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Monitoring awkward postures is a proactive prevention for MusculoskeletalDisorders (MSDs)in construction. Machine Learning (ML) models have shownpromising 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 oflearned postures; ii) MSDs assessment with recognized postures. This studyproposed an incremental Convolutional Long Short-Term Memory (CLN) model,investigated effective IL strategies, and evaluated MSDs assessment usingrecognized postures. Tests with nine workers showed the CLN model with shallowconvolutional layers achieved high recognition performance (F1 Score) underpersonalized (0.87) and generalized (0.84) modeling. Generalized shallow CLNmodel under Many-to-One IL scheme can balance the adaptation (0.73) andforgetting of learnt subjects (0.74). MSDs assessment using postures recognizedfrom incremental CLN model had minor difference with ground-truth, whichdemonstrates the high potential for automated MSDs monitoring in construction.

Applying Incremental Deep Neural Networks-based Posture Recognition Model for Injury Risk Assessment in Construction