AUTHOR=Wang Ziheng , Sato Keizo , Nawrin Saida Salima , Widatalla Namareq Salah , Kimura Yoshitaka , Nagatomi Ryoichi 

TITLE=Low Back Pain Exacerbation Is Predictable Through Motif Identification in Center of Pressure Time Series Recorded During Dynamic Sitting

JOURNAL=Frontiers in Physiology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2021.696077

DOI=10.3389/fphys.2021.696077

ISSN=1664-042X

ABSTRACT=Background: Low back pain (LBP) is a common health problem-sitting on a chair for a considerable time is considered a significant risk factor. Furthermore, the level of pain may vary at different times of the day. However, the role of the time-sequence property of sitting behavior in relation to LBP has not been considered. During the dynamic sitting, small changes, such as slight or big sways, have been identified. Therefore, it is possible to identify the motif consisting of such changes, which may be associated with the incidence, exacerbation, or improvement of LBP. Method: Office chairs installed with load cells were provided to the office workers. Data obtained from the load cells of each chair. The participants were asked to answer subjective levels of pain including LBP. COP was calculated from the load level, the changes in COP were analyzed by applying the Toeplitz inverse covariance-based clustering (TICC) analysis, COP changes were categorized into several states. Based on the states, common motifs were identified as a characteristic combination of different states by motif-aware state assignment (MASA). Finally, the identified motif was tested as a feature to infer the changing levels of LBP within a day. Changes in the levels of LBP from morning to evening were categorized as worse, no change, or better. The specificity and sensitivity of the LBP inference were compared among ten different models, including SSA-PNN. Result: There exists a common motif, consisting of stable sitting and slight sway. When LBP improved towards the evening, the frequency of motif appearance was higher than that of worsening LBP (p < 0.05) or when there was no change. The performance of the SSA-PNN optimization was better than that of the other algorithms. Accuracy, sensitivity, recall, andF1-score were 68.89%, 77.66%, 51.45%(lower than Ridge classifier, 52.78%), and 71.33%, respectively. Conclusion: A lower frequency of a common motif of the COP dynamic changes characterized by stable sitting and slight sway was found to be associated with the exacerbation of LBP in the evening. LBP exacerbation is predictable by AI-based analysis of COP changes during the sitting behavior of the office workers.