IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning.

Internet of Things (IoT) technologies allow building a digital representation of people, objects, or physical phenomena to be available on the Internet. Thus, stakeholders can access this information from remote places or computational systems could analyze this data to find patterns, make decisions, or execute actions. For instance, a doctor could diagnose patients by analyzing the received data from an IoT system even when patients are located in a remote place. This article proposes an IoT system for monitoring electrocardiogram (ECG) signal and processing heart data in order to generate an alert when an arrhythmia is present. This system involves a Polar H10 heart sensor, machine-learning models to classify heart events, and communication technology to share and store patient's information. In the first place, the architecture of the IoT monitoring system and the communication between the components are described by discussing the designing criteria. Second, the experimentation process performs the training and the assessment of three classification algorithms, random forest, convolutional neural network, and k-nearest neighbors. The results show that k-nearest neighbor has the best accuracy percentage classifying the arrhythmias under study (premature ventricular contraction 94%, fusion of ventricular beat 81%, and supraventricular premature beat 82%); also, it is able to discern normal and unclassifiable beats with 93% and 97%, respectively.

Internet of things-based technological acceptance learning management framework for the physical education system.

BACKGROUND: Internet of Things (IoT) is a hopeful advancement that is an accurate international link for smart devices for total initiatives. Physical Education (PE) builds students' abilities and trust to engage in various physical activities, both within and outside their classrooms. The challenging characteristics in the learning management system include lack of setting a clear goal, lack of system integration, and failure to find an implementation team is considered as an essential factor. OBJECTIVE: In this paper, an IoT-based technological acceptance learning management framework (IoT-TALMF) has been proposed to identify the objectives, resource allocation, and effective team for group work in the physical education system. METHOD: Physical Educators primarily use the learning management framework as databases of increased management components, choosing to interact with students, teammates, organizations. Statistical course content analysis is introduced to identify and set clear goals that motivate students for the physical education system. The course instructor learning technique is incorporated with IoT-TALMF to improve system integration based on accuracy and implement an effective team to handle unexpected cost delays in the physical education system. RESULTS: The numerical results show that the IoT-TALMF framework enhances the identity accuracy ratio of 97.33%, the performance ratio of students 96.2%, and the reliability ratio of 97.12%, proving the proposed framework's reliability.