A supervisory controller intended to arrest dynamic falls with a wearable cold-gas thruster
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ABSTRACT:
SUBMITTER: Baimyshev A
PROVIDER: S-EPMC10952053 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Project description:We aimed to investigate the prognostic value of dynamic changes in arterial blood gas analysis (ABGA) measured after the start of cardiopulmonary resuscitation (CPR) for return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest (OHCA). This prospective observational study was conducted at the emergency department of a university hospital from February 2018 to February 2020. All blood samples for gas analysis were collected from a radial or femoral arterial line, which was inserted during CPR. Changes in ABGA parameters were expressed as delta (Δ), defined as the values of the second ABGA minus the values of the initial ABGA. The primary outcome was sustained ROSC. Out of the 80 patients included in the analysis, 13 achieved sustained ROSC after in-hospital resuscitation. Multivariable logistic analysis revealed that ΔpaO2 (odds ratio [OR] = 1.023; 95% confidence interval [CI] = 1.004-1.043, p = 0.020) along with prehospital shockable rhythm (OR = 84.680; 95% CI = 2.561-2799.939, p = 0.013) and total resuscitation duration (OR = 0.881; 95% CI = 0.805-0.964, p = 0.006) were significant predictors for sustained ROSC. Our study suggests a possible association between ΔpaO2 in ABGA during CPR and an increased rate of sustained ROSC in the late phase of OHCA.
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Project description:A laptop-driven, benchtop control system that automatically adjusts carbon dioxide (CO2) removal in artificial lungs (ALs) is described. The proportional-integral-derivative (PID) feedback controller modulates pump-driven air sweep gas flow through an AL to achieve a desired exhaust gas CO2 partial pressure (EGCO2). When EGCO2 increases, the servoregulator automatically and rapidly increases sweep flow to remove more CO2. If EGCO2 decreases, the sweep flow decreases to reduce CO2 removal. System operation was tested for 6 hours in vitro using bovine blood and in vivo in three proof-of-concept sheep experiments. In all studies, the controller automatically adjusted the sweep gas flow to rapidly (<1 minute) meet the specified EGCO2 level when challenged with changes in inlet blood or target EGCO2 levels. CO2 removal increased or decreased as a function of arterial pCO2 (PaCO2). Such a system may serve as a controller in wearable AL systems that allow for large changes in patient activity or disease status.
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Project description:Gyroscopic actuators are appealing for wearable applications due to their ability to provide overground balance support without obstructing the legs. Multiple wearable robots using this actuation principle have been proposed, but none has yet been evaluated with humans. Here we use the GyBAR, a backpack-like prototype portable robot, to investigate the hypothesis that the balance of both healthy and chronic stroke subjects can be augmented through moments applied to the upper body. We quantified balance performance in terms of each participant's ability to walk or remain standing on a narrow support surface oriented to challenge stability in either the frontal or the sagittal plane. By comparing candidate balance controllers, it was found that effective assistance did not require regulation to a reference posture. A rotational viscous field increased the distance healthy participants could walk along a 30mm-wide beam by a factor of 2.0, compared to when the GyBAR was worn but inactive. The same controller enabled individuals with chronic stroke to remain standing for a factor of 2.5 longer on a narrow block. Due to its wearability and versatility of control, the GyBAR could enable new therapy interventions for training and rehabilitation.
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Project description:IntroductionFalling is among the most serious clinical problems in Parkinson's disease (PD). We used body-worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life.MethodsWe matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5-year window, with an average follow-up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1-year cumulative incidence of falling.ResultsThe incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self-reported PD patients than controls (95% confidence interval, 1.6-2.0).ConclusionHaving PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime "falling disease." The results also point to the feasibility of using body-worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Project description:The next future strategies for improved occupational safety and health management could largely benefit from wearable and Internet of Things technologies, enabling the real-time monitoring of health-related and environmental information to the wearer, to emergency responders, and to inspectors. The aim of this study is the development of a wearable gas sensor for the detection of NH3 at room temperature based on the organic semiconductor poly(3,4-ethylenedioxythiophene) (PEDOT), electrochemically deposited iridium oxide particles, and a hydrogel film. The hydrogel composition was finely optimised to obtain self-healing properties, as well as the desired porosity, adhesion to the substrate, and stability in humidity variations. Its chemical structure and morphology were characterised by infrared spectroscopy and scanning electron microscopy, respectively, and were found to play a key role in the transduction process and in the achievement of a reversible and selective response. The sensing properties rely on a potentiometric-like mechanism that significantly differs from most of the state-of-the-art NH3 gas sensors and provides superior robustness to the final device. Thanks to the reliability of the analytical response, the simple two-terminal configuration and the low power consumption, the PEDOT:PSS/IrOx Ps/hydrogel sensor was realised on a flexible plastic foil and successfully tested in a wearable configuration with wireless connectivity to a smartphone. The wearable sensor showed stability to mechanical deformations and good analytical performances, with a sensitivity of 60 ± 8 μA decade-1 in a wide concentration range (17-7899 ppm), which includes the safety limits set by law for NH3 exposure.
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Project description:In the fall of 2016, a field study was conducted in the Uinta Basin Utah to improve information on oil and natural gas well pad pneumatic controllers (PCs). A total of 80 PC systems at five oil sites (supporting six wells) and three gas sites (supporting 12 wells) were surveyed, and emissions data were produced using a combination of measurements and engineering emission estimates. Ninety-six percent of the PCs surveyed were low actuation frequency intermittent vent type. The overall whole gas emission rate for the study was estimated at 0.36 scf/h with the majority of emissions occurring from three continuous vent PCs (1.0 scf/h average) and eleven (14%) malfunctioning intermittent vent PC systems (1.6 scf/h average). Oil sites employed, on average 10.3 PC systems per well compared to 1.5 for gas sites. Oil and gas sites had group average PC emission rates of 0.28 scf/h and 0.67 scf/h, respectively, with this difference due in part to site selection procedures. The PC system types encountered, the engineering emissions estimate approach, and comparisons to measurements are described. Survey methods included identification of malfunctioning PC systems and emission measurements with augmented high volume sampling and installed mass flow meters, each providing a somewhat different picture of emissions that are elucidated through example cases.
| S-EPMC6178829 | biostudies-literature
Project description:BackgroundFalls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments. To address this gap, we investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population.MethodsWe collected data from a wearable airbag's inertial measurement units (IMUs) from individuals with (n = 20 stroke) and without (n = 15 control) history of stroke while performing a series of falls (842 falls total) and non-falls (961 non-falls total) in a laboratory setting. A leave-one-subject-out crossvalidation was used to compare the performance of two identical machine learned models (adaptive boosting classifier) trained on cohort-dependent data (control or stroke) to pre-detect falls in the stroke cohort.ResultsThe average performance of the model trained on stroke data (recall = 0.905, precision = 0.900) had statistically significantly better recall (P = 0.0035) than the model trained on control data (recall = 0.800, precision = 0.944), while precision was not statistically significantly different. Stratifying models trained on specific fall types revealed differences in pre-detecting anterior-posterior (AP) falls (stroke-trained model's F1-score was 35% higher, P = 0.019). Using activities of daily living as non-falls training data (compared to near-falls) significantly increased the AUC (Area under the receiver operating characteristic) for classifying AP falls for both models (P < 0.04). Preliminary analysis suggests that users with more severe stroke impairments benefit further from a stroke-trained model. The optimal lead time (time interval pre-impact to detect falls) differed between control- and stroke-trained models.ConclusionsThese results demonstrate the importance of population sensitivity, non-falls data, and optimal lead time for machine learned pre-impact fall detection specific to stroke. Existing fall mitigation technologies should be challenged to include data of neurologically impaired individuals in model development to adequately detect falls in other high fall risk populations. Trial registration https://clinicaltrials.gov/ct2/show/NCT05076565 ; Unique Identifier: NCT05076565. Retrospectively registered on 13 October 2021.
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Project description:Falls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the "first fall", delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning). 8521 participants (72.7 ± 12.0 years, 5392 female) from six countries were assessed using a digital falls risk assessment protocol. Data consisted of wearable sensor data captured during the Timed Up and Go (TUG) test along with self-reported questionnaire data on falls risk factors, applied to previously trained and validated classifier models. We found that 25.8% of patients reported a fall in the previous 12 months, of the 74.6% of participants that had not reported a fall, 21.5% were found to have a high predicted risk of falls. Overall 26.2% of patients were predicted to be at high risk of falls. 29.8% of participants were found to have slow walking speed, while 19.8% had high gait variability and 17.5% had problems with transfers. We report an observational study of results obtained from a novel digital fall risk assessment protocol. This protocol is intended to support the early identification of older adults at risk of falls and inform the creation of appropriate personalized interventions to prevent falls. A population-based approach to management of falls using objective measures of falls risk and mobility impairment, may help reduce unnecessary outpatient and emergency department utilization by improving risk prediction and stratification, driving more patients towards clinical and community-based falls prevention activities.
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Project description:BackgroundThe development and physiology of plants are influenced by light intensity and its changes. Despite the significance of this phenomenon, there is a lack of understanding regarding the processes light regulates. This lack of understanding is partly due to the complexity of plant's responses, but also due to the limited availability of light setups capable of producing specific light patterns.ResultsWhile unraveling the complexities of plant responses will require further studies, this research proposes a simple method to implement dynamic light setups. In this study, we introduce two distinct electronic circuits that are cost-effective and enable the control of a dimmable power supply.ConclusionThis method enables the generation of intricate light patterns and rapid intensity fluctuations, providing a means to investigate how plants respond and develop when exposed to dynamic light conditions.
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Project description:Accurate and automatic assessments of body segment kinematics via wearable sensors are essential to provide new insights into the complex interactions between active lifestyle and fall risk in various populations. To remotely assess near-falls due to balance disturbances in daily life, current approaches primarily rely on biased questionnaires, while contemporary data-driven research focuses on preliminary fall-related scenarios. Here, we worked on an automated framework based on accurate trunk kinematics, enabling the detection of near-fall scenarios during locomotion. Using a wearable inertial measurement cluster in conjunction with evaluation algorithms focusing on trunk angular acceleration, the proposed sensor-framework approach revealed accurate distinguishment of balance disturbances related to trips and slips, thereby minimising false detections during activities of daily living. An important factor contributing to the framework's high sensitivity and specificity for automatic detection of near-falls was the consideration of the individual's gait characteristics. Therefore, the sensor-framework presents an opportunity to substantially impact remote fall risk assessment in healthy and pathological conditions outside the laboratory.
| S-EPMC11649693 | biostudies-literature