Real-Time Speech Workload Estimation for Intelligent Human- Machine Systems

Demanding task environments (e.g., supervising a remotely piloted aircraft) require performing tasks quickly and accurately; however, periods of low and high operator workload can decrease task performance. Intelligent modulation of the system’s demands and interaction modality in response to changes in operator workload state may increase performance by avoiding undesirable workload states. This system requires real- time estimation of each workload component (i.e., cognitive, physical, visual, speech, and auditory) to adapt the correct modality. Existing workload systems estimate multiple workload components post-hoc, but none estimate speech workload, or function in real-time. This manuscript presents an algorithm to estimate speech workload and mitigate undesirable workload states in real-time. The adaptive system uses the algorithm’s estimates to mitigate under/overload, a crucial step towards adaptive machine-human systems.

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Fessonia: a Method for Real-Time Estimation of Human Operator Workload Using Behavioural Entropy

10.1109/smc52423.2021.9658880 ◽

2021 ◽

Author(s):

Paraskevas Chatzithanos ◽

Grigoris Nikolaou ◽

Rustam Stolkin ◽

Manolis Chiou

Keyword(s):

Real Time ◽

Time Estimation ◽

Human Operator ◽

Real Time Estimation ◽

Operator Workload

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Speech Workload Estimation for Human-Machine Interaction

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181319631018 ◽

2019 ◽

Vol 63 (1) ◽

pp. 277-281

Author(s):

Jamison Heard ◽

Julian Fortune ◽

Julie A. Adams

Keyword(s):

Real Time ◽

Task Performance ◽

Visual Speech ◽

Human Machine Interaction ◽

Essential Component ◽

Human Machine Interfaces ◽

Remotely Piloted Aircraft ◽

High Workload ◽

Machine Interaction

Performing tasks quickly and accurately in dynamic and intense environments is critical, such as supervising a remotely piloted aircraft; however, these environments contain periods of low and high workload, which can decrease task performance. A system capable of intelligently adapting its interaction modality based on the human’s workload state may mitigate these undesirable workload states: underload and overload. Such a system requires mechanisms to determine accurately the human’s overall workload state and each workload component state (i.e., cognitive, physical, visual, speech, and auditory) in order to understand the current workload state’s underlying cause effectively. Existing work estimates multiple workload components, but no method estimates speech workload. This manuscript presents an algorithm for accurately estimating a human’s speech workload level using methods suitable for real-time workload assessment. The algorithm is an essential component to future adaptive human-machine interfaces.

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A machine for real-time estimation of the tribological characteristics of materials

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-4-61-65 ◽

2020 ◽

Vol 86 (4) ◽

pp. 61-65

Author(s):

M. V. Abramchuk ◽

R. V. Pechenko ◽

K. A. Nuzhdin ◽

V. M. Musalimov

Keyword(s):

Real Time ◽

Time Estimation ◽

Tribological Characteristics ◽

Continuous Processing ◽

Friction Machine ◽

Dynamic Friction Coefficient ◽

Displacement Sensors ◽

Frictional Interaction ◽

Real Time Estimation ◽

Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

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