Mobile Health System Research Articles

Comments on “Light-Weight and Robust Security-Aware D2D-Assist Data Transmission Protocol for Mobile-Health Systems”

The mobile-health (M-Health) system, also known as the wireless body area network for remote patient monitoring, is a system which can remotely monitor a real-time human body’s health status parameters. Generalized signcryption can realize encryption, signature, and signcryption with only one key pair and one algorithm. To address the communication security requirement for M-Health system, Zhang et al. proposed a light-weight secure data transmission protocol for M-Health system by using a certificateless generalized signcryption (CLGSC) scheme recently. In this paper, we point out that an insider attack exists in Zhang et al. ’s CLGSC scheme, which destroys the confidentiality of the scheme. Thus, their light-weight and robust security-aware D2D-assist data transmission protocol for mobile-health system is insecure, too.

Congestive Heart Failure Risk Assessment Monitoring through Internet of things and mobile Personal Health Systems.

Congestive heart failure (CHF) occurs when the heart cannot provide the necessary cardiac output for the metabolic needs of the human body. The most prominent symptoms are increased venous pressure, abnormal heart and breathing rate, tiredness and leg swelling. Most important pathogenesis influence are: age, gender, high blood pressure, alcohol and smoking, sedentary lifestyle and diet, genetic predisposition and family history, diabetes, and atherosclerosis. Common causes are considered to be valvular heart disease, coronary heart disease and hypertension. CHF diagnosis can be achieved through physical examination (i.e., blood pressure, body mass index, blood tests) and echocardiography. In this work, we present a smart mobile application and internet of things capable of the early detection and real-time monitoring of CHF exacerbations, enforcing prevention on a daily basis. We refer to the architectural elements of our approach accounting for the integration of a secure access scheme, following GDPR regulation, as a novel biometric solution to increase security and access. A first validation of the system is also presented.

Foreword Embedded Sensor Systems

Embedded sensor systems are gaining a role of importance in the research community because a large number of embedded computers are deeply integrated into our lives, cars, houses, and work environments. They support us in decisions, analyze and process physical quantities surrounding us in order to simplify our lives. Embedded sensor systems control our health status, allow us to prevent diseases, or help a physician to monitor us while we are at home or at work. At the same time, they prevent accidents and control our level of fatigue or attention. Embedded sensor systems may also bring significant improvements in the industrial field and factory automation. It is not futuristic to think about a world where manufacturing robots and other industrial automation systems meaningfully interact with the objects they are assembling, or a factory in which they are able to move with deftly and safety, because they know the surrounding environments well. Embedded sensor systems can allow working machines to be able to “see,” “hear,” and thus understand their surroundings. They can improve our safety, life, health, and industrial production. It clearly seems that the smart factories, such as the intelligent houses, pro-active smartphones, and wearable mobile health systems, are no longer science fiction. These applications lay the foundations for greater challenges on smart sensors and embedded sensor systems. These challenges need innovative solutions, and this Special Section realizes a forum for designers, researchers, and major industrial personalities, in which to present their latest developments in the embedded sensor systems.

Detection of Respiratory Crackle Sounds via an Android Smartphone-based System.

Pulmonary auscultation with traditional stethoscope, although useful, has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. In this work, we present the development of a mobile health system for the automated detection of crackle sounds, comprised by an acoustical sensor, a smart phone device, and a mobile application (app) implemented in Android. The app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smart phone. The algorithm for crackle detection was based on a time-varying autoregressive modeling. Performance of the automated detector was analyzed using synthetic fine and coarse crackle sounds randomly added to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios. Accuracy and sensitivity were found to range from 90.7% to 94.0% and from 91.2% to 94.2%, respectively. Application of the proposed mobile system to real acquired data from a patient with pulmonary fibrosis is also exemplified.

M-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare delivery systems. In recent years, big data has become increasingly synonymous with mobile health, however key challenges of 'Big Data and mobile health', remain largely untackled. This is becoming particularly important with the continued deluge of the structured and unstructured data sets generated on daily basis from the proliferation of mobile health applications within different healthcare systems and products globally. The aim of this paper is of twofold. First we present the relevant big data issues from the mobile health (m-Health) perspective. In particular we discuss these issues from the technological areas and building blocks (communications, sensors and computing) of mobile health and the newly defined (m-Health 2.0) concept. The second objective is to present the relevant rapprochement issues of big m-Health data analytics with m-Health. Further, we also present the current and future roles of machine and deep learning within the current smart phone centric m-health model. The critical balance between these two important areas will depend on how different stakeholder from patients, clinicians, healthcare providers, medical and m-health market businesses and regulators will perceive these developments. These new perspectives are essential for better understanding the fine balance between the new insights of how intelligent and connected the future mobile health systems will look like and the inherent risks and clinical complexities associated with the big data sets and analytical tools used in these systems. These topics will be subject for extensive work and investigations in the foreseeable future for the areas of data analytics, computational and artificial intelligence methods applied for mobile health.

Personalized method for self-management of trunk postural ergonomic hazards in construction rebar ironwork

Construction rebar workers face postural ergonomic hazards that can lead to work-related Lower Back Disorders (LBDs), primarily due to their prolonged awkward working postures required by the job. In a previous study, Wearable Inertial Measurement Units (WIMUs)-based Personal Protective Equipment (PPE) was developed to alert workers when their trunk inclination holding time exceeded acceptable thresholds as defined in ISO standard 11226:2000. However, subsequent field testing identified PPE was ineffective for some workers because the adopted ISO thresholds were not personalized and did not consider differences in individual’s response to postural ergonomic hazards. To address this problem, this paper introduces a worker-centric method to assist in the self-management of work-related ergonomic hazards, based on data-driven personalized healthcare intervention. Firstly, personalized information is gathered by providing each rebar ironworker a WIMU-based personalized mobile health (mHealth) system to capture their trunk inclination angle and holding time data. Then, the captured individual trunk inclination holding times are analyzed by a Gaussian-like probability density function, where abnormal holding time thresholds can be generated and updated in response to incoming trunk inclination records of an individual during work time. These abnormal holding time thresholds are then adapted to be used as personalized trunk inclination holding time recommendations for an individual worker to self-manage their working postures, based on their own trunk inclination records. The proposed worker-centric method to assist in the self-management of ergonomic postural hazards leading to LBDs was field tested on a construction site over a three-month duration. The results of the paired t-tests indicate that posture scores evaluated by the Ovako Working Posture Analysis System (OWAS) significantly decrease when the personalized recommendation is applied, while increase again when the personalized recommendation is removed. Based on data-driven personalized healthcare intervention, the results demonstrate the significant potential of the proposed worker-centric self-management method for rebar workers in preventing and controlling postural ergonomic hazards during construction rebar ironwork.

Accurate and fast neonatal heart rate assessment with a smartphone-based application - a manikin study.

AimThis study determined the accuracy and speed of the NeoTapLifeSupport (NeoTapLS), a free smartphone application that aims to assess a neonate's heart rate.MethodsWe asked 30 participants with a variety of backgrounds to test the NeoTapLS, which was developed by our own nonprofit organisation Tap4Life, to determine a randomly selected heart rate by auscultation or palpation. The study was carried out in 2014 at Sachs’ Children and Youth Hospital, Sweden, using a Laerdal SimNewB manikin that simulates true values. The NeoTapLS calculates the heart rate based on the user's last three taps on the smartphone screen.ResultsA total of 1200 measurements were carried out. A high correlation was found between measured and true values by auscultation (correlation coefficient 0.993) as well as by palpation (correlation coefficient 0.986) with 93.5% of the auscultations and 86.3% of the palpations differing from the true value by five beats or fewer. The mean time to the first estimated heart rate was 14.9 seconds for auscultation and 16.3 seconds for palpation.ConclusionHeart rates could be accurately and rapidly assessed using the NeoTapLS on a manikin. A globally accessible mobile health system could offer a low‐cost alternative to expensive medical equipment.

Usability testing of an mHealth device for swallowing therapy in head and neck cancer survivors.

The objective of this study was to conduct the first patient usability testing of a mobile health (mHealth) system for in-home swallowing therapy. Five participants with a history of head and neck cancer evaluated the mHealth system. After completing an in-application (app) tutorial with the clinician, participants were asked to independently complete five tasks: pair the device to the smartphone, place the device correctly, exercise, interpret progress displays, and close the system. Quantitative and qualitative methods were used to evaluate the effectiveness, efficiency, and satisfaction with the system. Critical changes to the app were found in three of the tasks, resulting in recommendations for the next iteration. These issues were related to ease of Bluetooth pairing, placement of device, and interpretation of statistics. Usability testing with patients identified issues that were essential to address prior to implementing the mHealth system in subsequent clinical trials. Of the usability methods used, video observation (synced screen capture with videoed gestures) revealed the most information.

Towards a cooperative security system for mobile-health applications

Mobile health (m-Health) system architectures are typically based on mobile and wireless communications, and use mobile devices with data exchange supported by Web services (WS). Although m-Health systems offer mobility as a potential and precious resource they also present several challenged issues and constraints, such as, battery and storage capacity, broadcast constraints, interferences, disconnections, noises, limited bandwidths, and network delays. Furthermore, constant mobility and often-required Internet connectivity also exposes and compromises the privacy and confidentiality of the m-Health system information. This paper proposes a novel data encryption solution for mobile health systems, considering a novel and early-proposed cooperation strategy. This encryption solution, tries to guarantee the best confidentiality, integrity, and authenticity of m-Health systems users data. The paper also presents a performance evaluation study comparing the performance an m-Health application with and without the DE4MHA.

Mobile App for Simplifying Life With Diabetes: Technical Description and Usability Study of GlucoMan.

BackgroundPatients with diabetes can be affected by several comorbidities that require immediate action when occurring as they may otherwise cause fatal or consequential damage. For this reason, patients must closely monitor their metabolism and inject insulin when necessary. The documentation of glucose values and other relevant measurements is often still on paper in a diabetes diary.ObjectiveThe goal of this work is to develop and implement a novel mobile health system for the secure collection of relevant data referring to a person’s metabolis and to digitize the diabetes diary to enable continuous monitoring for both patients and treating physicians. One specific subgoal is to enable data transmission of health parameters to secure data storage.MethodsThe process of implementing the system consists of (1) requirements analysis with patients and physicians to identify patient needs and specify relevant functionalities, (2) design and development of the app and the data transmission, and (3) usability study.ResultsWe developed and implemented the mobile app GlucoMan to support data collection pertaining to a person’s metabolism. An automated transfer of measured values from a glucometer was implemented. Medication and nutrition data could be entered using product barcodes. Relevant background knowledge such as information on carbohydrates was collected from existing databases. The recorded data was transmitted using international interoperability standards to the MIDATA.coop storage platform. The usability study revealed some design issues that needs to be solved, but in principle, the study results show that the app is easy to use and provides useful features.ConclusionsData collection on a patient’s metabolism can be supported with a multifunctional app such as GlucoMan. Besides monitoring, continuous data can be documented and made available to the treating physician. GlucoMan allows patients to monitor disease-relevant parameters and decide who accesses their health data. In this way, patients are empowered not only to manage diabetes but also manage their health data.

Model for Personalization of Mobile Health System for Monitoring Patients with Chronic Diseases

Model for Personalization of Mobile Health System for Monitoring Patients with Chronic Diseases

Implementing a Mobile Health System to Integrate the Treatment of Addiction Into Primary Care: A Hybrid Implementation-Effectiveness Study.

BackgroundDespite the near ubiquity of mobile phones, little research has been conducted on the implementation of mobile health (mHealth) apps to treat patients in primary care. Although primary care clinicians routinely treat chronic conditions such as asthma and diabetes, they rarely treat addiction, a common chronic condition. Instead, addiction is most often treated in the US health care system, if it is treated at all, in a separate behavioral health system. mHealth could help integrate addiction treatment in primary care.ObjectiveThe objective of this paper was to report the effects of implementing an mHealth system for addiction in primary care on both patients and clinicians.MethodsIn this implementation research trial, an evidence-based mHealth system named Seva was introduced sequentially over 36 months to a maximum of 100 patients with substance use disorders (SUDs) in each of three federally qualified health centers (FQHCs; primary care clinics that serve patients regardless of their ability to pay). This paper reports on patient and clinician outcomes organized according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.ResultsThe outcomes according to the RE-AIM framework are as follows: Reach—Seva reached 8.31% (268/3226) of appropriate patients. Reach was limited by our ability to pay for phones and data plans for a maximum of 100 patients per clinic. Effectiveness—Patients who were given Seva had significant improvements in their risky drinking days (44% reduction, (0.7-1.25)/1.25, P=.04), illicit drug-use days (34% reduction, (2.14-3.22)/3.22, P=.01), quality of life, human immunodeficiency virus screening rates, and number of hospitalizations. Through Seva, patients also provided peer support to one another in ways that are novel in primary care settings. Adoption—Patients sustained high levels of Seva use—between 53% and 60% of the patients at the 3 sites accessed Seva during the last week of the 12-month implementation period. Among clinicians, use of the technology was less robust than use by patients, with only a handful of clinicians using Seva in each clinic and behavioral health providers making most referrals to Seva in 2 of the 3 clinics. Implementation—At 2 sites, implementation plans were realized successfully; they were delayed in the third. Maintenance—Use of Seva dropped when grant funding stopped paying for the mobile phones and data plans. Two of the 3 clinics wanted to maintain the use of Seva, but they struggled to find funding to support this.ConclusionsImplementing an mHealth system can improve care among primary care patients with SUDs, and patients using the system can support one another in their recovery. Among clinicians, however, implementation requires figuring out how information from the mHealth system will be used and making mHealth data available in the electronic health (eHealth) record. In addition, paying for an mHealth system remains a challenge.

IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning.

Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles. Existing approaches for I2S assignment usually rely on hand crafted features and shallow classification approaches (e.g., support vector machines), with no agreement regarding the most suitable features for the assignment task. Moreover, estimating the complete orientation alignment of an IMU relative to the segment it is attached to using a machine learning approach has not been shown in literature so far. This is likely due to the high amount of training data that have to be recorded to suitably represent possible IMU alignment variations. In this work, we propose online approaches for solving the assignment and alignment tasks for an arbitrary amount of IMUs with respect to a biomechanical lower body model using a deep learning architecture and windows of 128 gyroscope and accelerometer data samples. For this, we combine convolutional neural networks (CNNs) for local filter learning with long-short-term memory (LSTM) recurrent networks as well as generalized recurrent units (GRUs) for learning time dynamic features. The assignment task is casted as a classification problem, while the alignment task is casted as a regression problem. In this framework, we demonstrate the feasibility of augmenting a limited amount of real IMU training data with simulated alignment variations and IMU data for improving the recognition/estimation accuracies. With the proposed approaches and final models we achieved 98.57% average accuracy over all segments for the I2S assignment task (100% when excluding left/right switches) and an average median angle error over all segments and axes of ° for the I2S alignment task.

The Feasibility and Usability of RunningCoach: A Remote Coaching System for Long-Distance Runners.

Studies have shown that about half of the injuries sustained during long-distance running involve the knee. Cadence (steps per minute) has been identified as a factor that is strongly associated with these running-related injuries, making it a worthwhile candidate for further study. As such, it is critical for long-distance runners to minimize their risk of injury by running at an appropriate running cadence. In this paper, we present the results of a study on the feasibility and usability of RunningCoach, a mobile health (mHealth) system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence.

Voice Disorder Identification by Using Machine Learning Techniques

Nowadays, the use of mobile devices in the healthcare sector is increasing significantly. Mobile technologies offer not only forms of communication for multimedia content (e.g. clinical audio-visual notes and medical records) but also promising solutions for people who desire the detection, monitoring, and treatment of their health conditions anywhere and at any time. Mobile health systems can contribute to make patient care faster, better, and cheaper. Several pathological conditions can benefit from the use of mobile technologies. In this paper we focus on dysphonia, an alteration of the voice quality that affects about one person in three at least once in his/her lifetime. Voice disorders are rapidly spreading, although they are often underestimated. Mobile health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is necessary to realize a valid and precise mobile health system. The key contribution of this paper is to investigate and compare the performance of several machine learning techniques useful for voice pathology detection. All analyses are performed on a dataset of voices selected from the Saarbruecken voice database. The results obtained are evaluated in terms of accuracy, sensitivity, specificity, and receiver operating characteristic area. They show that the best accuracy in voice diseases detection is achieved by the support vector machine algorithm or the decision tree one, depending on the features evaluated by using opportune feature selection methods.

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors, such as environmental monitoring, mobile-health systems, intelligent transportation systems, and so on. The information and communication technology sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets, and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing, and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS requires cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that are continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access, and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

Analysis and development of mobile perinatal mental health system

Perinatal depression includes major and minor depressive episodes during pregnancy or in the first 12 months after birth. The American College of Obstetricians and Gynecologists recommends that clinicians screen patients at least once during pregnancy for depression and anxiety symptoms using a standardised tool. This paper attempts to consolidate current depression screening tools into an electronic application that can be accessed from a computer, tablet or cell phone and can be sent directly to the primary care physician's office for review. The object-oriented approach was used to analysis and design the system. The systems requirements are first gathered using questionnaire and interviews with practitioners; then, analysed based on unified modelling language techniques such as use case, class, and sequence diagrams. This system will aid healthcare providers in identifying patients who are at risk of perinatal depression.

Analysis and development of mobile perinatal mental health system

Perinatal depression includes major and minor depressive episodes during pregnancy or in the first 12 months after birth. The American College of Obstetricians and Gynecologists recommends that clinicians screen patients at least once during pregnancy for depression and anxiety symptoms using a standardised tool. This paper attempts to consolidate current depression screening tools into an electronic application that can be accessed from a computer, tablet or cell phone and can be sent directly to the primary care physician's office for review. The object-oriented approach was used to analysis and design the system. The systems requirements are first gathered using questionnaire and interviews with practitioners; then, analysed based on unified modelling language techniques such as use case, class, and sequence diagrams. This system will aid healthcare providers in identifying patients who are at risk of perinatal depression.

Proof-of-Concept Testing of a Real-Time mHealth Measure to Estimate Postural Control During Walking: A Potential Application for Mild Traumatic Brain Injuries.

Background: Most individuals with mild traumatic brain injury (mTBI) experience post-injury deficits in postural control. Currently available measures of postural control are lab-based or supervised, which may hinder timely symptom assessment for individuals with mTBI, including Asian populations, who do not seek initial screening post-injury. In this proof-of-concept testing study, we introduce a real-time mobile health (mHealth) system to measure postural control during walking. The proposed mHealth system can be used for home-based symptom assessment and management of mTBI.Methods: In our proposed mHealth system, a smartwatch, a smartphone, and a cloud server communicate to measure, collect, and store body balance data in real time. Specifically, we focus on the rotation vector data that have been reported to be the most effective in terms of differentiating balance control during walking across different participants.Results: Constant motion change in four participants (two females and two males; three healthy participants, and one individual with reduced physical mobility) was collected and analyzed. The results of our data analysis show that, compared to healthy participants, the individual was reduced physical mobility had a wider range of motion between right and left, up and down, and forward and backward while walking. We also found that female participants had narrower ranges of right-to-left and up-and-down motions than their male counterparts.Conclusions: Our results highlight the potential of the proposed real-time mHealth system for home-based symptom assessment and management of mTBI, which may benefit Asian and other nonwhite racial minority groups that appear to be more reluctant to access post-acute rehabilitation services.

A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems

Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.