背景:调查工作时间的传统方法依赖于员工在工作地点的实际存在。然而,准确识别工作地点的休息时间和区分工作地点以外的远程工作对工时估算提出了挑战。机器学习有可能区分工作和下班时的人与智能手机交互。
目的:在本研究中,我们的目标是开发一种名为“工作模式中的概率”的新方法,它利用人与智能手机的交互模式和相应的GPS位置数据来估计工作时间。
方法:要捕获人与智能手机的交互和GPS位置,我们使用了“员工时间”应用程序,由我们的团队开发,被动地和连续地记录参与者\'屏幕事件,包括通知的时间戳,屏幕打开或关闭事件,和应用程序使用模式。极端梯度增强树被用来将这些相互作用模式转化为概率,而一维卷积神经网络根据先前的序列概率生成连续概率。由此产生的工作模式的可能性使我们能够辨别办公室工作的时期,下班,在工地休息,远程工作。
结果:我们的研究包括121名参与者,总计5503人日(人日代表所有参与者收集和分析数据的累计天数).开发的机器学习模型表现出平均的预测性能,由接收器工作特性曲线下的面积测量,0.915(SD0.064)。使用工作模式中的概率(高于0.5)估计的工作时间明显长于GPS定义的工作时间(平均每天11.2,SD2.8小时)(平均每天10.2,SD2.3小时;P<.001)。这种差异归因于111.6(SD106.4)分钟的远程工作时间高于54.7(SD74.5)分钟的休息时间。
结论:我们新颖的方法,工作模式中的概率,利用人与智能手机的交互模式和机器学习模型来提高工时调查的精度和准确性。通过集成人与智能手机的交互和GPS数据,我们的方法提供了对工作模式的宝贵见解,包括远程工作和休息,在优化工作效率和福祉方面提供潜在的应用。
Traditional methods for investigating work hours rely on an employee\'s physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work.
In this study, we aimed to develop a novel approach called \"probability in work mode,\" which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours.
To capture human-smartphone interactions and GPS locations, we used the \"Staff Hours\" app, developed by our team, to passively and continuously record participants\' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work.
Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes.
Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.