  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.
