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The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human-Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators' cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators' MWL, whilst also allowing for increased integrity and reliability of the system.

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This paper presents a sensor-orientated approach to on-orbit position uncertainty generation and quantification for both ground-based and space-based surveillance applications. A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). In line with the envisioned Space Situational Awareness (SSA) evolutions, the method aims to represent the navigation and tracking errors in the form of an uncertainty volume that accurately depicts the size, shape, and orientation. Simulation case studies are then conducted to verify under which sensors performance the method meets Gaussian assumptions, with a greater view to the implications that uncertainty has on the cyber-physical architecture evolutions and Cognitive Human-Machine Systems required for Space Situational Awareness and the development of a comprehensive Space Traffic Management framework.

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Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator's cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator's states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator's cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.

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The goal of this study was to investigate the effects of visual degradation on warning symbol comprehension across warning symbol types and age groups. Twenty-seven black and white ANSI symbols of four different types (prohibition, course of action, information, and hazard symbols) were presented to older (N = 21, M = 73.1) and younger adults (N = 20, M = 21.4) via computer at three degradation levels (0%, 30%, 40% of pixels inverted); accuracy and response time in answering yes-no questions about the symbols were recorded. Younger adults were more accurate and faster overall than older adults (p < .01). Regarding degradation, 0% and 30% inverted symbols did not significantly differ in comprehension (p ≥ .25), but both were comprehended better than 40% inverted symbols (p < .01); no interactions were observed. For degraded warning symbols, results suggest symbols must be substantially degraded to affect base comprehensibility, and age differences exist. These data have practical implications for warnings in environments susceptible to degradation.

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Many companies are developing robots for the home, including robots specifically for older adults. There is little understanding, however, about the types and characteristics of tasks that younger and older individuals would be willing to let a robot perform. In a mailed questionnaire, participants were asked to indicate their willingness to have a robot perform each of 15 robot tasks that required different levels of interaction with the human owner and different levels of task criticality. The responses of 117 older adults (aged 65-86) and 60 younger adults (aged 18-25) were analyzed. The results indicated that respondents of both groups were more willing to have robots perform infrequent, albeit important, tasks that required little interaction with the human compared to service-type tasks with more required interaction; they were least willing to have a robot perform non-critical tasks requiring extensive interaction between robot and human. Older adults reported more willingness than younger adults in having a robot perform critical tasks in their home. The results suggest that both younger and older individuals are more interested in the benefits that a robot can provide than in their interactive abilities.

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A study was conducted to examine the expectations that younger and older individuals have about domestic robots and how these expectations relate to robot acceptance. In a questionnaire participants were asked to imagine a robot in their home and to indicate how much items representing technology, social partner, and teammate acceptance matched their robot. There were additional questions about how useful and easy to use they thought their robot would be. The dependent variables were attitudinal and intentional acceptance. The analysis of the responses of 117 older adults (aged 65-86) and 60 younger adults (aged 18-25) indicated that individuals thought of robots foremost as performance-directed machines, less so as social devices, and least as unproductive entities. The robustness of the Technology Acceptance Model to robot acceptance was supported. Technology experience accounted for the variance in robot acceptance due to age.

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OBJECTIVE: An empirical investigation was done to determine if there are age-related differences attributable to costs in reliance on a decision aid. BACKGROUND: Costs of reliance on a decision aid may affect reliance on the aid. Older and younger adults may not perceive and respond to a dynamic cost structure equally or objectively. METHOD: Sixteen older adults (65-74 years) and 16 younger adults (18-28 years) performed a counting task with an imperfect decision aid. Two types of costs were manipulated: (a) cost of error (CoE) and (b) cost of verification (CoV). The percentage of trials in which participants agreed with the decision aid and did not perform the task manually was recorded as reliance. RESULTS: Participants decreased their reliance as the CoE increased and increased their reliance with a lower CoV; however, they tended to underrely on the decision aid. Younger adults tended to change their reliance behavior more than older adults did with the changing cost structure. CONCLUSIONS: Older and younger adults appear to interpret costs differently, with older adults being less responsive to changes in costs. Older adults may have been less able to monitor the changing costs and hence not adapt to them as well as younger adults. APPLICATION: Designers of decision aids should consider explicitly stating costs associated with reliance on the aid, as individuals may differ in how they interpret and respond to changing costs.

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