# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 6 Discussion and Future Research Directions  
In the IoHT layers presented in Section 2, the physiological data acquired are transmitted to the PHR layer (cloud storage), then the intelligent layer, and the result presentation layer. According to [101], a different approach for the analysis of clinical data in the cloud was proposed. In the area of FOG, clinical data can be collected and analyzed with available distributed edge resources easily by linking isolated cloud resources. The author in [102] observed the challenges between load balancing and remote resources edge capability. The PHR commonly used in medical/health environments may need more experts to operate/handle them as it involves a lot of information transfer across platforms, as reported in [92; 103]. Better feature analysis and representation is necessary across the IoHT chain for more enhanced processing of data by the computers. The PHR is considered the best option for data representation using the concept of web semantic ontologies, as reported in [92]. However, PHR faced a few challenges, particularly during the interconnection of wearable sensors with clinical vital health data. Similarly, the increase in the data flow (big data) and acquisition complexity may create difficulties in the analysis of clinical information by doctors. As such, the need for an advanced machine learning approach (AI) in the analysis and better interoperability of clinical vital signs cannot be overemphasized.  
Shallow data mining approaches such as DT, LR, RF, KNN, SVM, and ANN were used to compare intensive care unit mortality prediction models [104]. The authors ranked and compared the classification algorithms by fine-tuning their hyper-parameters for the best model fit. For instance, the physiological vital signs parameters, heart rate, blood glucose  
Figure 4: AI/ML algorithms and their application areas in healthcare.  
level, body temperature, blood pressure, oxygen saturation, etc., were measured and analyzed using a decision tree classifier. The increase in interconnections and data exchange among IoT devices has led to the era of \"data-hungry traditional ML methods\" [105] and the advanced ML approach (DL). Despite the performance of deep learning models on a large volume of data, the results obtained sometimes face interpretation challenges. For instance, a researcher must identify the performance evaluation metrics (confusion matrix) suitable for the interpretation of associated hyper-parameters. This led to more findings in the field of DL for better results analysis and presentation [106].  
Future research in the area of AI, IoT, and vital health signs can focus more on the Blockchain, tactile internet, online social networks, big data analytics, virtual reality, augmented reality, and the internet of nano things. Furthermore, as the number of wearable sensor production is increasing, there is a need for quality control and standard regulations to ensure interoperability and standardization of interfaces and protocols, which is missing in state-of-the-art health IoT devices. The lack of non-uniformity and communication between the sensors and the vital signs is regarded as the main challenge in AI/IoT integration. Similarly, there is a need to optimize security and minimize device power dissipation, computational complexity, and memory issues. Therefore, the integrated IoT health monitoring system can be optimized in real-time with proper encryption and an uninterrupted power supply.