A wireless body area network (WBAN) is a wireless network of wearable computing devices and intelligent physiological sensors. The intelligent physiological sensors collect and process sensitive data from the patient body. The security, reliability, and trustworthiness of sensitive data collected and processed by intelligent physiological sensors are critical due to its unique application domain. Moreover, consistent and reliable data gathering along with its transmission also plays a vital role in WBANs. Trust management (in BANs) has been found as a useful tool to improve cooperation among sensor nodes, security as well as reliability. The paper recommends a novel and efficient, lightweight trust assessment scheme (ETAS) suitable for health application domains and does not rely purely on any encryption technique. The primary purpose is to develop an exciting comprehensive, novel trust estimation framework for BANs to enhance reliability, dependability, security by isolating compromised (hotspot) nodes with great resource efficiency. ETAS incorporates several exclusive (unique) features like efficient trust evaluator, secure and attack resistant, along with competent trust aggregator function to achieve comprehensive trust score. The trust evaluator function is a multi-trust (communication trust, data trust, and energy trust) strategy to deal with severe internal security threats such as badmouthing attack, ballot-stuffing attack, sybil attack, traitor attack, etc. with less resource consumption. Moreover, ETAS incorporates both the success rate and misbehavior component during trust evaluation. The success rate record the number of successful/unsuccessful interactions among sensor nodes in terms of packet send/receive. The misbehavior component keeps records of current and past misbehavior of sensor nodes for effective decision making. Moreover, ETAS focus on the frequency of interaction among biomedical sensor nodes within a specified period to analyze their behavior for efficient trust decisions. Furthermore, ETAS incorporates temperature, data trust as well as the trust score of biomedical sensors to identify hotspot nodes in BSN. ETAS offers full flexibility to adjust the trust threshold, trust domain, reward, and punishment term according to system and application requirements. ETAS's efficiency is validated through several outcomes (MATLAB R2019a) along with theoretical analysis in terms of energy consumption, attack detection, mitigation, trust computation cost, and packet delivery ratio.

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Paroxysmal kinesigenic dyskinesia (PKD) is characterized by recurrent attacks of abnormal involuntary movements that are triggered by sudden movement, intention to move, or acceleration. A 10-year-old boy presented with paroxysmal, involuntary twisting movements of the left upper and lower limbs, precipitated by sudden body movements, lasting for 10-15 seconds and subsiding spontaneously. On examination, choreiform movements were observed, which were precipitated by sudden movements during some activities. The patient responded to carbamazepine with complete subsidence of the movements. The diagnosis of PKD was further confirmed by genetic testing. A high suspicion index helps in the prompt and early diagnosis of this rare entity.