Abstract Manual load carrying without sufficient rest may cause work-related musculoskeletal disorders (WMSDs) and needs to be monitored at construction sites. While previous studies have been able to predict load-carrying modes using multiple wearable inertial measurement unit (IMU) sensors, wearing multiple sensors obtrudes on workers during various construction tasks. In this context, by using a single IMU sensor, this research proposes an automatic detecting technique for excessive carrying-load (DeTECLoad) to predict load-carrying weights and postures simultaneously. DeTECLoad converts the IMU data into image data using a Gramian Angular Field, and then uses a hybrid Convolutional Neural Network-Long Short-Term Memory to classify load-carrying modes from the image data. DeTECLoad provides 92.46% and 96.33% accuracies for the load-carrying weight and posture classifications, respectively. By exploiting DeTECLoad, a construction worker's excessive load-carrying tasks could be managed in situ, helping to prevent construction site WMSDs.

中文翻译：

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使用具有 Gramian 角场的深度学习网络检测过度承载任务

摘要 没有充分休息的手动负重可能会导致与工作相关的肌肉骨骼疾病 (WMSD)，需要在施工现场进行监测。虽然之前的研究已经能够使用多个可穿戴惯性测量单元 (IMU) 传感器来预测承载模式，但在各种施工任务中，佩戴多个传感器会突出工人。在此背景下，本研究通过使用单个 IMU 传感器，提出了一种超载负载自动检测技术 (DeTECLoad)，可同时预测负载重量和姿势。DeTECLoad 使用 Gramian Angular Field 将 IMU 数据转换为图像数据，然后使用混合卷积神经网络-长短期记忆从图像数据中对承载模式进行分类。DeTECLoad 提供 92.46% 和 96。承载重量和姿势分类的准确率分别为 33%。通过利用 DeTECLoad，可以就地管理建筑工人的过度承重任务，有助于防止建筑工地 WMSD。