extract features that are invariant to distortion.	Widely utilized for human activity identification due to its ability to model time dependent data. It is invariant to changes in data distribution.	Requires large amount of training data to obtain discriminant features. In addition, it requires a high number of hyper-parameter optimization.
Recurrent neural networks [9,43,44]	Deep learning algorithm for modeling temporal changes in data.	Ability to extract temporal dependencies and complex changes in sequential data.	Difficult to train due to large parameter update and vanishing gradients.
Deep autoencoder algorithms [46,47,49,50]	Generative deep learning model that replicates copies of training data as input.	Reduces high-dimensional data to low dimensional feature vectors. This helps to reduce computational complexity.	Lack of scalability to high-dimensional data. It is difficult to train and optimize, especially for one layer autoencoder.