Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions.

The development of prognostics and health management solutions in the manufacturing industry has lagged behind academic advances due to a number of practical challenges. This work proposes a framework for the initial development of industrial PHM solutions that is based on the system development life cycle commonly used for software-based applications. Methodologies for completing the planning and design stages, which are critical for industrial solutions, are presented. Two challenges that are inherent to health modeling in manufacturing environments, data quality and modeling systems that experience trend-based degradation, are then identified and methods to overcome them are proposed. Additionally included is a case study documenting the development of an industrial PHM solution for a hyper compressor at a manufacturing facility operated by The Dow Chemical Company. This case study demonstrates the value of the proposed development process and provides guidelines for utilizing it in other applications.

Heterogeneous human-robot task allocation based on artificial trust.

Effective human-robot collaboration requires the appropriate allocation of indivisible tasks between humans and robots. A task allocation method that appropriately makes use of the unique capabilities of each agent (either a human or a robot) can improve team performance. This paper presents a novel task allocation method for heterogeneous human-robot teams based on artificial trust from a robot that can learn agent capabilities over time and allocate both existing and novel tasks. Tasks are allocated to the agent that maximizes the expected total reward. The expected total reward incorporates trust in the agent to successfully execute the task as well as the task reward and cost associated with using that agent for that task. Trust in an agent is computed from an artificial trust model, where trust is assessed along a capability dimension by comparing the belief in agent capabilities with the task requirements. An agent's capabilities are represented by a belief distribution and learned using stochastic task outcomes. Our task allocation method was simulated for a human-robot dyad. The team total reward of our artificial trust-based task allocation method outperforms other methods both when the human's capabilities are initially unknown and when the human's capabilities belief distribution has converged to the human's actual capabilities. Our task allocation method enables human-robot teams to maximize their joint performance.

Predicting driver takeover performance in conditionally automated driving.

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers' physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers' takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers' takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.

Time domain characterization for sleep apnea in oronasal airflow signal: a dynamic threshold classification approach.

OBJECTIVE: Apneas are the most common type of sleep-related breathing disorders; they cause patients to move from restorative sleep into inefficient sleep. The American Academy of Sleep Medicine (AASM) considers sleep apnea as a hidden health crisis that affects 29.4 million adults, costing the USA billions of dollars. Traditional detection methods of sleep apnea are achieved by human observation of the respiration signals. This introduces limitations in terms of access and efficiency of diagnostic sleep studies. However, alternative device technologies have limited diagnostic accuracy for detecting apnea events although many of the previous investigational algorithms are based on multiple physiological channel inputs. Guided by the AASM recommendations for sleep apnea diagnostics, this paper investigates time domain metrics to characterize changes in oronasal airflow respiration signals during the occurrence of apneic events. APPROACH: A new algorithm is developed to derive a respiratory baseline from the oronasal airflow signal in order to detect sleep apnea events using a dynamically adjusted threshold classification approach. To demonstrate our results, we use polysomnography data of [Formula: see text] patients with different apnea severity levels as reflected by their overnight apnea hypopnea index (AHI), including patients with mild apnea (5 [Formula: see text] AHI [Formula: see text]), moderate apnea ([Formula: see text] AHI [Formula: see text]), and severe apnea (AHI [Formula: see text]). MAIN RESULTS: Our results indicate the ability to characterize sleep apnea events in oronasal airflow signals using the proposed dynamic threshold classification approach. Overall, the new algorithm achieved a sensitivity of 80.0%, specificity of 88.7%, and an area under receiver operating characteristics curve of 0.844. SIGNIFICANCE: The present results contribute a new approach for progressive detection of sleep apnea using an adaptive threshold that is dynamically adjusted with respect to the patient's respiration baseline, making it potentially able to effectively generalize over patients with different apnea severity levels and longer monitoring periods.

Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior.

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human-subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.

Multi-Step Ahead Predictions for Critical Levels in Physiological Time Series.

Standard modeling and evaluation methods have been classically used in analyzing engineering dynamical systems where the fundamental problem is to minimize the (mean) error between the real and predicted systems. Although these methods have been applied to multi-step ahead predictions of physiological signals, it is often more important to predict clinically relevant events than just to match these signals. Adverse clinical events, which occur after a physiological signal breaches a clinically defined critical threshold, are a popular class of such events. This paper presents a framework for multi-step ahead predictions of critical levels of abnormality in physiological signals. First, a performance metric is presented for evaluating multi-step ahead predictions. Then, this metric is used to identify personalized models optimized with respect to predictions of critical levels of abnormality. To address the paucity of adverse events, weighted support vector machines and cost-sensitive learning are used to optimize the proposed framework with respect to statistical metrics that can take into account the relative rarity of such events.

A new algorithm for the detection of sleep apnea events in respiration signals.

Sleep apneas are the most common type of sleep-related breathing disorders which cause a patient to move from a good sleep into an inefficient sleep. In addition, sleep apnea widely impacts the American population and is a large cost for healthcare. Traditional detection methods of sleep apneas are complex, expensive, and invasive to most patients. Among the various physiological signals, respiration signals are relatively easy to be monitored. However, not many studies are conducted using respiration signal only, and most of the previous algorithms are insufficient to detect apnea events. In this paper, we propose a new algorithm based on only the respiration signal to detect the apnea events during sleep and conduct experiments comparing the performance of our algorithm against two apnea detection algorithms. We use 20 patients' data, all of whom have severe Apnea Hypopnea Index (AHI>30: over 30 events per hour). Our study shows that our algorithm outperforms the other two algorithms.

Effect of concurrent oxygen therapy on accuracy of forecasting imminent postoperative desaturation.

Episodic postoperative desaturation occurs predominantly from respiratory depression or airway obstruction. Monitor display of desaturation is typically delayed by over 30 s after these dynamic inciting events, due to perfusion delays, signal capture and averaging. Prediction of imminent critical desaturation could aid development of dynamic high-fidelity response systems that reduce or prevent the inciting event from occurring. Oxygen therapy is known to influence the depth and duration of desaturation epochs, thereby potentially influencing the accuracy of forecasting of desaturation. In this study, postoperative pulse oximetry data were retrospectively modeled using autoregressive methods to create prediction models for [Formula: see text] and imminent critical desaturation in the postoperative period. The accuracy of these models in predicting near future [Formula: see text] values was tested using root mean square error. The model accuracy for prediction of critical desaturation ([Formula: see text] [Formula: see text]) was evaluated using meta-analytical methods (sensitivity, specificity, likelihood ratios, diagnostic odds ratios and area under summary receiver operating characteristic curves). Between-study heterogeneity was used as a measure of reliability of the model across different patients and evaluated using the tau-squared statistic. Model performance was evaluated in [Formula: see text] patients who received postoperative oxygen supplementation and [Formula: see text] patients who did not receive oxygen. Our results show that model accuracy was high with root mean square errors between 0.2 and 2.8%. Prediction accuracy as defined by area under the curve for critical desaturation events was observed to be greater in patients receiving oxygen in the 60-s horizon ([Formula: see text] vs. [Formula: see text]). This was likely related to the higher frequency of events in this group (median [IQR] [Formula: see text] [Formula: see text]) than patients who were not treated with oxygen ([Formula: see text] [Formula: see text]; [Formula: see text]). Model reliability was reflected by the homogeneity of the prediction models which were homogenous across both prediction horizons and oxygen treatment groups. In conclusion, we report the use of autoregressive models to predict [Formula: see text] and forecast imminent critical desaturation events in the postoperative period with high degree of accuracy. These models reliably predict critical desaturation in patients receiving supplemental oxygen therapy. While high-fidelity prophylactic interventions that could modify these inciting events are in development, our current study offers proof of concept that the afferent limb of such a system can be modeled with a high degree of accuracy.

Evaluating predictions of critical oxygen desaturation events.

This paper presents a new approach for evaluating predictions of oxygen saturation levels in blood ( SpO2). A performance metric based on a threshold is proposed to evaluate SpO2 predictions based on whether or not they are able to capture critical desaturations in the SpO2 time series of patients. We use linear auto-regressive models built using historical SpO2 data to predict critical desaturation events with the proposed metric. In 20 s prediction intervals, 88%-94% of the critical events were captured with positive predictive values (PPVs) between 90% and 99%. Increasing the prediction horizon to 60 s, 46%-71% of the critical events were detected with PPVs between 81% and 97%. In both prediction horizons, more than 97% of the non-critical events were correctly classified. The overall classification capabilities for the developed predictive models were also investigated. The area under ROC curves for 60 s predictions from the developed models are between 0.86 and 0.98. Furthermore, we investigate the effect of including pulse rate (PR) dynamics in the models and predictions. We show no improvement in the percentage of the predicted critical desaturations if PR dynamics are incorporated into the SpO2 predictive models (p-value = 0.814). We also show that including the PR dynamics does not improve the earliest time at which critical SpO2 levels are predicted (p-value = 0.986). Our results indicate oxygen in blood is an effective input to the PR rather than vice versa. We demonstrate that the combination of predictive models with frequent pulse oximetry measurements can be used as a warning of critical oxygen desaturations that may have adverse effects on the health of patients.