# Smart Clothing Framework for Health Monitoring Applications
## 2 Wearable Technology and Smart Clothing
### Computational Approach on Wearable Technologies
#### 2.6.2 Edge Computing for Health Monitoring  
The viability and acceptance of mobile healthcare are currently increasing. The upcoming fifth generation (5G) wireless systems appear to sustain exceptional high capacity, low latency, and massive connectivity [52]. The ongoing rapid advancement of wireless communication, data processing, IoT, the cloud, and edge computing have made mobile healthcare easier to implement than it was in the past [53]. Wireless Body Area Networks (WBAN) as part of IoT offer real-time and continuous healthcare monitoring to improve quality of life [54]. More recently the edge computing paradigm has fascinated researchers as a legitimate replacement of the standard cloud-based systems to reduce interaction timing and gather a huge amount of real data from IoT devices. Edge-based approaches can support time-dependent applications in the Industry 4.0 context in the near future [55]. [PERSON] et al. [56] proposed a novel offloading framework for multiserver multiaccess edge computing (MEC) network. In their proposed network, each access point (AP) is outfitted with an MEC server (MES) supporting mobile users (MUs) in executing computation-intensive tasks through offloading. The offloading decision problem was presented as a multiclass classification problem and the MES computational resource allocation task was expressed as a regression problem. A feedforward neural network (MTFNN) model was developed and trained to optimize the offloading decision and computational resource allocation as a mixed-integer nonlinear programming problem (MINLP) with high accuracy by solving the optimization problem. Then, the pretrained model directly extrapolated the solution to the MINLP problem online with very low computational cost. The testing results proved that the proposed MTFNN model outperforms the traditional optimization algorithms in terms of inference accuracy and high computational time. However, the authors could have provided some suggestions for generating the training datasets more efficiently. A novel framework for developing IOT with mobile edge computing (MEC)-based hierarchical machine learning (ML) task distribution for industrial applications was proposed by [PERSON] et al. [57]. ML and deep ML models developed for their device and MES, respectively, were pretrained offline using historical data and deployed accordingly to improve the computational execution time for the MEC setting. A joint optimization problem was devised to reduce the total delay considering a few factors including the ML model complexity and inference error rate, data quality and communications bandwidth. A closed-form solution was developed, and an optimal offloading strategy selection algorithm was proposed for multiple devices. The results showed that the communication cost progressively turns into a bottleneck, which reduces the delay of performing computingtasks using the edge computing strategy. However, the authors could exploit more AI techniques to verify the capability of the proposed framework. Fog and mobile edge computing have important roles in solving some difficulties of cloud computing, including mobility, response time, and location perception. A healthcare framework was proposed to detect and observe type 2 diabetes patients using a novel IoT system-based decision-making model. A hybrid technique based on both type 2 metrosophic and Visekriterijumsko kompromisno rangiranje (VIKOR) methods was applied. The results achieved by the proposed work showed the validity and robustness through reducing the execution time by 9.8% and increasing the patients' survival rates using personal data and symptoms [58]. However, the authors could investigate advanced machine learning methods to enhance the prediction accuracy. [PERSON] et al [59] proposed an emotion-aware system embedding a personal robot, smart clothing, and edge cloud for interacting with users. The EEG data, audio/video data, touch data, and physiological signals of the user were collected using smart clothing.