Abstract
Background: Low back pain (LBP) is a common health problem — sitting on a chair for a prolonged time is considered a significant risk factor. Furthermore, the level of LBP 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 pressure sensors were provided to a total of 22 office workers (age = 43.4 ± 8.3 years) in Japan. Pressure sensors data were collected during working days and hours (from morning to evening). The participants were asked to answer subjective levels of pain including LBP. Center of pressure (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 recurring sitting behavior pattern 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 exacerbated, did not change, or improved based on the survey questions. Here, we present a novel approach based on social spider algorithm (SSA) and probabilistic neural network (PNN) for the prediction of LBP. 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 level improved toward the evening, the frequency of motif appearance was higher than when LBP was exacerbated (p < 0.05) or the level did not change. The performance of the SSA-PNN optimization was better than that of the other algorithms. Accuracy, precision, recall, and F1-score were 59.20, 72.46, 40.94, and 63.24%, 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.
Highlights
Low back pain (LBP) is a highly common issue (Boisson et al, 2019) among people of all ages (Kamper et al, 2011; Hoy et al, 2012; Swain et al, 2014), and is generally described as pain, muscle stiffness, or rigidity located below the costal margin and above the lower gluteal folds, with or without leg pain (Koes et al, 2006)
As the figure shows, the common motif consists of state 1 and state 2, and we found that 91.11% (82/90) of days had this motif
We found Recurrent Neural Network (RNN) can learn this motif with accuracy higher than 92% and the motif can be recognized in real-time, this approach solves the problem that Toeplitz inverse covariance-based clustering (TICC) and motif-aware state assignment in noisy time-series data (MASA) take much time and computer memory to run, this finding should help others to find new ways of applying this tech in practice
Summary
Low back pain (LBP) is a highly common issue (Boisson et al, 2019) among people of all ages (Kamper et al, 2011; Hoy et al, 2012; Swain et al, 2014), and is generally described as pain, muscle stiffness, or rigidity located below the costal margin and above the lower gluteal folds, with or without leg pain (sciatica) (Koes et al, 2006). LBP causes the most disability in working-age people worldwide, especially in low-and middle-income countries where informal employment is common, and job-change options are limited (Hartvigsen et al, 2018). As one of the most common chronic health problems, LBP causes more people to leave the workforce than heart disease, diabetes, hypertension, neoplasm, respiratory disease, and asthma combined (Schofield et al, 2008). Older people who retire early because of LBP have approximately 87% less total wealth and income-producing assets than those who remain in full-time employment (Schofield et al, 2011). Low back pain (LBP) is a common health problem — sitting on a chair for a prolonged time is considered a significant risk factor. It is possible to identify the motif consisting of such changes, which may be associated with the incidence, exacerbation, or improvement of LBP
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