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This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN. [ABSTRACT FROM AUTHOR]

In this study, the contact image photoplethysmography (iPPG) technique was used through a smartphone video camera, and its usefulness was explored under baseline conditions, stress induced by Stroop test and recovery, taking as reference the heart rate variability (HRV) extracted from the electrocardiography (ECG) in two conditions: 1) spontaneous breathing, and 2) controlled breathing at a fixed rate of 6 breaths per minute. Thanks to the use of smartphones, the measurements were made in the homes of the volunteers, who were provided with the measurement systems. Linear temporal and spectral, as well as nonlinear indexes (Poincaré plot and binary symbolic dynamics) were explored for HRV and pulse rate variability (PRV). Similar results were found for ECG-based HRV and iPPG-based PRV, corroborating the usefulness of iPPG via smartphones in HRV studies, providing an interesting alternative to perform HRV analysis outside research and clinical settings.Clinical Relevance- This study shows the use of a smartphone to extract iPPG-based PRV time series and their linear and nonlinear indexes as a surrogate for ECG-based HRV during stress and a controlled breathing maneuver.

The Biomedical Engineering (BME) bachelor pro-gram of the Faculty of Sciences in Universidad Autónoma de San Luis Potosí (UASLP) was created in June of 2010, with the aim of training professionals with an integral perspective in the engineering field by considering a multidisciplinary approach to develop and apply technology in the areas of medicine and biology. After 10 years, our BME program has achieved national recognition. Despite of being an emerging program, this achievement has been obtained by the consolidation of our academic staff, the outstanding participation of our students in national and international academic events, and the historical graduation results. In our comprehensive evaluation, we report an overall terminal efficiency (completion rate) of 67% and a graduation rate of 47.2%, where these values are above the average for an engineering program in our institution. Additionally, the BME program provides students with solid skills and background to carry out research activities, which has resulted in a considerable number of alumni pursuing graduate studies or have already completed one. Our results show that 90% of our former students are working after graduation, but only 44% work in the field of biomedical engineering, since the regional labor market starts to saturate given the fact that, at present, students from six generations have completed our BME bachelor program. In this way, few graduates visualize the wide spectrum of job options where a biomedical engineer can impact, by their distinctive comprehensive and multidisciplinary training. Therefore, it is necessary to propose new curricular design strategies to provide our students with an academic training that allows them to enter a globalized world, where there is an even greater spectrum of engineering possibilities related to the fields of medicine and biology, in line with current trends.

Heart rate variability (HRV) has been extensively investigated as a noninvasive marker to evaluate the functionality of the autonomic nervous system (ANS). Many studies have provided photoplethysmography (PPG) as a surrogate for electrocardiogram (ECG) signal HRV measurements. Remote PPG (rPPG) has been also investigated for pulse rate variability (PRV) estimation but in controlled conditions. We remotely extracted PRV using a smartphone camera for subjects in static and lateral motion while their respiratory rate was set to three breathing rates in an indoor illumination environment. PRV was compared with ECG-based HRV as a gold standard. We tested our algorithms on five healthy subjects. The results showed high correlation for rPPG-based HRV by presenting means of standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive heartbeat interval differences (RMSSD) correlation coefficient greater than 0.95 in rest and greater than 0.87 in motion. The error of mean low frequency over high frequency (LF/HF) ratio estimated from PRV was 0.13 in rest and 0.25 in lateral motion. Moreover, a statistically significant correlation was obtained between HRV and PRV power spectra and temporal signals for all performed tasks. The obtained results contributed to confirm that remote imaging measurement of cardiac parameters is a promising, convenient, and low-cost alternative to specialized biomedical sensors in a diversity of relevant experimental maneuver.

Heart rate (HR) and respiratory rate (RR) are very important physiological variables useful to evaluate the cardiorespiratory system. At present, there is a great interest by the general population in knowing their health status, quickly and easily. Accordingly, several approaches have been proposed to achieve that goal. In this study, the simultaneous estimation of the instantaneous HR and RR values was achieved by the image photoplethysmography (iPPG) technique, in the contact mode directly implemented in a smartphone. iPPG results were compared with those obtained using specialized biomedical sensors such as the electrocardiogram and the respiratory effort band. Performance evaluation included three different respiratory maneuvers in five healthy volunteers. The absolute mean error for instantaneous HR and RR estimations reached 0.94 ± 0.28 beats per minute and 0.40 ± 0.11 breaths per minute, respectively. The mean correlation index was 0.69 ± 0.14 between the iPPG-derived respiratory signal and the respiratory effort reference signal.Clinical Relevance- These results appear to indicate that the contact iPPG method implemented directly on the smartphone is a good option, accessible to the common population to estimate the instantaneous HR and RR values outside specialized clinical environments, e.g., in the point-of-contact office.

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.

Underwater electrocardiogram (ECG) monitoring currently uses Ag/AgCl electrodes and requires sealing of the electrodes to avoid water intrusion, but this procedure is time consuming and often results in severe irritations or even tearing of the skin. To alleviate these problems, our research team developed hydrophobic electrodes comprised of a mixture of carbon black powder (CB) and polydimethylsiloxane (PDMS) that provide all morphological waveforms without distortion of an ECG signal for dry and water-immersed conditions. Performance comparison of CB/PDMS electrodes to adhesive Ag/AgCl hydrogel electrodes was carried out in three different scenarios which included recordings from a dry surface, water immersion, and post-water immersion conditions. CB/PDMS electrodes were able to acquire ECG signals highly correlated with those from adhesive Ag/AgCl electrodes during all conditions. Statistical reduction in ECG amplitude (p<0.05) was only found during the immersed condition with CB/PDMS electrodes when compared to Ag/AgCl electrodes sealed with their waterproof adhesive tape. Besides this reduction readability of the recordings was not obscured and all morphological waveforms of the ECG signal were discernible. The advantages of our CB/PDMS electrodes are that they are reusable, can be fabricated economically, and most importantly, high-fidelity underwater ECG signals can be acquired without relying on the heavy use of waterproof sealing.

We hypothesized that an iPhone 4s can be used to detect atrial fibrillation (AF) based on its ability to record a pulsatile photoplethysmogram (PPG) signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4s for AF detection, 25 prospective subjects with AF pre- and post-electrical cardioversion were recruited. Using an iPhone 4s, we collected 2-minute pulsatile time series. We investigated 3 statistical methods consisting of the Root Mean Square of Successive Differences (RMSSD), the Shannon entropy (ShE) and the Sample entropy (SampE), which have been shown to be useful tools for AF assessment. The beat-to-beat accuracy for RMSSD, ShE and SampE was found to be 0.9844, 0.8494 and 0.9552, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF or normal sinus rhythm (NSR) in the data. Using this criterion, we achieved an accuracy of 100% for both detecting the presence of either AF or NSR.