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The development and growth of wireless sensor networks play a vital role in the field of medical and health servicing sectors. In modern technology, wireless communication provides a lot of possibilities for the sharing of information at anytime and anywhere. The main objective of this chapter is to explore body area network communication technologies driven by AI. Medical AI mainly utilizes computer network topologies to perform monitoring, recording, diagnoses, and treatment process [21].

Table 1.3 Impact of AI-mHealth communication system in healthcare.

Source	Subject matter	Role of AI-Driven mHealth devices	Related performance measures
[12–14]	Medical big data analysis	Personalized clinical decision-making	A complex diagnosis process in multiple chronic illnesses became simplerSimilarities in illness patterns are analyzed effectively
[15]	Digital healthcare	Health Monitoring - Continuous Glucose Monitoring (CGM)CardioMEMS Heart Sensor with Wireless implantable Hemodynamic Monitoring (W-HM)Automated diagnostic algorithm	POCUS uses in heart diseasesCGM early detects hypoglycemic episodesW-HM results in 30% reduction in heart failure readmissions (hazard ratio 0.70, 95% confidence interval 0.60–0.84)
[16]	Atrial fibrillation detection	C statistic–based trained ANN using smart watch data	ANN predicts AF with 90.2% specificity and 98% sensitivity
[17]	Echocardiographic evaluation	Machine learning–based Associative memory classifier	Achieves 22% more accuracy in prediction than SVM.
[18, 19]	Transthoracic 3D Echocardiography (TTE) Left Heart Chamber Quantification	Automated Adaptive Analytics Algorithm	Achieves better correlation (r = 0.87 to 0.96) with manual 3D TTE
[20]	Echocardiogram Interpretation	CNN-based detection trained with 14 035 Echocardiogram images	CNN detects hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary arterial hypertension with 95% accuracy.

A body area network has wide applications in medical and non-medical fields. In the medical field, they are either used as wearable devices or implanted in a patient’s body or as a remote monitoring system to keep track of patient’s health based on the sensory nodes positioned in their bodies. This is very sensitive to older adults or patients with chronic diseases. Through biomedical sensors, motion detectors, and wireless communication, monitoring of every activity like glucose, blood pressure, and pulse rate is done. Figure 1.5 shows a typical body area network with wearable devices for health monitoring. All the required information is collected through the central hub and processed wirelessly to the healthcare provider or medical staff during emergencies. The end devices can also be wearable [22–24], which act as transducers to display human activities, temperature, and pressure.

Communication in the body sensor network is of two types.

1 (i) In-body communication uses RF signals between sensory nodes, which are implanted in our human body. The frequency at which the communication has to take place is defined by Medical Implantable Communication Service (MICS), and the range of frequency is 402–405 MHz

2 (ii) On-body communication is the communication between wearable sensory nodes, which consists of biosensors. Ultrawideband (UWB) can be used for on-body communication. IMS based, which is mainly used for industrial, medical, and scientific applications having a range of 2.4–2.485 GHz. Many electronic applications operate on this band.

Figure 1.5 Wearable devices in the health monitoring system (Adopted from [21]).