The rapidly expanding number of IoT-based camera devices makes smart work packaging (SWP) easier to access massive construction workers' personal image information for occupational health and safety (OHS) status monitoring. SWP can then transmit these personal data to the cloud for training the machine learning models and offer safety alerts or health insights. However, there are two urgently important challenges. Firstly, the machine learning model needs to aggregate the SWPs' image data from each construction worker, which may pose a risk to private data leakage without strict privacy and security agreement. In addition, the machine learning models trained on all SWPs' image data may compromise the personalization of image-based OHS status monitoring for each construction worker. To address the above issues, this study proposes a FedSWP framework, the federated transfer learning-enabled SWP for protecting the personal image information of construction workers in OHS management. FedSWP executes the gradient parameters aggregation through federated learning for the image data in each SWP and builds relatively personalized models by transfer learning. Crane operators' facial fatigue monitoring experiments are conducted and have evaluated that FedSWP can achieve accurate and personalized safety alerts and healthcare. This study paves the way for the generalization and extension of FedSWP in many construction OHS applications.

基于物联网的摄像头设备的数量迅速增长，使得智能工作包装（SWP）可以更轻松地访问大量建筑工人的个人图像信息，以进行职业健康与安全（OHS）状态监控。然后，SWP可以将这些个人数据传输到云中，以训练机器学习模型并提供安全警报或健康见解。但是，存在两个紧迫的重要挑战。首先，机器学习模型需要汇总来自每个建筑工人的SWP图像数据，如果没有严格的隐私和安全协议，可能会对私人数据泄漏造成风险。此外，在所有SWP图像数据上训练的机器学习模型可能会损害针对每个建筑工人的基于图像的OHS状态监视的个性化。为了解决上述问题，这项研究提出了一个FedSWP框架，该框架是支持联邦转移学习的SWP，用于在OHS管理中保护建筑工人的个人形象信息。FedSWP通过联合学习对每个SWP中的图像数据执行梯度参数聚合，并通过转移学习构建相对个性化的模型。进行了起重机操作员的面部疲劳监测实验，并评估了FedSWP可以实现准确且个性化的安全警报和医疗保健。这项研究为在许多建筑OHS应用中推广和扩展FedSWP铺平了道路。FedSWP通过联合学习对每个SWP中的图像数据执行梯度参数聚合，并通过转移学习构建相对个性化的模型。进行了起重机操作员的面部疲劳监测实验，并评估了FedSWP可以实现准确且个性化的安全警报和医疗保健。这项研究为在许多建筑OHS应用中推广和扩展FedSWP铺平了道路。FedSWP通过联合学习对每个SWP中的图像数据执行梯度参数聚合，并通过转移学习构建相对个性化的模型。进行了起重机操作员的面部疲劳监测实验，并评估了FedSWP可以实现准确且个性化的安全警报和医疗保健。这项研究为在许多建筑OHS应用中推广和扩展FedSWP铺平了道路。