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Work-Spot Preference Estimation Using Machine Learning for Activity-Based Workplaces
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AI
DOI：10.1109/ACCESS.2024.3443392.png)
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Abstract
En 中文
This paper addresses the hypothesis that the preference of a worker toward a work spot depends on attribute information such as her or his age and position and the type of work task on which she or he is working on that day. The system proposed in this paper is designed to estimate work-spot preferences using machine learning (ML) for activity-based workplaces. The system collects a variety of items related to attribute information and work-spot preferences for each work task from individual workers in advance. It constructs ML models using the collected data and estimates the work-spot preference for a worker. We extend the system to improve the accuracy for the case where the estimation is performed with a limited number of samples for training data. To examine the system, this paper presents two types of experiments: with computer graphics (CG) and with a real environment. Through these experiments, we collect data from people and construct ML models for the estimation of work-spot preferences based on attribute information and types of work tasks. The results demonstrate that the proposed system works for estimating the work-spot preference for each individual worker in accordance with the attribute information and the type of work tasks.
Keywords:
Employment
Task analysis
Maximum likelihood estimation
Workstations
Sensors
Privacy
Productivity
Machine learning
Activity-based workplace
work-spot recommendation
work-spot preference
machine learning