Predicting Office Workers' Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators.
Predicting Office Workers' Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators.
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预测办公室工作人员的生产力：一种整合生理，行为和心理指标的机器学习方法。
DOI：
10.3390/s23218694
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发表时间：
2023-10-25
期刊：
影响因子：
--
通讯作者：
Roll SC
中科院分区：
文献类型：
--
作者：
Awada M;Becerik-Gerber B;Lucas G;Roll SC
这项研究开创了机器学习框架的应用，利用生理、行为和心理特征来预测办公室工作人员的感知生产力。对两种方法进行了比较：基线模型，根据生理和行为特征预测生产力，以及扩展模型，结合对心理状态（如压力、正常压力、痛苦和情绪）的预测。利用各种机器学习模型进行比较，以评估其对心理状态和生产力的预测准确性，其中XGBoost表现最佳。扩展模型优于基线模型，实现了0.60的R2和10.52的较低MAE，而基线模型的R2为0.48，MAE为16.62。扩展模型的特征重要性分析揭示了对生产力关键预测因素的有价值的见解，揭示了心理状态在预测过程中的作用。值得注意的是，情绪和压力是生产力的重要预测因素。生理和行为特征，包括皮肤温度，皮肤电活动，面部运动，手腕加速度，也被确定。最后，一项比较分析显示，可穿戴设备（Empatica E4和H10 Polar）在预测生产力方面优于工作站插件（Kinect摄像头和计算机使用监控应用程序），强调了可穿戴设备作为评估生产力的独立工具的潜在效用。在智能工作站中实施该模型可以提供适应性强的环境，从而提高办公室工作人员的生产力和整体健康水平。
This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model’s R2 of 0.48 and MAE of 16.62. The extended model’s feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers.
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DOI：
10.3390/s23177378
发表时间：
2023-08-24
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Alhejaili R;Alomainy A
通讯作者：
Alomainy A
影响因子：
11.2
作者：
Koldijk, Saskia;Neerincx, Mark A.;Kraaij, Wessel
通讯作者：
Kraaij, Wessel
DOI：
10.3233/wor-210301
发表时间：
2021-01-01
影响因子：
2.3
作者：
Awada, Mohamad;Lucas, Gale;Roll, Shawn
通讯作者：
Roll, Shawn
影响因子：
7.4
作者：
Cha, Seung Hyun;Seo, Joonoh;Koo, Choongwan
通讯作者：
Koo, Choongwan
影响因子：
3.9
作者：
Di Fabio, Annamaria;Peiro, Jose Maria;Kozusznik, Malgorzata Wanda
通讯作者：
Kozusznik, Malgorzata Wanda