Digital Human Models in Human Factors and Ergonomics Evaluation of Gesture Interfaces

Aromaa, Susanna; Frangakis, Nikos; Tedone, Domenico; Viitaniemi, Juhani; Aaltonen, Iina

doi:10.1145/3229088

Cited by 7 publications

(2 citation statements)

References 55 publications

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“…Users are engaging with displays and contemporary operating power woodworking tools. The UX benchmark is designed to fuse state-of-the-art references in the field of UX satisfaction benchmarks [46][47][48][49] but also physical load evaluators, such as the popular NASA-TLX index [50]. However, it is important to note that the current participant pool is limited to one person with no prior construction experience but who is a competent digital literate.…”

Section: Experimental Set-upmentioning

confidence: 99%

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

Settimi,

Chutisilp,

Aymanns

et al. 2024

Applied Sciences

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We present TimberTool (TTool v2.1.1), a software designed for woodworking tasks assisted by augmented reality (AR), emphasizing its essential function of the real-time localization of a tool head’s poses within camera frames. The localization process, a fundamental aspect of AR-assisted tool operations, enables informed integration with contextual tracking, facilitating the computation of meaningful feedback for guiding users during tasks on the target object. In the context of timber construction, where object pose tracking has been predominantly explored in additive processes, TTool addresses a noticeable gap by focusing on subtractive tasks with manual tools. The proposed methodology utilizes a machine learning (ML) classifier to detect tool heads, offering users the capability to input a global pose and utilizing an automatic pose refiner for final pose detection and model alignment. Notably, TTool boasts adaptability through a customizable platform tailored to specific tool sets, and its open accessibility encourages widespread utilization. To assess the effectiveness of TTool in AR-assisted woodworking, we conducted a preliminary experimental campaign using a set of tools commonly employed in timber carpentry. The findings suggest that TTool can effectively contribute to AR-assisted woodworking tasks by detecting the six-degrees-of-freedom (6DoF) pose of tool heads to a satisfactory level, with a millimetric positional error of 3.9 ± 1 mm with possible large room for improvement and 1.19 ± 0.6° for what concerns the angular accuracy.

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Section: Experimental Set-upmentioning

confidence: 99%

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

Settimi,

Chutisilp,

Aymanns

et al. 2024

Applied Sciences

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show abstract

“…In such applications, verbal or gesture-based queues are used to communicate with the robots. For these, physical effort would effectively reduce to vocal strain [Zhao et al 2018] [Aromaa et al 2018]. Alternative interfaces such as augmented or virtual reality may have additional physical effort factors associated, including neck strain from the weight of displays [Garrett et al 2018] and cybersickness from extended use [Kennedy et al 1993;Rebenitsch and Owen 2016].…”

Section: Physical Effortmentioning

confidence: 99%

Towards Effective Interface Designs for Collaborative HRI in Manufacturing

Marvel

Bagchi

Zimmerman

et al. 2020

J. Hum.-Robot Interact.

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We present a comprehensive framework and test methodology for the evaluation of human-machine interfaces (HMI) and human-robot interactions (HRI) in collaborative manufacturing applications. An overview of the challenges that face current-and next-generation collaborative robot systems is presented, specifically focused on the interactions between man and machine, and a series of objectively quantitative and subjectively qualitative metrics are given to guide the development and assessment of interfaces and interactions. A generalized set of guidelines for the design of HMI is also proposed to address these challenges and thereby enable effective and intuitive diagnostics and error corrections when process failures occur. These guidelines are aimed at aiding researchers in developing effective interface and interaction technologies, maximizing operator situation awareness in human-robot collaborative manufacturing teams, promoting effective process and system diagnostics reporting, and enabling faster responses to equipment or application errors. CCS Concepts: • Human-centered computing → Collaborative interaction; User interface design; User centered design; • Computer systems organization → External interfaces for robotics;

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Evaluation of User Experience in Human–Robot Interaction: A Systematic Literature Review

Iriarte

Erle

Mazmela

2023

Int J of Soc Robotics

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Digital Human Models in Human Factors and Ergonomics Evaluation of Gesture Interfaces

Cited by 7 publications

References 55 publications

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

Towards Effective Interface Designs for Collaborative HRI in Manufacturing

Evaluation of User Experience in Human–Robot Interaction: A Systematic Literature Review

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