The impact of Internet of Things has been revolutionized in all fields of life, but its impact on the healthcare system has been significant due to its cutting edge transition. The role of Internet of Things becomes more dominant when it is supported by the features of mobile computing. The mobile computing extends the functionality of IoT in healthcare environment by bringing a massive support in the form of mobile health (m-health). In this research, a systematic literature review protocol is proposed to study how mobile computing assists IoT applications in healthcare, contributes to the current and future research work of IoT in the healthcare system, brings privacy and security in health IoT devices, and affects the IoT in the healthcare system. Furthermore, the intentions of the paper are to study the impacts of mobile computing on IoT in healthcare environment or smart hospitals in light of our systematic literature review protocol. The proposed study reports the papers that were included based on filtering process by title, abstract, and contents, and a total of 116 primary studies were included to support the proposed research. These papers were then analysed for research questions defined for the proposed study.

This work brings together the emerging virtual reality techniques and the natural user interfaces to offer new possibilities in the field of rehabilitation. We have designed a rehabilitation game based on a low cost device (Microsoft Kinect(TM)) connected to a personal computer. It provides patients having Parkinson's Disease (PD) with a motivating way to perform several motor rehabilitation exercises to improve their rehabilitation. The experiment was tested on seven Parkinson's Disease patients and results demonstrated significant improvements in completion time score and in the 10 Meters Walk Test score. Nevertheless, additional research is needed to determine if this type of training has a long-term impact. Both the device and protocol were well accepted by subjects, being safe and easy to use. We conclude that our work provides a simple and suitable tool resulting in a more enriching rehabilitation process where motivation is highly encouraged in PD patients. Feedback coming from participants corroborate the hypothesis that the system can be applied not only in clinical rehabilitation centers but at home.

The detection of tremors can be crucial for the early diagnosis and proper treatment of some disorders such as Parkinson's disease. A smartphonebased application has been developed for detecting hand tremors. This application runs in background and distinguishes hand tremors from common daily activities. This application can facilitate the continuous monitoring of patients or the early detection of this symptom. The evaluation analyzes 1770 accelerometer samples with cross-validation for assessing the ability of the system for processing unknown data, obtaining a sensitivity of 95.8% and a specificity of 99.5%. It also analyzes continuous data for some volunteers for several days, which corroborated its high performance.

Security in networks of unmanned aerial vehicles for surveillance with an agent-based approach inspired by the principles of blockchain. Ad Hoc Networks, 86, pp. 72-82.

Introduction Unsupervised digital cognitive testing is an appealing means to capture subtle cognitive decline in preclinical Alzheimer's disease (AD). Here, we describe development, feasibility, and validity of the Boston Remote Assessment for Neurocognitive Health (BRANCH) against in‐person cognitive testing and amyloid/tau burden. Methods BRANCH is web‐based, self‐guided, and assesses memory processes vulnerable in AD. Clinically normal participants (n = 234; aged 50–89) completed BRANCH; a subset underwent in‐person cognitive testing and positron emission tomography imaging. Mean accuracy across BRANCH tests (Categories, Face‐Name‐Occupation, Groceries, Signs) was calculated. Results BRANCH was feasible to complete on participants’ own devices (primarily smartphones). Technical difficulties and invalid/unusable data were infrequent. BRANCH psychometric properties were sound, including good retest reliability. BRANCH was correlated with in‐person cognitive testing ( r = 0.617, P < .001). Lower BRANCH score was associated with greater amyloid ( r = –0.205, P = .007) and entorhinal tau ( r = –0.178, P = .026). Discussion BRANCH reliably captures meaningful cognitive information remotely, suggesting promise as a digital cognitive marker sensitive early in the AD trajectory.

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Internet of Things (IoT) widely use analysis of data with artificial intelligence (AI) techniques in order to learn from user actions, support decisions, track relevant aspects of the user, and notify certain events when appropriate. However, most AI techniques are based on mathematical models that are difficult to understand by the general public, so most people use AI-based technology as a black box that they eventually start to trust based on their personal experience. This article proposes to go a step forward in the use of AI in IoT, and proposes a novel approach within the Human-centric AI field for generating explanations about the knowledge learned by a neural network (in particular a multilayer perceptron) from IoT environments. More concretely, this work proposes two techniques based on the analysis of artificial neuron weights, and another technique aimed at explaining each estimation based on the analysis of training cases. This approach has been illustrated in the context of a smart IoT kitchen that detects the user depression based on the food used for each meal, using a simulator for this purpose. The results revealed that most auto-generated explanations made sense in this context (i.e. 97.0%), and the execution times were low (i.e. 1.5 ms or lower) even considering the common configurations varying independently the number of neurons per hidden layer (up to 20), the number of hidden layers (up to 20) and the number of training cases (up to 4,000). INDEX TERMS Explainable artificial intelligence, human-centric artificial intelligence, Internet of Things, multilayer perceptron, smart kitchen, emotion detection.

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015-2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly. INDEX TERMS Healthcare, big data, big data management, big data analytics.