A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> A. HEALTHCARE <s> This paper describes the development and evaluation of wearable sensor earphones coupled with a mobile platform for comfortable assessment of cardiovascular function. Photoplethysmographic sensors were integrated into regular earphones to provide discreet and non-stigmatizing heart rate measurements. Preliminary results indicate that the proposed system has a high degree of accuracy at rest with an average error of 0.63%. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> A. HEALTHCARE <s> Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring. The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression. To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie's wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification. Extensive experimental work is conducted, utilizing four classification models. The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value. The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application's accuracy. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> A. HEALTHCARE <s> Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when dB, 84% when dB, and 88% when dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> A. HEALTHCARE <s> Tinnitus, the phantom sensation of sound, is a highly prevalent disorder that is difficult to treat, i.e., Available treatments are only effective for patient subgroups. Sufficiently large and qualitative longitudinal data sets, which aggregate the individuals' demographic and clinical characteristics, together with their response to specific therapeutic interventions, would therefore facilitate evidence-based treatment suggestions for individual patients. Currently, clinical trials are the standard instrument for realizing evidence-based medicine. However, the related information gathering is limited. For example, clinical trials try to reduce the complexity of the individual case by generating homogeneous groups to obtain significant results. From the latter, individual treatment decisions are inferred. A complementary approach would be to assess the effect of specific interventions in large samples considering the individual peculiarity of each subject. This allows providing individualized treatment decisions. Recently, mobile crowd sensing emerged as an approach for collecting large and ecological valid datasets at rather low costs. By providing mobile crowd sensing services to large numbers of patients, large datasets can be gathered cheaply on a daily basis. In the Track Your Tinnitus project, we implemented a mobile crowd sensing platform to reveal new medical aspects on tinnitus and its treatment. Additionally, we work on mobile services exploring approaches for understanding tinnitus and for improving its diagnostic and therapeutic management. We present the Track Your Tinnitus platform as well as its goals, architecture and preliminary results. Overall, the platform and its mobile services offer promising perspectives for tinnitus research and treatment. <s> BIB004
Healthcare monitoring uses sensors to monitor patient vital signs both locally and remotely. It offers enhanced patient care through early detection of adverse health conditions. It can influence patients' behavior to improve their health  . For a wide range of patient conditions, biosensors offer point-of-care monitoring. These sensors read some specific measurements such as heart rate, blood pressure and body temperature; and monitor certain medical conditions such as diabetes or seizures. The medical data collected from biosensors of the patient is forwarded to medical facilities or specialists for analysis and diagnosis Smartphone manufacturers are increasingly interested in healthcare applications, which led to the integration of more sensors in mobile phones. These devices are widely accessible and continuously connected to the network. They also have processing capabilities that exceed all of the traditional sensors used in WSNs. The combination of the powerful processing and connectivity of smartphones offers low-cost access to health services from an increasing range of healthcare applications BIB001 ,  , BIB002 , BIB003 . According to mobile research specialists (research2 guidance), smartphone applications for the mobile health industry are successfully installed and have reached 500 million of the total 1.4 billion smartphone users in 2015. Presently, there are more than 17,000 mHealth applications in major app stores  . Research in the domain of remote health monitoring over the past few years can be categorized into three main streams: how data is collected, how data is communicated, and where is the data processing performed. Foremski et al.  the research focuses on the role of mobile phone devices and their related technologies to monitor the patients. This role was limited to a mobile client terminal used to browse healthcare records. However, with the extensive availability of smartphones a range of new smartphone-based medical applications became available. These tools provide the user as well as the care provider with essential tools that help monitoring and diagnosing certain health situations that requires continuous care. In the aforementioned scenarios all the discussed healthcare applications are personalized rather than belonging to MCS. However, we envision that MCS can serve the healthcare sector from the point of view of medical practitioners' clinical research and treatment assessment statistics, which is a major pillar of medical advancement. An example of using MCS in medical clinical research is the TrackYourTinnitus project BIB004 , where MCS has been used with data anonymization to provide datasets on large numbers of patients on daily basis with low cost enabling ubiquitous clinical trials research. The same model can be applied to clinical trials related to any of the various smartphone-based sensing applications discussed earlier in this Subsection. Thus, MCS would allow the transfer of the collected personal mHealth information to a centralized cloud, after appropriate anonymization (as discussed in Section V.B). Then, big data, cloud computing, and data analytics could be used on a collective level to study the population's health status, gather information about disease proliferation, and take appropriate measures.