Johanssonramirez7354

The consumption of tobacco has reached global epidemic proportions and is characterized as the leading cause of death and illness. Among the different ways of consuming tobacco (e.g., smokeless, cigars), smoking cigarettes is the most widespread. In this paper, we present a two-step, bottom-up algorithm towards the automatic and objective monitoring of cigarette-based, smoking behavior during the day, using the 3D acceleration and orientation velocity measurements from a commercial smartwatch. In the first step, our algorithm performs the detection of individual smoking gestures (i.e., puffs) using an artificial neural network with both convolutional and recurrent layers. In the second step, we make use of the detected puff density to achieve the temporal localization of smoking sessions that occur throughout the day. In the experimental section we provide extended evaluation regarding each step of the proposed algorithm, using our publicly-available, realistic Smoking Event Detection (SED) and Free-living Smoking Event Detection (SED-FL) datasets recorded under semi-controlled and free-living conditions, respectively. In particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of 0.863 for the detection of puffs and an F1-score/Jaccard index equal to 0.878/0.604 towards the temporal localization of smoking sessions during the day. Finally, to gain further insight, we also compare the puff detection part of our algorithm with a similar approach found in the recent literature.Operating at low sweat rates, such as those experienced by humans at rest, is still an unmet need for state-of-the-art wearable sweat harvesting and testing devices for lactate. Here, we report the on-skin performance of a non-invasive wearable sweat sampling patch that can harvest sweat at rest, during exercise, and post-exercise. The patch simultaneously uses osmosis and evaporation for long-term (several hours) sampling of sweat. Osmotic sweat withdrawal is achieved by skin-interfacing a hydrogel containing a concentrated solute. The gel interfaces with a paper strip that transports the fluid via wicking and evaporation. Proof of concept results show that the patch was able to sample sweat during resting and post-exercise conditions, where the lactate concentration was successfully quantified. The patch detected the increase in sweat lactate levels during medium level exercise. Blood lactate remained invariant with exercise as expected. We also developed a continuous sensing version of the patch by including enzymatic electrochemical sensors. Such a battery-free, passive, wearable sweat sampling patch can potentially provide useful information about the human metabolic activity.Homes equipped with ambient sensors can measure physiological signals correlated with the resident's health without requiring a wearable device. Gait characteristics may reveal physical imbalances or recognize changes in cognitive health. In this paper, we use the physical interactions with floor to both localize the resident and monitor their gait. Accelerometers are placed at the corners of the room for sensing. Gradient boosting regression was used to perform localization with an accuracy of 82%, reasonably accounting for inhomogeneity in the floor with just 3 sensors. A method using step time variance is proposed to detect gait imbalances; results on induced limps are presented.A single-lead electrocardiographic (ECG) sensor with 3D printed dry electrodes is developed and tested for short-term wireless ECG monitoring. In a first of its kind approach, a 3D printer and available cost-effective conductive plastics are used to manufacture dry electrodes that can detect an ECG when placed on the chest. The electrodes could be produced in less than 10 minutes and with minimal material resources. To demonstrate the utility of the newly developed sensor, 30-second, 1 and 5-minute recordings are captured and statistically analyzed using established Signal Quality Indices (SQIs) for consumer and medical-grade ECG applications. Heart rate (HR) algorithmic considerations for dry electrode ECG is also explored. The performance of the proposed dry electrode ECG is reliable for HR estimations similar to wet-electrode ECG measurements. The obtained ECG signals demonstrated acceptable quality with Signal to Noise Ratios (SNRs) ranging around 13 dB and Kurtosis Signal Quality Index (kSQI) from approximately 18 to 21. Also, visually, the QRS complexes and T-wave features of an ECG were easily identifiable. These dry electrodes are feasible low-cost rapid manufacturing solutions for single-lead ECG monitoring that takes into consideration the added benefit of better patient comfortability, good quality ECG content and minimum cost for electrode development.Bipolar Disorder (BD), characterized by mood fluctuating between episodes of mood elevation and depression, is a leading cause of disability worldwide. Lithium continues to be prescribed as a first-line mood stabilizer for the management of BD. However, lithium has a very narrow therapeutic index and it is crucial to carefully monitor lithium plasma levels as concentrations greater than 1.2 mmol/L are potentially toxic and can be fatal. The current techniques of lithium monitoring are cumbersome and require frequent blood tests with the consequent discomfort which results in patients evading treatment. Dermal interstitial fluid (ISF), an underutilized information-rich biofluid, can be a proxy for direct blood sampling and allow lithium drug monitoring as its lithium concentration is proportional to the concentrations in blood. Therefore, in this study we seek to investigate the measurement of lithium therapeutic concentrations in artificial ISF. Our study employs a colorimetric method, based on the reaction between chromogenic agent Quinizarin and Li+ ion which can be detected using optical spectroscopy in the visible region (400-800 nm), to determine lithium levels in artificial ISF. The resulting spectra of our experiments show spectral variations which are related to lithium concentrations in spiked samples of artificial ISF, with a correlation coefficient (R) of 0.9. Future work will focus on investigating the feasibility of utilizing ISF for real-time and minimally-invasive lithium drug monitoring.The novel coronavirus disease (COVID-19), as a pandemic, has intensely impacted the global healthcare systems. Remote health monitoring of positive COVID-19 patients isolating at home has been identified as a practical approach to minimize the mortality rate. This work proposes a cost-effective and ease-to-use wristband with the capability of continuous real-time monitoring of heart rate (HR), respiration rate (RR), and blood oxygen saturation (SpO2), temperature and accelerometry. The proposed wristband comprises three different sensing elements, namely, PPG sensor, temperature sensor, and accelerometer. The sensors' output signals are transmitted via Bluetooth. Process of the PPG signals measured from the wrist anatomical position provides essential information regarding HR, RR, and SpO2. The deployed temperature sensor and accelerometer, measure the wearers' body temperature and physical activities. Experimental results obtained from a group of subjects demonstrate that the wristband can monitor HR, RR, SpO2, and body temperature with the Mean Absolute Errors (MAEs) of 2.75 bpm, 1.25 breaths/min, 0.64%, and 0.22 Co, respectively. Such a small variation confirms that the wristband can be potentially deployed in the public health network to determine and track patients infected by COVID-19.For hospitalized patients with pulmonary conditions, the onset of respiratory decline can occur unnoticed, due to the absence of a way to continuously and noninvasively monitor lung condition. Based on the relationship between lung volume and pleural pressure, we hypothesized that the time delay (∆t) between the start of a respiratory cycle and the occurrence of lung sounds associated with inspiration would correlate with lung volume. Additionally, we developed a re-search tool, consisting of a respiration belt, digital stethoscope, data collection system and MATLAB algorithm, to measure this delay. Leukadherin-1 mw We conducted a feasibility study with three healthy individuals that involved safely manipulating lung volume, through subject position and activity, and plotting ∆t against volume measurements obtained via spirometry. The results indicated that ∆t was measurable and changed with lung volume and, therefore, has the potential to serve as a lung condition monitoring tool.In this study, we proposed a framework for extracting gait events and extensive temporal features, seamlessly, during walking and running on a treadmill by constructing a finite state machine (FSM) transition rules based on two IMU sensors attached to the back of the shoes. Detailed innerclass states were defined to recognize the double support phase on walking gait and the double flight phase on running gait. Further, an in-depth speed-based analysis of temporal gait features can be performed for each tested speed with an automatic speed change detection algorithm based on the moving average filter applied to motion intensity data. The results have demonstrated that the FSM can accurately distinguish walking gait and running gait while also extract a detailed gait phase, respectively. This finding may contribute to a more flexible gait analysis where a change in speed or transition from walk to run can be anticipated and recognized accordingly.Back injuries and other occupational injuries are common in workers who engage in long, arduous physical labor. The risk of these injuries could be reduced using assistive devices that automatically detect an object lifting motion and support the user while they perform the lift; however, such devices must be able to detect the lifting motion as it occurs. We thus developed a system to detect the start and end of a lift (performed as a stoop or squat) in real time based on pelvic angle and the distance between the user's hands and the user's center of mass. The measurements were input to an algorithm that first searches for hand-center distance peaks in a sliding window, then checks the pelvic displacement angle to verify lift occurrence. The approach was tested with 5 participants, who performed a total of 100 lifts of four different types. The times of actual lifts were determined by manual video annotation. The median time error (absolute difference between detected and actual occurrence time) for lifts that were not false negatives was 0.11 s; a lift was considered a false negative if it was not detected within two seconds of it actually occurring. Furthermore, 95% of lifts that were detected occurred within 0.28 s of actual occurrence. This shows that it is possible to reliably detect lifts in real time based on the pelvic displacement angle and the distance between the user's hands and their center of mass.Sleep patterns often change during pregnancy and postpartum. However, if severe and persistent, these changes can depict a risk factor for significant health complications. It is thus essential to identify and understand changes in women's sleeping pattern over the course of pregnancy and postpartum, to offer an appropriate and timely intervention if necessary. In this paper, we discuss sleep disturbances during pregnancy and their association with pregnancy complications. We also review the state-of-the-art digital devices for real-time sleep assessment, and highlight their strengths and limitations.Clinical Relevance-This review highlights an importance of an individualized holistic pregnancy care program which engages both the healthcare professionals and the obstetric population, together with an educational module to increase the user awareness on the importance of sleep disturbances and their consequences during and after pregnancy.