Assessing physical workload for human–robot peer-based teams

Harriott, Caroline E.; Zhang, Tao; Adams, Julie A.

doi:10.1016/j.ijhcs.2013.04.005

Cited by 20 publications

(20 citation statements)

References 31 publications

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“…The task networks and workload component values generate IMPRINT Pro models to derive continuous workload values across seven components: auditory, cognitive, visual, speech, gross motor, fine motor, and tactile. The models used in the presented algorithm combine IMPRINT Pro's gross motor, fine motor, and tactile components into a physical workload model (Harriott et al, 2013). IMPRINT Pro generates an overall model by aggregating each component model.…”

Section: Workload Assessment Algorithmmentioning

confidence: 99%

Multi-Dimensional Human Workload Assessment for Supervisory Human–Machine Teams

Heard

Adams

2019

Journal of Cognitive Engineering and Decision Making

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Humans commanding and monitoring robots’ actions are used in various high-stress environments, such as the Predator or MQ-9 Reaper remotely piloted unmanned aerial vehicles. The presence of stress and potential costly mistakes in these environments places considerable demands and workload on the human supervisors, which can reduce task performance. Performance may be augmented by implementing an adaptive workload human–machine teaming system that is capable of adjusting based on a human’s workload state. Such a teaming system requires a human workload assessment algorithm capable of estimating workload along multiple dimensions. A multi-dimensional algorithm that estimates workload in a supervisory environment is presented. The algorithm performs well in emulated real-world environments and generalizes across similar workload conditions and populations. This algorithm is a critical component for developing an adaptive human–robot teaming system that can adapt its interactions and intelligently (re-)allocate tasks in dynamic domains.

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Section: Workload Assessment Algorithmmentioning

confidence: 99%

Multi-Dimensional Human Workload Assessment for Supervisory Human–Machine Teams

Heard

Adams

2019

Journal of Cognitive Engineering and Decision Making

Self Cite

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“…Speech-response time and noise level are indicators of auditory workload [6,8,21], while speech workload can be assessed using speech-based measures (e.g., speech-response time, speech rate, and filler utterances [6,30]). Heart rate [18,32], respiration rate [33,47], skin temperature [39,40], and posture-based metrics (e.g., posture sway and posture magnitude [20,35,43]) correlate to physical workload. Physiological metrics vary from individual to individual (i.e., physical fitness impacts heart rate); thus, workload assessment algorithms must account for these individual differences to generalize across populations [11,24].…”

Section: Relevant Workmentioning

confidence: 99%

A Diagnostic Human Workload Assessment Algorithm for Collaborative and Supervisory Human--Robot Teams

Heard

Heald

Harriott

et al. 2019

J. Hum.-Robot Interact.

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High-stress environments, such as first-response or a NASA control room, require optimal task performance, as a single mistake may cause monetary loss or even the loss of human life. Robots can partner with humans in a collaborative or supervisory paradigm to augment the human's abilities and increase task performance. Such teaming paradigms require the robot to appropriately interact with the human without decreasing either's task performance. Workload is related to task performance; thus, a robot may use a human's workload state to modify its interactions with the human. Assessing the human's workload state may also allow for dynamic task (re-)allocation, as a robot can predict whether a task may overload the human and, if so, allocate it elsewhere. A diagnostic workload assessment algorithm that accurately estimates workload using results from two evaluations, one peer based and one supervisory based, is presented. The algorithm correctly classified workload at least 90% of the time when trained on data from the same human-robot teaming paradigm. This algorithm is an initial step toward robots that can adapt their interactions and intelligently (re-)allocate tasks. CCS Concepts: • Computing methodologies → Neural networks; • Computer systems organization → Robotic autonomy;

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“…Over 500 human performance functions have been analyzed and validated (Silverman, Johns, Cornwell, & O’Brien, 2006) for domains, such as driving (e.g., Salvucci, 2001), power plant operation (e.g., Mumaw, Roth, Vicenti, & Burns, 2000), and military applications (e.g., Weaver et al, 2001). Prior work focused on evaluating the applicability of a subset of these HPFs to human-robot interaction (e.g., Harriott & Adams, 2013; Harriott, Buford, Zhang, & Adams, 2015; Harriott, Zhang, & Adams, 2013; Harriott, Zhuang, Adams, & DeLoach, 2012).…”

Section: A General Formal Framework For Smmsmentioning

confidence: 99%

A Framework for Developing and Using Shared Mental Models in Human-Agent Teams

Scheutz

DeLoach

Adams

2017

Journal of Cognitive Engineering and Decision Making

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Converging evidence from psychology, human factors, management and organizational science, and other related fields suggests that humans working in teams employ shared mental models to represent and use pertinent information about the task, the equipment, the team members, and their roles. In particular, shared mental models are used to interact efficiently with other team members and to track progress in terms of goals, subgoals, achieved and planned states, as well as other team-related factors. Although much of the literature on shared mental models has focused on quantifying the success of teams that can use them effectively, there is little work on the types of data structures and processes that operate on them, which are required to operationalize shared mental models. This paper proposes the first comprehensive formal and computational framework based on results from human teams that can be used to implement shared mental models for artificial virtual and robotic agents. The formal portion of the framework specifies the necessary data structures and representations, whereas the computational framework specifies the necessary computational processes and their interactions to build, update, and maintain shared mental models.

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Assessing physical workload for human–robot peer-based teams

Cited by 20 publications

References 31 publications

Multi-Dimensional Human Workload Assessment for Supervisory Human–Machine Teams

Multi-Dimensional Human Workload Assessment for Supervisory Human–Machine Teams

A Diagnostic Human Workload Assessment Algorithm for Collaborative and Supervisory Human--Robot Teams

A Framework for Developing and Using Shared Mental Models in Human-Agent Teams

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