# Smart Clothing Framework for Health Monitoring Applications
## 2 Wearable Technology and Smart Clothing
### Computational Approach on Wearable Technologies
#### 2.6.4 Deep Learning for Healthcare  
At present, deep learning has played a great role in analysing a huge amount of data with its advanced techniques. It acts through processing a large amount of data to solve complex problems using artificial neural networks. There are some basic differences between traditional machine learning and deep learning algorithms. Traditional machine learning algorithms learn from model functions and then predict future activity from history, whereas the deep learning ones contain more than one hidden layer to understand data features and relations among the parameters [67]. Deep learning has elevated performance in learning feature representation from raw data. Currently, many deep learning algorithms including Deep Neural Network (DNN), Convolutional Neural Networks (CNNs), Recurrent neural networks (RNN), Restricted Boltzmann machines and Deep autoencoders are available for the applications of handling large amount of data and making effective decisions [67]. Deep learning algorithms are principally self-directed, generating new features and recommending solving end-to-end problems, which can be applied in appropriate data analytics and decision making through the data collected by the smart clothing [68]. Namely, CNN performs enhanced extractions of data information compared to machine learning algorithms such as SVM. Furthermore, RNN is applied for analysing health-related temporal sequence data. Learning features and classification automatically with high accuracy and performing automatic feature extraction without the involvement of human intervention are the key advantages of deep learning. The key shortcomings of deep learning are that it is extremely time consuming and requires superior performances of the hardware. Deep learning has the capability of offering results for complicated analysis. For example, due to them having numerous levels of complexity, the networks perform complex decision making in many cases. However, this is expensive for training and building models in terms of time. [PERSON] and [PERSON] [53] suggested a convolutional neural network (CNN) with a transfer learning technique for detecting voice pathology in a mobile healthcare framework. The voices were obtained using a smartphone and processed. The experimental results showed that the accuracy of voice pathology detection was as high as 97.5%. According to the achieved results, the cloud manager directs the decision and recording of the voice signal to the local caring authority. Then, the authority or doctor analyses the signal, makes the decision and gives advice to the patients. However, the authors could add GUI to make the outcome visible. [PERSON] et al. [69] recommended a method combining a hybrid recommendation algorithm and deep neural networks (DNN) for predicting disease considering medical records while ambiguous explanation of symptoms detected. The authors used real-world datasets for predicting potential phenotypes by analysing the high-level and low-level order features of the disease. The experimental results showed considerable accuracy in predicting diseases; however, further research is still required to investigate additional methods to enhance the capability of prediction accuracy and conditions for making appropriate decisions. [PERSON] and [PERSON] [70] proposed a method for classifying unnaturally induced gait alterations by merging noninvasive wearable sensors and deep learning. The proposed method diagnosed gait abnormalities considering a symptom that has could potentially indicate movement illnesses. For collecting pressure data on 12 patients, long-term and short-term memory networks with deep learning architectures were employed in their method. The experimental results showed a good accuracy of 82.0% in classifying the gait function of patients precisely, which can be beneficial in making decisions regarding the status of a patient. However, the system did not provide a performance comparison with other methods to understand the capability of the proposed method.