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The objective of the Health Bot is to analyze and elaborate on the implementation of an intelligent system that can support telemedicine services using natural language processing (NLP) technology. Health Bot is a robust, modularized, and user-friendly platform that aims to improve the patient's interactions with the health care system. Using NLP and speech recognition algorithms, the platform can analyze and classify free text and voice input data to symptoms. The classified data are used in the machine learning (ML) training process of the artificial intelligence (AI) models in order to predict the probability of the patient suffering from a specific disease and get alerted in case of disorders. The Health Bot is acting as a medical virtual agent in order to collect any information needed in the sense of a medical interview and provide medical assessments, close appointments with the doctors, and monitor/record the patient's health condition. In this paper, two models have been trained for the Covid-19 and heart disease cases with 98.3% and 82% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Automatic gesture recognition is an important field in the area of human-computer interaction. Until recently, the main approach to gesture recognition was based mainly on real time video processing. 'e objective of this work is to propose the utilization of commodity smartwatches for such purpose. Smartwatches embed accelerometer sensors, and they are endowed with wireless communication capabilities (primarily Bluetooth), so as to connect with mobile phones on which gesture recognition algorithms may be executed. 'e algorithmic approach proposed in this paper accepts as the input readings from the smartwatch accelerometer sensors and processes them on the mobile phone. As a case study, the gesture recognition application was developed for Android devices and the Pebble smartwatch. 'is application allows the user to define the set of gestures and to train the system to recognize them. 'ree alternative methodologies were implemented and evaluated using a set of six 3-D natural gestures. All the reported results are quite satisfactory, while the method based on SAX (Symbolic Aggregate approXimation) was proven the most efficient. [ABSTRACT FROM AUTHOR]

The present paper studies the prospective and the performance of a forthcoming high-speed third generation (3.5G) networking technology, called enhanced uplink, for delivering mobile health (m-health) applications. The performance of 3.5G networks is a critical factor for successful development of m-health services perceived by end users. In this paper, we propose a methodology for performance assessment based on the joint uplink transmission of voice, real-time video, biological data (such as electrocardiogram, vital signals, and heart sounds), and healthcare records file transfer. Various scenarios were concerned in terms of real-time, nonreal-time, and emergency applications in random locations, where no other system but 3.5G is available. The accomplishment of quality of service (QoS) was explored through a step-by-step improvement of enhanced uplink system's parameters, attributing the network system for the best performance in the context of the desired m-health services. [ABSTRACT FROM AUTHOR]