Arjan Stuiver

Cardiovascular state changes during performance of a simulated ambulance dispatchers' task: potential use for adaptive support

Adaptive support has the potential to keep the operator optimally motivated, involved, and able to perform a task. In order to use such support, the operators state has to be determined from physiological parameters and task performance measures. In an environment where the task of an ambulance dispatcher was simulated, two studies have been carried out to evaluate the feasibility of using cardiovascular measures for adaptive support. During performance of this 2-3h lasting planning task, a pattern of results is found that can be characterized by an initial increase of blood pressure and heart rate and a decrease of heart rate variability (defense reaction pattern) followed by an ongoing increase of blood pressure counteracted by a decrease in heart rate. This pattern can be explained by an augmented short-term blood pressure control (baroreflex), which is reflected in an increase of baroreflex sensitivity. Additionally, in this latter phase heart rate variability (HRV) increases as a function of time, while blood pressure variability decreases. In the two studies performed, the baroreflex pattern was consistent for all the relevant variables. In both studies there were periods with high and low workload. Effects of task load are mainly reflected in the variability measures, while in the second study, additionally, blood pressure level was higher during periods with high task demands. The conclusion of the studies is that consistent cardiovascular response patterns can be recognized during this semi-realistic planning task, where variability measures are most sensitive to task demand changes, while blood pressure and baroreflex sensitivity are most informative with respect to cardiovascular state changes. These findings can be seen as a great potential benefit for future use in adaptive support applications.

An Adaptive Driver Support System: User Experiences and Driving Performance in a Simulator

Objective: The aim of this study was to test the implementation of an adaptive driver support system. Background: Providing support might not always be desirable from a safety perspective, as support may lead to problems related to a human operator being out of the loop. In contrast, adaptive support systems are designed to keep the operator in the loop as much as possible by providing support only when necessary. Method: A total of 31 experienced drivers were exposed to three modes of lane-keeping support: nonadaptive, adaptive, and no support. Support involved continuously updated lateral position feedback shown on a head-up display. When adaptive, support was triggered by performance-based indications of effort investment. Narrowing lane width and increasing density of oncoming traffic served to increase steering demand, and speed was fixed in all conditions to prevent any compensatory speed reactions. Results: Participants preferred the adaptive support mode mainly as a warning signal and tended to ignore nonadaptive feedback. Furthermore, driving behavior was improved by adaptive support in that participants drove more centrally, displayed less lateral variation and drove less outside the lane’s delineation when support was in the adaptive mode compared with both the no-support mode and the nonadaptive support mode. Conclusion: A human operator is likely to use machine-triggered adaptations as an indication that thresholds have been passed, regardless of the support that is initiated. Therefore supporting only the sensory processing stage of the human information processing system with adaptive automation may not feasible. Application: These conclusions are relevant for designing adaptive driver support systems.

Short-Term Cardiovascular Responses to Changing Task Demands

When measuring operator states the predictive power of cardiovascular and respiratory measures in relation to mental workload has been questioned. One of the main questions is to what extent do cardiovascular measures actually reflect mental workload. This question arises because good measures of mental workload should be sensitive to changes in mental effort alone and not to other influences or at least the changes associated with mental workload should be easy to isolate. In the case of cardiovascular measures, the physiological change brought on by the baroreflex is a compensatory control effect that can potentially overshadow changes in physiology due to mental effort and therefore reduce the usefulness of cardiovascular measures. However, this does not need to be the case. Despite the effects caused by the baroreflex differences in heart rate, heart rate variability and other cardiovascular measures associated with task related effort can still be found using short-term response patterns. The short-segment analysis approach described in this paper is based on a time-frequency method in which the spectral power of the cardiovascular measures in specified spectral bands is computed from small time segments, i.e. 30 s. To demonstrate the effectiveness of this technique two studies which made use of a simulation of an ambulance dispatchers task are described, both with easy and difficult task conditions. A short-lasting increase in task demand was found to be reflected in short-lasting increases in heart rate and blood pressure in combination with corresponding decreases in heart rate variability and blood pressure variability. These effects were larger in easy task conditions than in hard conditions, likely due to a higher overall effort-level during the hard task conditions. However, the developed measures are still very sensitive to mental effort and if this brief segmentation approach is used cardiovascular measures show promise as good candidates for reflecting mental effort during the assessment of operator state.

Cardiovascular state changes in simulated work environments

The usefulness of cardiovascular measures as indicators of changes in cognitive workload has been addressed in several studies. In this paper the question is explored whether cardiovascular patterns in heart rate, blood pressure, baroreflex sensitivity and HRV that are found are consistent within and between two simulated working environments. Two studies, were performed, both with 21 participants: one in an ambulance dispatch simulation and one in a driving simulator. In the ambulance dispatcher task an initial strong increase in blood pressure is followed by a moderate on-going increase in blood pressure during the next hour of task performance. This pattern is accompanied by a strong increase in baroreflex sensitivity while heart rate decreases. In the driving simulator study, blood pressure initially increases but decreases almost to baseline level in the next hour. This pattern is accompanied by a decrease in baroreflex sensitivity, while heart rate decreases. Results of both studies are interpreted in terms of autonomic control (related to both sympathetic and para-sympathetic effects), using a simplified simulation of a baroreflex regulation model. Interpretation of the results leads to the conclusion that the cardiovascular response patterns in both tasks are a combination of an initial defensive reaction, in combination with compensatory blood pressure control. The level of compensatory blood pressure control, however, is quite different for the two tasks. This helps to understand the differences in response patterns between the two studies in this paper and may be helpful as well for understanding differences in cardiovascular response patterns in general. A substantial part of the effects observed during task performance are regulatory effects and are not always directly related to workload manipulations. Making this distinction may also contribute to the understanding of differences in cardiovascular response patterns during cognitive workload.

Artefact-free real-time computation of cardiovascular measures

Cardiovascular measures can be used as indices of physiological and affective state. The theoretical and practical issues encountered in the sequence of data-acquisition, artefact handling and data (pre-) processing are described in this paper. The results of these processes are used to formulate suggestions how to develop an operator status model, a generic system for operator assessment.

Using Physiological Measures For Task Adaptation

Current Man Machine Interfaces (MMI) present information to the operator when it becomes available and when it is convenient to the computer; such automated systems do not act as a ‘team player’. Computers lack insight in the actual status, intentions and occupations of the operator. An approach is presented (COMPANION) in which better co-operation between user and computer is stimulated. In this approach, information about the users’ (physiological) state is used to adapt the MMI of the task at moments or in time periods where this is helpful for optimal task performance. Also task performance measures can be used in this feedback structure. COMPANION is about improving the Human Machine relation by: not interrupting the operator with non-urgent messages when he or she is completing an important task, and supporting the operator when task demands are high. In order to prevent specific solutions for each new task or for each additional information source an Operator Status Model (OSM) has been developed.

Adaptive Task Support Based on Dynamic Human State Estimation

Adaptive systems that provide task support to the human operator when needed can be a solution to the problems of traditional automation in complex dynamic systems. One way of initiating support is automatically: let the technical system assess operator functional state based on continuous monitored physiological, performance, and environmental factors and let it decide whether support is needed. For this concept to work in practice several human-related problems need to be solved. This is the focus of the REFLECT project which aims at developing adaptive systems that really support the human operator. Some of the accumulated knowledge gained in this and preceding projects are addressed in this paper. In addition, a design cycle for adaptive systems is presented that provides a functional overview of the design issues to be solved.

Applying Neural-Symbolic Cognitive Agents in Intelligent Transport Systems to reduce CO2 emissions

Providing personalized feedback in Intelligent Transport Systems is a powerful tool for instigating a change in driving behaviour and the reduction of CO2 emissions. This requires a system that is capable of detecting driver characteristics from real-time vehicle data. In this paper, we apply the architecture and theory of a Neural-Symbolic Cognitive Agent (NSCA) to effectively learn and reason about observed driving behaviour and related driver characteristics. The NSCA architecture combines neural learning and reasoning with symbolic temporal knowledge representation and is capable of encoding background knowledge, learning new hypotheses from observed data, and inferring new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model, and it scales well to hundreds of thousands of data samples as in the application reported in this paper. We have applied the NSCA in an Intelligent Transport System to reduce CO2 emissions as part of an European Union project, called EcoDriver. Results reported in this paper show that the NSCA outperforms the state-of-the-art in this application area, and is applicable to very large data.