Psychophysiological responses to different levels of cognitive and physical workload in haptic i nteraction. Robotica

SUMMARY Psychophysiological measurements, which serve as objective indicators of psychological state, have recently been introduced into human-robot interaction. However, their usefulness in haptic interaction is uncertain, since they are influenced by physical workload. This study analyses psychophysiological responses to a haptic task with three different difficulty levels and two different levels of physical load. Four physiological responses were recorded: heart rate, skin conductance, respiratory rate and skin temperature. Results show that mean respiratory rate, respiratory rate variability and skin temperature show significant differences between difficulty levels regardless of physical load and can be used to estimate cognitive workload in haptic interaction

Multimodal Interfaces to Improve Therapeutic Outcomes in Robot-Assisted Rehabilitation

The paper presents the developing of a new robotic system for the administration of a highly sophisticated therapy to stroke patients. This therapy is able to maximize patient motivation and involvement in the therapy and continuously assess the progress of the recovery from the functional viewpoint. Current robotic rehabilitation systems do not include patient information on the control loop. The main novelty of the presented approach is to close patient in the loop and use multisensory data (such as pulse, skin conductance, skin temperature, position, velocity, etc.) to adaptively and dynamically change complexity of the therapy and real-time displays of a virtual reality system in accordance with specific patient requirements. First, an analysis of subject’s physiological responses to different tasks is presented with the objective to select the best candidate of physiological signals to estimate the patient physiological state during the execution of a virtual rehabilitation task. Then, the design of a prototype of multimodal robotic platform is defined and developed to validate the scientific value of the proposed approach

Analysis of autonomic indexes on drivers' workload to assess the effect of visual ADAS on user experience and driving performance in different driving conditions

Advanced driver assistance systems (ADASs) allow information provision through visual, auditory, and haptic signals to achieve multidimensional goals of mobility. However, processing information from ADAS requires operating expenses of mental workload that drivers incur from their limited attentional resources. The change in driving condition can modulate drivers' workload and potentially impair drivers' interaction with ADAS. This paper shows how the measure of cardiac activity (heart rate and the indexes of autonomic nervous system (ANS)) could discriminate the influence of different driving conditions on drivers' workload associated with attentional resources engaged while driving with ADAS. Fourteen drivers performed a car-following task with visual ADAS in a simulated driving. Drivers' workload was manipulated in two driving conditions: one in monotonous condition (constant speed) and another in more active condition (variable speed). Results showed that drivers' workload was similarly affected, but the amount of attentional resources allocation was slightly distinct between both conditions. The analysis of main effect of time demonstrated that drivers' workload increased over time without the alterations in autonomic indexes regardless of driving condition. However, the main effect of driving condition produced a higher level of sympathetic activation on variable speed driving compared to driving with constant speed. Variable speed driving requires more adjustment of steering wheel movement (SWM) to maintain lane-keeping performance, which led to higher level of task involvement and increased task engagement. The proposed measures appear promising to help designing new adaptive working modalities for ADAS on the account of variation in driving condition

Applications of Affective Computing in Human-Robot Interaction: state-of-art and challenges for manufacturing

The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, i.e., capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g., facial expressions, body language, voice, or physiological signals) and to adapt its behaviour accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g., healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector

Quantifying Cognitive Efficiency of Display in Human-Machine Systems

As a side effect of fast growing informational technology, information overload becomes prevalent in the operation of many human-machine systems. Overwhelming information can degrade operational performance because it imposes large mental workload on human operators. One way to address this issue is to improve the cognitive efficiency of display. A cognitively efficient display should be more informative while demanding less mental resources so that an operator can process larger displayed information using their limited working memory and achieve better performance. In order to quantitatively evaluate this display property, a Cognitive Efficiency (CE) metric is formulated as the ratio of the measures of two dimensions: display informativeness and required mental resources (each dimension can be affected by display, human, and contextual factors). The first segment of the dissertation discusses the available measurement techniques to construct the CE metric and initially validates the CE metric with basic discrete displays. The second segment demonstrates that displays showing higher cognitive efficiency improve multitask performance. This part also identifies the version of the CE metric that is the most predictive of multitask performance. The last segment of the dissertation applies the CE metric in driving scenarios to evaluate novel speedometer displays; however, it finds that the most efficient display may not better enhance concurrent tracking performance in driving. Although the findings of dissertation show several limitations, they provide valuable insight into the complicated relationship among display, human cognition, and multitask performance in human-machine systems

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

n/

Shared mental models and intra-team psychophysiological patterns: A test of the juggling paradigm

We explored implicit coordination mechanisms underlying the conceptual notion of "shared mental models" (SMM) through physiological (i.e., breathing and heart rates) and affective-cognitive (i.e., arousal, pleasantness, attention, self-efficacy, other's efficacy) monitoring of two professional jugglers performing a real-time interactive task of increasing difficulty. There were two experimental conditions: "individual" (i.e., solo task) and "interactive" (i.e., two jugglers established a cooperative interaction by juggling sets of balls with each other). In both conditions, there were two task difficulties: “easy” and “hard”. Descriptive analyses revealed that engaging in a dyadic cooperative motor task (interactive condition) required greater physiological effort (Median Cohen’s d = 2.13) than performing a solo motor task (individual condition) of similar difficulty. Our results indicated a strong positive correlation between the jugglers’ heart rate for the easy (r = .87) and hard tasks (r = .77). The relationship between the jugglers’ breathing rate was significant for the easy task (r = .73) but non-significant for the hard task. The findings are interpreted based on research on SMM and Theory of Mind. Practitioners should advance the notion of “shared-regulation” in the context of team coordination through the use of biofeedback training