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WEBA dataset as the Reflection of Work content effect on Workload perception in Real life Working conditions
WEBA dataset as the Reflection of Work content effect on Workload perception in Real life Working conditions
The effect of work content on workload, stress, and performance was not well addressed in the literature, due to the lack of comprehensive conceptualization, problem definition, and relevant dataset. The gap between laboratory-simulated studies and real-life working conditions delays the generalization, hindering the development of performance management and monitoring tools. Contributing to this topic, a data collection effort is organized, which considers unique work conditions and work content factors of a coffee shop, to conceptualize scenarios that better highlight their effect on human performance, thus creating the Work content Effect on BAristas (WEBA) dataset. Utilizing sensor technologies to recognize the ongoing activities, physical work activities and heart rates of five baristas in 55 shifts with different work content combinations during real-life working processes were recorded, while the integration of subjective and objective measures of workload and emotions were deployed as perceived workload indicators. Heart rate signals during normal conditions without working were measured as the baseline. This dataset is unique in its conceptualization and useful for scrutinizing more nuances of the effect of work content on performance and the well-being of employees, as well as facilitating better human factor engineering, workplace and work task design.
Post Date: 03 January 2025
Assessing human worker performance by pattern mining of Kinect sensor skeleton data
Assessing human worker performance by pattern mining of Kinect sensor skeleton data
The human worker is an in-disposable factor in manufacturing processes. Traditional observation methods to assess their performance is time-consuming and expert-dependent, while it is still impossible to diagnose the detailed movement trajectory with the naked eye. Industry 4.0 technologies can innovate that process with smart sensors paired with data mining techniques for automated operation and develop a database of frequent movements for corporate reference and improvement. This paper proposes an approach to automatically assess worker performance with skeleton data by applying pattern mining methods and supervised learning algorithms. A use case is performed on an electrical assembly line to validate the approach, with the skeleton data collected by Kinect sensor v2. By using supervised learning, the movements of workers in each workstation can be segmented, and the line performance can be assessed. The work movement motifs can be recognized with pattern mining. The mined results can be used to further improve the production processes in terms of work procedures, movement symmetry, body utilization, and other ergonomics factors for both short and long-term human resource development. The promising result motivates further utilization of easy-to-adopt technology in Industry 5.0, which facilitates human-centric data-driven improvements.
Post date 12 September 2023
The human-centric Industry 5.0 collaboration architecture
The human-centric Industry 5.0 collaboration architecture
While the primary focus of Industry 4.0 revolves around extensive digitalization, Industry 5.0, on the other hand, seeks to integrate innovative technologies with human actors, signifying an approach that is more value-driven than technology-centric. The key objectives of the Industry 5.0 paradigm, which were not central to Industry 4.0, underscore that production should not only be digitalized but also resilient, sustainable, and human-centric. This paper is focusing on the human-centric pillar of Industry 5.0. The proposed methodology addresses the need for a human-AI collaborative process design and innovation approach to support the development and deployment of advanced AI-driven co-creation and collaboration tools. The method aims to solve the problem of integrating various innovative agents (human, AI, IoT, robot) in a plant-level collaboration process through a generic semantic definition, utilizing a time event-driven process. It also encourages the development of AI techniques for human-in-the-loop optimization, incorporating cross-checking with alternative feedback loop models. Benefits of this methodology include the Industry 5.0 collaboration architecture (I5arc), which provides new adaptable, generic frameworks, concepts, and methodologies for modern knowledge creation and sharing to enhance plant collaboration processes.
The I5arc aims to investigate and establish a truly integrated human-AI collaboration model, equipped with methods and tools for human-AI driven co-creation.
Provide a framework for the co-execution of processes and activities, with humans remaining empowered and in control.
The framework primarily targets human-AI collaboration processes and activities in industrial plants, with potential applicability to other societal contexts.
Post date: 18 June 2023
Challenges of the Fourth Industrial Revolution in HRM
Challenges of the Fourth Industrial Revolution in HRM
As a result of the changes caused by Industry 4.0 and Industry 5.0, unknown or less prominent challenges will be the focus of the operation of organizations and will essentially transform current human resource management (HRM) and its framework and tools. This research aims to identify Industry 4.0 solutions and expected changes in the field of human resources (HR) and for employees and to outline emerging trends of Industry 4.0 that impact HR based on interviews with surveyed companies and a review of the relevant literature. Structured interviews were conducted in this research. After individually processing the responses of each interviewee, the responses were formulated by considering all interviews. This research points out that in terms of HR, recruitment and training are being most affected by the fourth industrial revolution, and changes in competencies and their development processes have begun. Hopefully, the discovered connections will inspire further research and provide useful information on the fields of Industry 4.0 and HR.
Our review about operators and cyber-physical systems is published!
Our review about operators and cyber-physical systems is published!
Enabling Technologies for Operator 4.0: A Survey
Enabling Technologies for Operator 4.0: A Survey
The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space.
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