# Advances in IoMT for Healthcare Systems
## 2 Review of Published Articles
The 5G operators can offer customized network capacity and local services according to the requirements of their clients by using the use-case of network slicing. The operators can also establish a strategic viewpoint of their costs and revenues regarding actors, functions, architectures, and processes during the slicing of their networks. Furthermore, the service providers can unlock the core benefits of AI and ML in various standards and industrial forums to incorporate intelligence and automation in their network services. The suggested NS can help design healthcare models that are agility smart, more efficient, and cost-effective. In Contribution 7, the authors conducted a detailed review of IoT-enabled data acquisition of human vital signs through non-invasive mechanisms that do not require skin contact. It is revealed that health informatics is lacking the applications of a self-power high-speed smart processing unit that can perform real-time non-invasive data acquisition. These processing units should be additionally powered with the cross-platform ability to communicate with different nodes. In this regard, the synergistic integration of modern technologies (i.e., AI, deep learning, and metaverse, etc.) is required to empower the sensing techniques and multi-camera systems. In Contribution 8, a cooperative energy-efficient routing protocol is proposed for underwater wireless sensor networks. Therein, the hotspot issue is resolved with the help of sink mobility. The sensor nodes collaboratively share their data with the sink nodes to increase the network lifetime and harvest energy. In mobile healthcare, applications of energy-efficient protocols are of much interest for online patient monitoring and data acquisition tasks. In Contribution 9, the authors proposed an adaptive support vector machine (SVM) classifier for the improved classification of electroencephalography (EEG) signals. The traditional common spatial patterns (CSP) filtering method has shortcomings relating artifacts, whereby the ECG components are distorted. The proposed feature based adaptive extraction method outperforms the existing methods in terms of information transfer rate and classification accuracy. In Contribution 10, the authors proposed a deep learning based novel method for emotion detection through multimodal physiological signals. For emotion charting, the accurate classification of physiological signals is challenging due to their non-linear nature and noise enclosure during their recording. The proposed method processes the physiological signals to remove noise with different filtering, classification and deep learning methods for the identification of four different emotional states. The accuracy of the proposed system is reported higher than that of the other related methods. In IoMT, such agility smart emotion charting methods are of great demand especially for stroke-affected patients and therapeutic treatments. In Contribution 11, the authors proposed an improved fall detection model aimed at elderly individuals. It is a hardware-independent model that uses transfer learning on small datasets to make it generalizable for various wearable devices. The results showed that the proposed transfer learning model has considerably improved the performance in terms of the area under the curve, F1-scores, and false positive prediction rate for fall detection. We need sophisticated fall detection systems because major falls cause morbidity and mortality, especially in older adults. In Contribution 12, the authors proposed an interactive system for cardiovascular diseases. The system collects and analyzes cardiovascular data over the Internet using preprocessing algorithms and mathematical analysis of cardiac records to ensure continuous healthcare, especially for patients in remote areas. In Contribution 13, we found a monitoring system that can support ML, AI, and IoT solutions to ensure more accurate healthcare treatment for heart failure patients.