论文标题
在建筑中应用渐进的深神网络姿势识别模型来评估伤害风险评估
Applying Incremental Deep Neural Networks-based Posture Recognition Model for Injury Risk Assessment in Construction
论文作者
论文摘要
监测尴尬的姿势是建筑中肌肉骨骼疾病（MSD）的积极预防。机器学习（ML）模型已显示出可穿戴传感器的姿势识别的有希望的结果。但是，需要进行进一步的研究：i）增量学习（IL），训练有素的模型适应新的姿势并控制遗忘学习的姿势； ii）MSDS评估具有公认的姿势。这项研究提出了一个增量卷积长的短期记忆（CLN）模型，研究了有效的IL策略，并使用公认的姿势评估了MSDS评估。与九名工人进行的测试显示了具有浅卷积层的CLN模型，在个性化（0.87）和广义（0.84）建模下实现了高识别性能（F1分数）。在多对一IL方案下，广义的浅层CLN模型可以平衡适应性（0.73）和忘记学习的受试者（0.74）。使用从增量CLN模型认识到的姿势的MSDS评估与地面真相的差异很小，这证明了自动化自动化的MSD构造中的高潜力。
Monitoring awkward postures is a proactive prevention for Musculoskeletal Disorders (MSDs)in construction. Machine Learning (ML) models have shown promising results for posture recognition from Wearable Sensors. However, further investigations are needed concerning: i) Incremental Learning (IL), where trained models adapt to learn new postures and control the forgetting of learned postures; ii) MSDs assessment with recognized postures. This study proposed an incremental Convolutional Long Short-Term Memory (CLN) model, investigated effective IL strategies, and evaluated MSDs assessment using recognized postures. Tests with nine workers showed the CLN model with shallow convolutional layers achieved high recognition performance (F1 Score) under personalized (0.87) and generalized (0.84) modeling. Generalized shallow CLN model under Many-to-One IL scheme can balance the adaptation (0.73) and forgetting of learnt subjects (0.74). MSDs assessment using postures recognized from incremental CLN model had minor difference with ground-truth, which demonstrates the high potential for automated MSDs monitoring in construction.
