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Software and Technology Standards as Tools
Published in Jim Goodell, Janet Kolodner, Learning Engineering Toolkit, 2023
Jim Goodell, Andrew J. Hampton, Richard Tong, Sae Schatz
The internet of things (IoT) is a network of physical objects embedded with sensors, software, and computing technologies for the purpose of connecting and exchanging data over the internet. For example, instrumented medical tools (such as smart infusion pumps, electronic medical mannequins, and environmental spatial sensors) could be used to unobtrusively collect data about medical students’ performance on a training scenario. (This data could then be streamed to a networked software analysis application, likely on a cloud server, to be analyzed and acted upon, such as by delivering real-time feedback.) There are countless potential uses for IoT systems for learning engineers—from instrumenting communications devices to inform military team training to using them to enhance the experience of a children’s museum.16 It’s important for learning engineering team members to realize that learning technologies have moved beyond online platforms and client-side software apps. Increasingly, just-in-time learning is being facilitated by intelligent things like smart speakers with natural language processing and other technological devices distributed throughout learners’ environments.
Introduction to E-Monitoring for Healthcare
Published in Govind Singh Patel, Seema Nayak, Sunil Kumar Chaudhary, Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare, 2023
Seema Nayak, Shamla Mantri, Manoj Nayak, Amrita Rai
There has been a huge evolution of the embedded systems market due to the fast development of the connected devices. Internet of Things (IoT) is the system of embedded devices, software, sensors, and network connectivity that allows objects to collect and exchange data. IoT permits objects to be sensed and controlled remotely across existing network infrastructure, creating opportunities for more direct integration between the physical world and computer-based systems, and resulting in better efficiency, accuracy, and financial benefits. Things or objects in the IoT sense can refer to a wide variety of devices, such as heart monitoring implants, biochip transponders on farm animals, electric clams in coastal waters, and automobiles with sensors. Radio-frequency identification (RFID) was seen as a prerequisite for the concept of IoT. If all objects and people in daily life were equipped with embedded devices and computers, then they could manage data and maintain record. In addition to RFID, things may be tagged through technologies as near-field communication, barcodes, QR codes, Bluetooth, and digital watermarking.
IoT Application for Healthcare
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Monika Sharma, Hemant K Upadhyay, Sapna Juneja, Abhinav Juneja
Even though health problems are rising worldwide, there are not enough health care professionals to combat them [1]. In developing countries, health care is a huge concern for the government and related institutions. Having enough facilities to offer in-house treatment is a major problem that has gained the attention of IoT researchers. The most promising solution we have is that with IoT, patients can manage their own health conditions and get help in emergency cases. On the other side, doctors can manage and consult with patients more easily. Over the years several advanced IoT applications have been developed to support patients and medical officers [9]. IoT helps healthcare to improve existing features by supporting patient management, medical records management, medical emergency management, treatment management and other facilities, increasing the quality of healthcare applications. As reported by Alam (2018), the number of connected devices is expected to reach 75.44 billion by the year 2021. The expansion of the internet and its collaboration with data science and artificial intelligence is making our machines ever smarter and able to communicate with us all the time.
Digital competencies for Singapore’s national medical school curriculum: a qualitative study
Published in Medical Education Online, 2023
Humairah Zainal, Xin Xiaohui, Julian Thumboo, Fong Kok Yong
The advent of digital technologies such as Artificial Intelligence (AI), Internet of Things (IoT) and Machine Learning (ML) have influenced healthcare delivery worldwide. In view of these digital transformations in healthcare, medical schools need to train their students in relevant digital competencies for them to succeed in clinical practice [1–5]. Such competencies would include skills in handling big data, understanding how they are being personalized in healthcare delivery through AI applications, utilizing AI and other digital technologies in a safe and ethical manner in clinical practice, knowing the limitations, pitfalls and benefits of these technologies for patient care, and communicating effectively with patients while using digital tools such as the electronic health records (EHRs), to name a few [2,6,7].
Attack detection and mitigation scheme through novel authentication model enabled optimized neural network in smart healthcare
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Security of devices against attacks is of major concern in the present days (Atzori et al. 2010) that depicts attacks can be executed on the IoT devices by an attacker. The first type of attack is the captivation of device control by the attacker, and the next is the information stealing attack, and the disruption of device operation is the final type of attack. Several other attacks like sinkhole attack (Fotohi and Bari 2020), Man-in-Middle (Newaz et al.), and denial of sleep attack (Al-Janabi and Al-Raweshidy 2017) are also disrupt the healthcare data. The health diagnosis systems based on IoT devices involve in diagnosing the data of patient’s health record stored in cloud and alert the patients during abnormal readings (Andrea et al. 2015). However, in case of insecure transmission, the diagnosis may provide incorrect measures leading even to the cause of death of the patients. There arises a possibility to occur unusual action in the health care systems with the absence of security in IoT. An adversary attack may listen to the communication and know the important health related information of patients. Some of the security related problems associated with the health care applications of IoT are data integrity, privacy and freshness of data, and authentication (Fotohi and Pakdel 2021; Tran-Dang et al. 2020). The identification and the authentication of the IoT sensors is a major challenge, and various hybrid strategies are used to authenticate and identify them (Deep et al. 2020; Vaishnavi and Sethukarasi 2021).
Heuristic-based channel selection with enhanced deep learning for heart disease prediction under WBAN
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
V. Muthu Ganesh, Janakiraman Nithiyanantham
Remote health monitoring systems have been implemented in the recent medical research field through adopting IoT devices (Xiao et al. 2020). These devices are interrelated for observing physical events. Heart disease is one of the major threats to increase the mortality rate, and thus, it is an important and challenging health issue. Consequently, there is a need of observing different symptoms such as cold sweats, shortness of breath, blood pressure, chest congestion and chest pain (Xiao et al. 2020). The physical examination is the significant goal for resolving the issues of precise diagnosis and delivery of therapies. Conversely, while processing the huge data to predict heart disease, it remains a complex problem (Guo et al. 2020). Although supervised learning is dependent on the efficiency of the training technique that needs necessary provision for handling the nonlinearity of the attained features (Liu et al. 2020).