Research works on operator monitoring underline the benefit of taking into consideration several signal modalities to improve accuracy for an objective mental state diagnosis. Heart rate (HR) is one of the most utilized systemic measures to assess cognitive workload (CW), whereas, respiration parameters are hardly utilized. This study aims at verifying the contribution of analyzing respiratory signals to extract features to evaluate driver’s activity and CW variations in driving. Eighteen subjects participated in the study. The participants carried out two different cognitive tasks requiring different CW demands, a single task as well as a competing cognitive task realized while driving in a simulator. Our results confirm that both HR and breathing rate (BR) increase in driving and are sensitive to CW. However, HR and BR are differently modulated by the CW variations in driving. Specifically, HR is affected by both driving activity and CW, whereas, BR is suitable to evidence a variation of CW only when driving is not required. On the other hand, spectral features characterizing respiratory signal could be also used similarly to HR variability indices to detect high CW episodes. These results hint the use of respiration as an alternative to HR to monitor the driver mental state in autonomic vehicles in order to predict the available cognitive resources if the user has to take over the vehicle.
Background We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO 2 ) signals. Methods We recorded the BF and SpO 2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S 24 severity score that represented the patient's severity over the previous 24 h; the validity of MS 24 , the maximum S 24 score, was checked against rates of intubation risk and prolonged ICU stay. Results Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S 24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS 24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS 24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). Conclusions Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.
In 2013, attention deficits accounted for 40 to 50 % of injury accidents. Recent studies have succeeded in detecting impaired states of attention, with a view to assisting the driver, and provide a new opportunity to increase road safety. This study focuses on the detection of drivers' cognitive effort and seeks, through the study of heart rate change (HRC), to identify a sensitive indicator of cognitive effort in short time windows. Eighteen young drivers participated in the study and took part in 8 experimental sessions where they performed a passive or active cognitive task (counting) while driving or not. The counting task had two difficulty levels (counting of beeps vs visuospatial skills and number adding). Participants' heart rates were monitored during all tasks. Previous results recorded in laboratory conditions have been replicated during driving: during the first seconds after a cognitive effort, there is a slight deceleration and a sharp acceleration in heart rate. Conversely, in the absence of cognitive effort, simple cardiac deceleration was observed. Our study confirms that it is possible to distinguish HRC in response to a cognitive effort over short time windows by observing the grand mean of evoked cardiac responses at 0.5 s intervals from stimulus onset when averaged over a significant number of episodes. The new opportunities offered with this cognitive effort indicator are discussed. Recent literature data show that the removal of respiratory influence from heart rate is feasible. With such correction, it seems possible to improve the sensitivity of HRC, and HR acceleration should be observed without averaging the HRC over many trials. If this proves effective, using an algorithm to detect cognitive effort in real time, future assistance devices could warn drivers or overcome their mistakes when they no longer control driving activity because of a cognitive effort.
Peat and mine drainage treatment sludge can be valorized as amendments on mine sites to stabilize gold mine tailings and reduce the potential leaching of contaminants in pore water. However, the influence of organic amendments on the mobility of metalloids and/or metals in the tailings must be validated, as the leached contaminants may vary according to their type, nature, and origin. The objective of the present study was to evaluate over time the effect of peat‐ and/or Fe‐rich sludge amendments on the mobility of As and metallic cations in the drainage water of tailings potentially producing contaminated neutral drainage. Ten duplicated weathering cell experiments containing tailings alone or amended with peat and/or Fe‐rich sludge (5–10% dry weight) were performed and monitored for 112 d. The results showed that as low as 5% peat amendment would promote As mobility in tailings’ pore water, with As concentrations exceeding Quebec discharge criteria (>0.2 mg L−1). In addition, As(III), the most mobile and toxic form, was predominant with 10% peat, whereas organic species were negligible in all cells. The use of peat alone as organic amendment for the stabilization of tailing contaminants could increase the risk of generating As‐rich contaminated neutral drainage. Conversely, the mix of only 5% Fe‐rich sludge with or without peat decreased As concentrations in leachates by 65 to 80%. Further studies on the use of “peat” or “peat + Fe‐rich sludge” as cover or amendment should be conducted with a focus on Fe/As and Ca/As ratios. Core Ideas Peat amendments enhanced the leaching of As from gold mine tailings. Amendments of 5% peat promoted As(V) leaching, whereas 10% peat increased As(III) leaching. As(III) was predominant at ≥20 mg L−1 dissolved organic C from peat. Mine drainage treatment sludge could decrease As concentrations by 65 to 80% in tailings’ pore water.
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