An intelligent web-based system with an analytical dashboard to monitor employee mental workload for better decision-making
Bhashitha Wendakoon Mudiyanselage, Kavishwa (2024-06-13)
K. Bhashitha Wendakoon Mudiyanselage
13.06.2024
© 2024, Kavishwa Bhashitha Wendakoon Mudiyanselage. Tämä Kohde on tekijänoikeuden ja/tai lähioikeuksien suojaama. Voit käyttää Kohdetta käyttöösi sovellettavan tekijänoikeutta ja lähioikeuksia koskevan lainsäädännön sallimilla tavoilla. Muunlaista käyttöä varten tarvitset oikeudenhaltijoiden luvan.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202406184660
https://urn.fi/URN:NBN:fi:oulu-202406184660
Tiivistelmä
In today's fast-paced and demanding work environments, monitoring and managing employee mental workload is crucial for ensuring productivity, well-being, and overall organizational success. This research introduces the Neuro-Behavioral Workload Assessment System (NBWAS), a novel framework combining physiological, behavioral, and subjective measures to provide a comprehensive and quantifiable mental workload assessment. NBWAS leverages electroencephalography (EEG) data, task performance metrics, and self-reported workload ratings to generate real-time insights into employee cognitive load.
A web-based analytical dashboard was developed to visualize and interpret the workload data collected through NBWAS. This dashboard empowers employers to monitor individual and collective workload levels, identify potential burnout risks, and make informed decisions regarding workload distribution and resource allocation. Integrating a deep learning model trained on company-specific data enables the dashboard to offer personalized recommendations to employees experiencing high mental workloads, facilitating timely interventions and support.
The research findings demonstrate the effectiveness of the NBWAS framework in accurately assessing mental workload and the dashboard's utility in providing actionable insights to employers. User feedback and testing results highlight the system's user- friendly interface and its potential to positively impact employee well-being and organizational productivity.
This research contributes to the growing knowledge of mental workload assessment and management. The NBWAS framework and the analytical dashboard represent valuable tools for organizations seeking to create a healthier, more productive, data-driven work environment. Future research directions include expanding the applicability of NBWAS to diverse industries and job roles, incorporating additional physiological and behavioral measures, and enhancing the dashboard's personalization and adaptability features.
A web-based analytical dashboard was developed to visualize and interpret the workload data collected through NBWAS. This dashboard empowers employers to monitor individual and collective workload levels, identify potential burnout risks, and make informed decisions regarding workload distribution and resource allocation. Integrating a deep learning model trained on company-specific data enables the dashboard to offer personalized recommendations to employees experiencing high mental workloads, facilitating timely interventions and support.
The research findings demonstrate the effectiveness of the NBWAS framework in accurately assessing mental workload and the dashboard's utility in providing actionable insights to employers. User feedback and testing results highlight the system's user- friendly interface and its potential to positively impact employee well-being and organizational productivity.
This research contributes to the growing knowledge of mental workload assessment and management. The NBWAS framework and the analytical dashboard represent valuable tools for organizations seeking to create a healthier, more productive, data-driven work environment. Future research directions include expanding the applicability of NBWAS to diverse industries and job roles, incorporating additional physiological and behavioral measures, and enhancing the dashboard's personalization and adaptability features.
Kokoelmat
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