Bigdata Oriented Multimedia Mobile Health Applications

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Real-Time Big Data Processing and Analytics: Concepts, Technologies, and Domains

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

Mobile Health Interventions and RCTs: Structured Taxonomy and Research Framework

Mobile Health Interventions (MHIs) have addressed a range of healthcare challenges and have been evaluated using Randomized Controlled Trials (RCTs) to establish clinical effectiveness. Using PRISMA we conducted a systematic literature review of RCTs for MHIs and identified 70 studies which were analyzed and classified using Nickerson-Varshney-Muntermann (NVM) taxonomy. From the resultant iterations of the taxonomy, we extracted insights from the categorized studies. RCTs cover a wide range of health conditions including chronic diseases, general wellness, unhealthy practices, family planning, end-of-life, and post-transplant care. The MHIs that were utilized by the RCTs were varied as well, although most studies did not find significant differences between MHIs and usual care. The challenges for MHI-based RCTs include the use of technologies, delayed outcomes, patient recruitment, patient retention, and complex regulatory requirements. These variances can lead to a higher rate of Type I/Type II errors. Further considerations are the impact of infrastructure, contextual and cultural factors, and reductions in the technological relevancy of the intervention itself. Finally, due to the delayed effect of most outcomes, RCTs of insufficient duration are unable to measure significant, lasting improvements. Using the insights from seventy identified studies, we developed a classification of existing RCTs along with guidelines for MHI-based RCTs and a research framework for future RCTs. The framework offers opportunities for (a) personalization of MHIs, (b) use of richer technologies, and (c) emerging areas for RCTs.

Wearable Sensors for Vital Signs Measurement: A Survey

With the outbreak of coronavirus disease-2019 (COVID-19) worldwide, developments in the medical field have aroused concerns within society. As science and technology develop, wearable medical sensors have become the main means of medical data acquisition. To analyze the intelligent development status of wearable medical sensors, the current work classifies and prospects the application status and functions of wireless communication wearable medical sensors, based on human physiological data acquisition in the medical field. By understanding its working principles, data acquisition modes and action modes, the work chiefly analyzes the application of wearable medical sensors in vascular infarction, respiratory intensity, body temperature, blood oxygen concentration, and sleep detection, and reflects the key role of wearable medical sensors in human physiological data acquisition. Further exploration and prospecting are made by investigating the improvement of information security performance of wearable medical sensors, the improvement of biological adaptability and biodegradability of new materials, and the integration of wearable medical sensors and intelligence-assisted rehabilitation. The research expects to provide a reference for the intelligent development of wearable medical sensors and real-time monitoring of human health in the follow-up medical field.

Application of artificial intelligence in wearable devices: Opportunities and challenges

BACKGROUND AND OBJECTIVES

Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data.

METHODS

Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives.

RESULTS

Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays.

CONCLUSION

The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.

Mechanism of Dopaminergic Nerve Transmission in Different Doses of Morphine Addiction and Stress-Induced Depression

Depression not only threatens the health and quality of life of patients but also brings a huge mental and economic burden to the patients' families. This paper mainly studies the mechanism of dopaminergic neurotransmission in different doses of morphine addiction and stress-induced depression. In the experiment, 40 male SD rats were selected. The experiment established a rat model of chronic stress depression. The rats used in this model are all raised in a single cage, and there will be various stimuli every day for 21 days, but high-intensity continuous stimuli must be avoided, and the same stimuli will not appear continuously. The experiment established a depression animal model through chronic unpredictable mild stress (CUMS), combined with the conditioned position preference (CPP) model of morphine addiction to detect the establishment of CPP in such animals, so as to explore certain stress stimuli or depression, the influence on morphine addiction, and the relationship between them. The second or third branches of pyramidal neurons were selected to analyze the PL and CA3 regions. When analyzing the density of dendrites, each animal selected at least 8 dendrites in order to count the number of dendrites and selected a length of 20 μm on each branch to record the number of dendrites. All measured values are expressed as average ± standard deviation and analyzed by SPSS17.0 statistical software, and Levene test is used in the scattered consistency test. The average NIV of PEN before injection was 11.92 ± 2.90 Hz, and the average latency was 0.16 ± 0.03 s. The results indicate that CUMS may reduce the conditioned learning and memory ability by damaging the learning loop, rather than affecting the reward loop to weaken the establishment of morphine-dependent CPP.

Immersive virtual reality health games: a narrative review of game design

BACKGROUND

High quality head-mounted display based virtual reality (HMD-VR) has become widely available, spurring greater development of HMD-VR health games. As a behavior change approach, these applications use HMD-VR and game-based formats to support long-term engagement with therapeutic interventions. While the bulk of research to date has primarily focused on the therapeutic efficacy of particular HMD-VR health games, how developers and researchers incorporate best-practices in game design to achieve engaging experiences remains underexplored. This paper presents the findings of a narrative review exploring the trends and future directions of game design for HMD-VR health games.

METHODS

We searched the literature on the intersection between HMD-VR, games, and health in databases including MEDLINE, Embase, CINAHL, PsycINFO, and Compendex. We identified articles describing HMD-VR games designed specifically as health applications from 2015 onwards in English. HMD-VR health games were charted and tabulated according to technology, health context, outcomes, and user engagement in game design.

FINDINGS

We identified 29 HMD-VR health games from 2015 to 2020, with the majority addressing health contexts related to physical exercise, motor rehabilitation, and pain. These games typically involved obstacle-based challenges and extrinsic reward systems to engage clients in interventions related to physical functioning and pain. Less common were games emphasizing narrative experiences and non-physical exercise interventions. However, discourse regarding game design was diverse and often lacked sufficient detail. Game experience was evaluated using primarily ad-hoc questionnaires. User engagement in the development of HMD-VR health games primarily manifested as user studies.

CONCLUSION

HMD-VR health games are promising tools for engaging clients in highly immersive experiences designed to address diverse health contexts. However, more in-depth and structured attention to how HMD-VR health games are designed as game experiences is needed. Future development of HMD-VR health games may also benefit from greater involvement of end-users in participatory approaches.

The development of a mobile user interface ability evaluation system for the elderly

This research aimed to develop a comprehensive evaluation of the mobile user interface abilities of the elderly so that technology can be designed to meet individualized needs. A total of 135 older adults were evaluated with the developed system, the Elderly Mobile User Interface Ability Evaluation System (EMUIAES). The prediction of age and the use of technology on elderly mobile interface usage were investigated based on the findings of the evaluation. The relationship between performance on Fitts' task and elderly mobile user interface ability (EMUIA) was also examined. The findings showed a strong effect of age on the elderly's use of mobile user interfaces. Previous experience with personal and tablet computers also contributed to the use of mobile user interfaces. In addition, this research demonstrated the application of Fitts' law to describe the elderly mobile user interface behaviors, particularly for tasks involving fast tapping and pointing. The EMUIAES can provide future researchers and designers a comprehensive tool to describe the elderly's diverse behaviors and changes in their ability to use mobile interfaces. Individualized interface designs for elderly users can be developed based on these findings to improve the elderly users' experiences of using technology.

Interaction Modalities Used on Serious Games for Upper Limb Rehabilitation: A Systematic Review

This systematic review aims to analyze the state-of-the-art regarding interaction modalities used on serious games for upper limb rehabilitation. A systematic search was performed in IEEE Xplore and Web of Science databases. PRISMA and QualSyst protocols were used to filter and assess the articles. Articles must meet the following inclusion criteria: they must be written in English; be at least four pages in length; use or develop serious games; focus on upper limb rehabilitation; and be published between 2007 and 2017. Of 121 articles initially retrieved, 33 articles met the inclusion criteria. Three interaction modalities were found: vision systems (42.4%), complementary vision systems (30.3%), and no-vision systems (27.2%). Vision systems and no-vision systems obtained a similar mean QualSyst (86%) followed by complementary vision systems (85.7%). Almost half of the studies used vision systems as the interaction modality (42.4%) and used the Kinect sensor to collect the body movements (48.48%). The shoulder was the most treated body part in the studies (19%). A key limitation of vision systems and complementary vision systems is that their device performances might be affected by lighting conditions. A main limitation of the no-vision systems is that the range-of-motion in angles of the body movement might not be measured accurately. Due to a limited number of studies, fruitful areas for further research could be the following: serious games focused on finger rehabilitation and trauma injuries, game difficulty adaptation based on user's muscle strength and posture, and multisensor data fusion on interaction modalities.

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved.

Visual saliency models for summarization of diagnostic hysteroscopy videos in healthcare systems

In clinical practice, diagnostic hysteroscopy (DH) videos are recorded in full which are stored in long-term video libraries for later inspection of previous diagnosis, research and training, and as an evidence for patients' complaints. However, a limited number of frames are required for actual diagnosis, which can be extracted using video summarization (VS). Unfortunately, the general-purpose VS methods are not much effective for DH videos due to their significant level of similarity in terms of color and texture, unedited contents, and lack of shot boundaries. Therefore, in this paper, we investigate visual saliency models for effective abstraction of DH videos by extracting the diagnostically important frames. The objective of this study is to analyze the performance of various visual saliency models with consideration of domain knowledge and nominate the best saliency model for DH video summarization in healthcare systems. Our experimental results indicate that a hybrid saliency model, comprising of motion, contrast, texture, and curvature saliency, is the more suitable saliency model for summarization of DH videos in terms of extracted keyframes and accuracy.