## 4 Deep Learning
[79] used inertial sensor data from a wearable smartphone to apply an LSTM-CNN model to a HAR issue to identify activities of daily life such as standing, walking, walking downstairs, and going upstairs. The model is made up of a pre-processing phase that uses a two-layer LSTM to extract temporal features, two convolutional layers with a max-pooling layer to extract spatial features, a global average pooling layer (GAP), a batch normalization layer (BN), and an output layer (with a Softmax classifier) that produces a probability distribution over classes. Three open datasets (UCI, WISDM, and OPPORTUNITY) were used for testing, with overall accuracy ratings of 95.78%, 95.85%, and 92.63%, respectively. The cost-minimization method is used by the logistic regression-based Softmax classifier to describe multi-class classification problems. A generative deep learning model called autoencoder (AE) converts high-dimensional data into low-dimensional feature vectors by using copies of training data as input. This reduces the complexity of calculation. AE is an unsupervised method for learning data coding since it uses a feature learning paradigm that directly learns a para-metric map from inputs to their representation ([PERSON] and [PERSON] et al. [80]; [PERSON] et al. [81]). The encoder, a feature-extracting function, and the decoder, which maps the feature space back into the input space, are the two parts of an AE. An encoder and a decoder consist of an input layer, an output layer, and numerous hidden layers in between. In order to decrease the reconstruction error, which is a measurement of the disparity between the inputs and their reconstruction over all training datasets, a back-propagation technique is employed to modify the encoder and decoder parameters (the weights of the hidden layers). Deep AE provides a data-driven method for learning feature extraction in an effort to lessen the over-reliance on manually produced features prevalent in conventional machine learning techniques. Different fields use AE variations that have been established. By connecting the hidden representations of two single AEs, a deep coupling AE (DCAE) model is created. DCAE is used to gather the combined information from multi-modal data. [PERSON] et al. developed a DCAE in [80] with the objective of discovering a combined feature between vibration and acoustic data in order to categorize the health state of gears and bearings. For the purpose of combining multimodal signals obtained from several sensors, the model uses a deep learning approach based on the CAE. This technique fuses multimodal data fusion and feature learning into a single step. Furthermore, by self-teaching the high-level features through greedy layer-wise training, the created deep architecture can effectively extract correlations between vibration and acoustic data. To increase precision and decrease over-fitting in HAR with smartphone-embedded accelerometer sensor data, [PERSON] and [PERSON] et al. [82] present a deep sparse AE-based deep learning model. The sparse AE is a not supervised DL technique to learn an over-complete feature representation from the raw sensor data by modeling the loss function's sparsity term with the sparsity term and setting to zero some of the active units. The model can train stable, linearly separable, displacement, distortion, and change-invariant feature representations thanks to the sparsity term. The features of the sparse AE guarantee effective low-dimensional characteristic extraction from the high-dimensional structure of the input sensor data. Furthermore, Additionally, a complicated activity recognition framework is compactly represented. The model and the training algorithm of a deep belief network (DBN) were proposed by [PERSON] and [PERSON] et al. [83]. DBNs use a greedy layer-by-layer learning approach and a hierarchical structure with numerous stacked restricted Boltzmann machines (RBMs), followed by a fine-tuning. A visible layer and a concealed layer are both present in every RBM.