Activity recognition of construction equipment is vital for operational productivity and safety analysis. For automated equipment monitoring, many researchers have developed kinematic or visual sensing approaches and found that the two approaches have their own technical advantages and disadvantages in classifying different types of equipment activities. However, since previous methods adopted only one of kinematic or visual sensing, there is a limitation to fully benefit from both approaches, causing difficulty in monitoring construction equipment precisely. Additionally, despite the great potential of data fusion, the hybrid effects of kinematic–visual sensing are still unclear. To fill such knowledge gaps, this study developed a hybrid kinematic–visual sensing approach and investigated its impacts on the recognition of equipment activities. Specifically, a smartphone was installed inside the equipment's cabin, and kinematic and visual data were collected from its built-in sensors, gyroscopes, accelerometers, and cameras. Total 60-min data were collected, and the data were further split into training (40-min) and testing data (20-min). The data were then used to experiment three different models: kinematic, visual, and hybrid models. In the experiments, the average F-score of the hybrid model was 77.4%, whereas those of kinematic and visual models were 61.7% and 72.4%, respectively. These results indicated that the hybrid sensing could improve the recognition performance and monitor construction equipment better than relying only on sole type of data sources. The findings can contribute to more reliable activity recognition and operation analysis of construction equipment, and provide meaningful insights for future research.

建筑设备的活动识别对于运营生产率和安全性分析至关重要。对于自动化设备监控，许多研究人员开发了运动学或视觉传感方法，发现这两种方法在对不同类型的设备活动进行分类时具有各自的技术优缺点。然而，由于先前的方法仅采用运动学或视觉传感中的一种，因此存在局限性，无法从两种方法中充分受益，从而导致难以精确地监视建筑设备。此外，尽管数据融合具有巨大潜力，但是运动学-视觉传感的混合效应仍然不清楚。为了弥补这些知识空白，本研究开发了一种运动学-视觉传感的混合方法，并研究了其对设备活动识别的影响。具体来说，是在设备机舱内安装了智能手机，并从其内置传感器，陀螺仪，加速度计和相机中收集了运动和视觉数据。收集了总共60分钟的数据，并将数据进一步分为训练（40分钟）和测试数据（20分钟）。然后将数据用于实验三种不同的模型：运动学模型，视觉模型和混合模型。在实验中，混合模型的平均F分数为77.4％，而运动模型和视觉模型的平均F分数分别为61.7％和72.4％。这些结果表明，与仅依靠单一类型的数据源相比，混合感测可以改善识别性能并更好地监控建筑设备。这些发现可有助于对建筑设备进行更可靠的活动识别和运行分析，