Dynamics Of Human Response Research Articles

Flood–pedestrian simulator for modelling human response dynamics during flood-induced evacuation: Hillsborough stadium case study

Abstract. The flood–pedestrian simulator uses a parallel approach to couple a hydrodynamic model to a pedestrian model in a single agent-based modelling (ABM) framework on graphics processing units (GPU), allowing dynamic exchange and processing of multiple-agent information across the two models. The simulator is enhanced with more realistic human body characteristics and in-model behavioural rules. The new features are implemented in the pedestrian model to factor in age- and gender-related walking speeds for the pedestrians in dry zones around the floodwater and to include a maximum excitement condition. It is also adapted to use age-related moving speeds for pedestrians inside the floodwater, with either a walking condition or a running condition. The walking and running conditions are applicable without and with an existing two-way interaction condition that considers the effects of pedestrian congestion on the floodwater spreading. A new autonomous change of direction condition is proposed to make pedestrian agents autonomous in wayfinding decisions driven by their individual perceptions of the flood risk or the dominant choice made by the others. The relevance of the newly added characteristics and rules is demonstrated by applying the augmented simulator to reproduce a synthetic test case of a flood evacuation in a shopping centre, to then contrast its outcomes against the version of the simulator that does not consider age and gender in the agent characteristics. The enhanced simulator is demonstrated for a real-world case study of a mass evacuation from the Hillsborough football stadium, showing usefulness for flood emergency evacuation planning in outdoor spaces where destination choice and individual risk perception have great influence on the simulation outcomes.

Perturbation recovery during walking is impacted by knowledge of perturbation timing in below-knee prosthesis users and non-impaired participants

Previous research found that below-knee prosthesis users proactively increase their lateral margin-of-stability on their impaired side in anticipation of an impending perturbation when the timing is predictable and potentially directed toward the impaired limb. While knowledge of perturbation timing and direction influences proactive strategies, the consequences of such knowledge and anticipatory behavior on recovery from perturbations is unclear. This study characterized center-of-mass (CoM) dynamics of below-knee prosthesis users and non-impaired controls following a lateral perturbation when the perturbation direction is known but a priori knowledge of the timing of perturbation is either known or unknown. Across groups, CoM displacement during perturbation exposure increased when directed towards the impaired or non-dominant limb with no influence of timing knowledge. In addition, peak CoM displacement was less with known timing irrespective of the perturbation direction. Generally, the CoM displacement during perturbation exposure correlated well with the CoM medial-lateral velocity during unperturbed walking, supporting evidence that human response dynamics to lateral perturbations are influenced by the instantaneous state of the body’s momentum.

Single-cell modeling of routine clinical blood tests reveals transient dynamics of human response to blood loss

Low blood count is a fundamental disease state and is often an early sign of illnesses including infection, cancer, and malnutrition, but our understanding of the homeostatic response to blood loss is limited, in part by coarse interpretation of blood measurements. Many common clinical blood tests actually include thousands of single-cell measurements. We present an approach for modeling the unsteady-state population dynamics of the human response to controlled blood loss using these clinical measurements of single-red blood cell (RBC) volume and hemoglobin. We find that the response entails (1) increased production of new RBCs earlier than is currently detectable clinically and (2) a previously unrecognized decreased RBC turnover. Both component responses offset the loss of blood. The model provides a personalized dimensionless ratio that quantifies the balance between increased production and delayed clearance for each individual and may enable earlier detection of both blood loss and the response it elicits.

Data-Inferred Personalized Human-Robot Models for Iterative Collaborative Output Tracking

This article studies collaborative human- robot output tracking when the desired output is only known to the human but not to the robot controller. The main contribution of this article is to propose and establish convergence conditions for an iterative learning algorithm that updates the robot input using (i) the effect of the human action on the combined human-robot output tracking (which includes the effect of the human-response dynamics) and (ii) data-inferred human-robot models. This allows the iterative learning control (ILC) to be personalized for each individual human operator. Additionally, experimental results are presented to illustrate the iterative learning approach. Results show that, with the proposed approach, the robot can learn to collaboratively track the output with 10.0% error, which is close to twice the robot noise of 4.6% of the desired output. Furthermore, the data-inferred models provided evidence of the effect of the human operator’s dynamics on the co-tracking task.

Inferring Intent for Novice Human-in-the-Loop Iterative Learning Control

This paper studies human-in-the-loop iterative learning control for output-trajectory tracking of linear time-invariant (LTI) systems. Note that knowledge of the intended output is necessary to ascertain the tracking error and find the corrective input needed to reduce the error at each iteration step. In human-in-the-loop systems, the controller does not have direct access to the intended output, and therefore, the tracking error needs to be inferred from human actions. A challenge, however, in inferring the intent and the tracking error is that the human-response dynamics affects the relation between the tracking error (known to the human) and the resulting human action. The main contribution of this paper is to infer the tracking error by inverting a model of the human response and using it for iteratively controlling general LTI systems. This is enabled by modifying the controlled-system dynamics perceived by the human to be of a form for which the human-response model is known. An advantage of the proposed approach is that output tracking is achieved in spite of (potentially) imperfect corrective actions of the novice human user. Results of human-in-the-loop tracking experiments with nine human subjects show that the proposed approach can estimate the human intent (to within 97% accuracy). Moreover, the iterative approach substantially reduces the tracking error (about 93%) when compared with the tracking error achieved by the human alone.

How to Train Your Robot?

This article explores the concept of inferring intent during human-in-the-loop robot learning for output tracking. The human-response dynamics can affect the precision achieved by the human during human-in-the-loop operation. Preview-based online inversion is a viable technique that allows for stable online inversion of complex linear controlled systems, even those for which stable causal inverses do not exist, such as non-minimum phase systems. The averaged motion can still be affected by the human-response dynamics and can therefore be still different from the user’s intent. Therefore, inferring the human intent is important and necessary in the context of human-robot shared control. The results of applying the inversion-based iterative learning scheme to the human-in-the-loop trajectory tracking task has also been presented in the article. Figures show that the output tracking performance improves with respect to the manual tracking performance when inverse control is applied. When the iterative learning control law is applied, further tracking improvement is achieved. Thus, the learned control input can successfully emulate the human intent. An advantage of the robot-learning framework is that it allows novice human operators, who may be experts in the task, but not in teaching a robot, to successfully achieve the task objectives, which can expand the usage and acceptability of robots in society.

Millennial Entrepreneurs and Climate Change Management - A Cross Country Analysis of Innovation

This research paper aims to explore the dynamics of human response to global climate change by delving deeper into the link between human capital and emotional agility, and innovation by uncovering the unexplored and implicit dynamics of human capital and cognitive constructs that form an intrinsic part of emergence of novel climate change mitigation ventures. The phenomena of climate change and climate change mitigation are not new but the emergence of millennial entrepreneurs who work towards climate change mitigation has made the field of inquiry captivating and worth pursuing. The research paper explores the recently proposed concept of emotional agility and cognitive schemas in young millennial entrepreneurs working towards climate change mitigation. The research study involved rigorous exploration of the phenomenon using support from extant research as well empirical evidence from entrepreneurs from seventeen countries belonging to emerging markets. The key contribution of this research is its quali...

Mechanisms and models of human dynamics (Reply)

Kentsis notes that the response time to an e-mail or a letter depends on the semantic content of the correspondence, as well as the social context in which the communication arises1. We would add that it also depends on deadlines, the time dependence of priorities and the dropping of past-deadline messages2, making human response dynamics sufficiently complicated that no simple model could fully account for it3,4,5,6. However, the advantage of the proposed modelling framework is that most of these effects can be incorporated into it, and their impact on the queuing process can be systematically evaluated. Addressing some of these additional mechanisms, including those suggested by Kentsis, requires information that is beyond reach for most researchers at this point.