リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Low back pain exacerbation is predictable through motif identification in center of pressure time series recorded during dynamic sitting」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Low back pain exacerbation is predictable through motif identification in center of pressure time series recorded during dynamic sitting

WANG ZIHENG 東北大学

2021.09.24

概要

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 sway, 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.

Purpose: To identify motifs associated with the exacerbation of self-reported LBP by continuously measuring the center of pressure (COP) during sitting behavior of office workers that enables prediction of LBP exacerbation.

Methods: Office chairs installed with pressure sensors to a total of 22 office workers (age = 43.4 ± 8.3 years) in Japan. Pressure sensor data were collected during working days and hours (from morning to evening). 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 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, I 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 improved towards the evening, the frequency of motif appearance was higher than both LBP exacerbated (p < 0.05) and 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 59.2%, 72.5%, 40.9%, and 63.2%, 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.

この論文で使われている画像

参考文献

1. Boisson M, Borderie D, Henrotin Y, et al.: Serum biomarkers in people with chronic low back pain and Modic 1 changes: a case-control study. Scientific Reports 2019; 9:1–5.

2. Hoy D, Bain C, Williams G, et al.: A systematic review of the global prevalence of low back pain. Arthritis Rheumatism 2012; 64:2028–2037.

3. Kamper SJ, Henschke N, Hestb Ae K L, et al.: Musculoskeletal pain in children and adolescents. Brazilian Journal of Physical Therapy 2011; 20:275–284.

4. Swain M, Henschke N, Kamper S, et al.: An international survey of pain in adolescents. BMC Public Health 2014; 14:1–7.

5. Koes B, Van Tulder M, Thomas S: Diagnosis and treatment of low back pain. BMJ 2006; 332:1430–1434.

6. National Center for Health Statistics (US). Health, United States, 2013: With Special Feature on Prescription Drugs. Hyattsville (MD): National Center for Health Statistics (US); 2014;Report No.: 2014-1232.

7. Airaksinen O, Brox J, Cedraschi C, et al.: European guidelines for the management of chronic nonspecific low back pain. European Spine Journal 2006; 15:s192–s300.

8. Hartvigsen J, Hancock MJ, Kongsted A, et al.: What low back pain is and why I need to pay attention. Lancet 2018; 391:2356–2367.

9. Schofield DJ, Shrestha RN, Passey ME, et al.: Chronic disease and labour force participation among older Australians. The Medical Journal of Australia 2008; 189:447– 450.

10. Deborah, J, Schofield, et al.: Back problems, comorbidities, and their association with wealth. The Spine Journal 2015; 15:34–41.

11. Schofield DJ, Shrestha RN, Percival R, et al.: Early retirement and the financial assets of individuals with back problems. European Spine Journal 2011; 20:731–736.

12. Brinjikji W, Diehn FE, Jarvik JG, et al.: MRI Findings of disc degeneration are more prevalent in adults with low back pain than in asymptomatic controls: A systematic review and meta-analysis. American Journal of Neuroradiology 2015; 36:2394–2399.

13. Balagué F, Mannion AF, Pellisé F, et al.: Non-specific low back pain. Lancet 2012; 379:482–491.

14. Maher C, Underwood M, Buchbinder R: Non-specific low back pain. Lancet 2017; 389:736–747.

15. Brinjikji W, Luetmer PH, Comstock B, et al.: Systematic literature review of imaging features of spinal degeneration in asymptomatic populations. American Journal of Neuroradiology 2015; 36:811–816.

16. Deyo RA, Korff MV, Duhrkoop D: Opioids for low back pain. BMJ 2015; 350:g6380.

17. d’Hemecourt PA, Gerbino II PG, Micheli LJ: Back injuries in the young athlete. Clinical Journal of Sport Medicine 2000; 19:663–679.

18. Chou R, Shekelle P: Will this patient develop persistent disabling low back pain? JAMA 2010; 303:1295–1302.

19. Vos T, Abajobir AA, Abate KH, et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study. Lancet 2017; 390:1211–1259.

20. Eifell R, Ashour H, Heslop PS, et al.: Association of 24-hour activity levels with the clinical severity of chronic venous disease. Journal of Vascular Surgery 2006; 44:580–587.

21. Baker R, Coenen P, Howie E, et al.: The short term musculoskeletal and cognitive effects of prolonged sitting during office computer work. International Journal of Environmental Research Public Health 2018; 15:1678.

22. Nidhi G, Stordal CC, Hallman DM, et al.: Is objectively measured sitting time associated with low back pain? A cross-sectional investigation in the NOMAD study. Plos One 2015; 10:e0121159.

23. Anne F, Walker JM: Short-term effects of workstation exercises on musculoskeletal discomfort and postural changes in seated video display unit workers. Physical Therapy 2002; 578–589.

24. Pate R, O’Neill J, Lobelo F: The evolving definition of "sedentary". Exercise and Sport Sciences Reviews 2008; 36:173–178.

25. Leitzmann MF, Jochem C, Schmid D: Sedentary behavior epidemiology. Springer2017 first edition;73-106.

26. Bennie JA, Chau JY, Ploeg H, et al.: The prevalence and correlates of sitting in European adults - a comparison of 32 Eurobarometer-participating countries. The International Journal of Behavioral Nutrition and Physical Activity 2013; 10:107.

27. Clemes SA, O’Connell SE, Edwardson CL: Office workers’ objectively measured sedentary behavior and physical activity during and outside working hours. Journal of Occupational Environmental Medicine 2014; 56:298–303.

28. Thorp AA, Healy GN, Winkler E, et al.: Prolonged sedentary time and physical activity in workplace and non-work contexts: a cross-sectional study of office, customer service and call centre employees. The International Journal of Behavioral Nutrition and Physical Activity 2012; 9:128.

29. Kurita S, Shibata A, Ishii K, et al.: Patterns of objectively assessed sedentary time and physical activity among Japanese workers: a cross-sectional observational study. BMJ Open 2019; 9:e021690.

30. Inoue G, Miyagi M, Uchida K, et al.: The prevalence and characteristics of low back pain among sitting workers in a Japanese manufacturing company. Journal of Orthopaedic Science 2015; 20:23–30.

31. Chen SM, Liu MF, Cook J, et al.: Sedentary lifestyle as a risk factor for low back pain: a systematic review. International Archives of Occupational and Environmental Health 2009; 82:797–806.

32. Kwon BK, Roffey DM, Bishop PB, et al.: Systematic review: occupational physical activity and low back pain. Occupational Medicine 2011; 61:541–548.

33. Harkness E, MacFarlane GJ, Nahit E, et al.: Risk factors for new-onset low back pain amongst cohorts of newly employed workers. Rheumatology 2003; 42:959-968.

34. Yip VYB: New low back pain in nurses: work activities, work stress and sedentary lifestyle. Journal of advanced nursing 2004; 46:430-440.

35. Liszka-Hackzell JJ, Martin DP: Categorization and analysis of pain and activity in patients with low back pain using a neural network technique. Journal of Medical Systems 2002; 26:337–347.

36. Gal N, Stoicu-Tivadar V, Andrei D, et al.: Computer assisted treatment prediction of low back pain pathologies. Studies in health technology and informatics 2014; 197:47–51.

37. Mohammed AK: FNDSB: A fuzzy-neuro decision support system for back pain diagnosis. Cognitive Systems Research 2018; 52:691–700.

38. Ashouri S, Abedi M, Abdollahi M, et al.: A novel approach to spinal 3-D kinematic assessment using inertial sensors: Towards effective quantitative evaluation of low back pain in clinical settings. Computers in Biology Medicine 2017; 89:144–149.

39. Caza-Szoka M, Massicotte D, Nougarou F, et al.: Surrogate analysis of fractal dimensions from SEMG sensor array as a predictor of chronic low back pain. in International Conference of the IEEE Engineering in Medicine Biology Society 2016; 6409–6412.

40. Hu B, Kim C, Ning X, et al.: Using a deep learning network to recognize low back pain in static standing. Ergonomics 2018; 61:1374–1381.

41. Hung CC, Shen TW, Liang CC, et al.: Using surface electromyography (SEMG) to classify low back pain based on lifting capacity evaluation with principal component analysis neural network method. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2014 18–21.

42. Magnusson ML, Bishop JB, Hasselquist L, et al.: Range of motion and motion patterns in patients with low back pain before and after rehabilitation. Spine 1998; 23:2631–2639.

43. Oliver CW, Atsma WJ: Artificial intelligence analysis of paraspinal power spectra. Clinical Biomechanics 1996; 11:422– 424.

44. Caza-Szoka M, Massicotte D, Nougarou F: Naive Bayesian learning for small training samples: Application on chronic low back pain diagnostic with sEMG sensors. in Instrumentation and Measurement Technology Conference (I2MTC), 2015; 470–475.

45. Du W, Omisore OM, Li H, et al.: Recognition of chronic low back pain during lumbar spine movements based on surface electromyography signals. IEEE Access 2018; 6:65027– 65042.

46. Olugbade TA, Bianchi-Berthouze N, Marquardt N, et al.: Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain. in International Conference on Affective Computing Intelligent Interaction 2015 243–249.

47. Boerema ST, Velsen LV, Vollenbroek M, et al.: Pattern measures of sedentary behavior in adults: A literature review. Digital Health 2020; 6:1–13.

48. O’Sullivan K, O’Keeffe M, O’Sullivan L, et al.: The effect of dynamic sitting on the prevention and management of low back pain and low back discomfort: A systematic review. Ergonomics 2012; 55:898–908.

49. Cheng J, Zhou B, Sundholm M, et al.: Smart chair: What can simple pressure sensors under the Chairs’ Legs Tell Us about User Activity? UBICOMM13: The Seventh International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies 2013; 81–84.

50. Griffiths E, Saponas TS, Brush A: Health chair: Implicitly sensing heart and respiratory rate. Association for Computing Machinery 2014; 661–671.

51. Huang M, Gibson I, Yang R: Smart chair for monitoring of sitting behavior. KnE Engineering 2017; 274–280.

52. Makhsous M, Fang L, Bankard J, et al.: Biomechanical effects of sitting with adjustable ischial and lumbar support on occupational low back pain: Evaluation of sitting load and back muscle activity. BMC Musculoskeletal Disorders 2009; 10:1–11.

53. Sondergaard K, Olesen CG, Sondergaard EK, et al.: The variability and complexity of sitting postural control are associated with discomfort. Journal of Biomechanics 2010; 43:1997–2001.

54. O’Sullivan K, O’Sullivan P, O’Keeffe M, et al.: The effect of dynamic sitting on trunk muscle activation: A systematic review. Applied Ergonomics 2013; 44:628–635.

55. Chun-Ting, Li, Yen-Nien, et al.: The effects of backward adjustable thoracic support in wheelchair on spinal curvature and back muscle activation for elderly people. PloS one 2014; 9:e113644.

56. Dunk NM, Callaghan JP: Gender-based differences in postural responses to seated exposures. Clinical Biomechanics 2005; 20:1101–1110.

57. Akkarakittichoke N, Janwantanakul P: Seat pressure distribution characteristics during 1 hour sitting in office workers with and without chronic low back pain. Safety and Health at Work 2017; 8:212–219.

58. Leonardi F, Bühlmann P: Computationally efficient change point detection for high- dimensional regression. arXiv preprint arXiv:160103704 2016.

59. Nystrup P, Madsen H, Lindstrm E: Long memory of financial time series and hidden markov models with time-varying parameters. Journal of Forecasting 2016; 36:989–1002.

60. Clarkson B, Mase K, Pentland A: Recognizing user context via wearable sensors. Iswc Proceedings 2000; 69–75.

61. Hallac D, Nystrup P, Boyd S: Greedy gaussian segmentation of multivariate time series. Advances in Data Analysis and Classification 2019; 13:727–751.

62. Hallac D, Vare S, Boyd S, et al.: Toeplitz inverse covariance-based clustering of multivariate time series data. Association for Computing Machinery 2017; 215–223.

63. Jain S, Hallac D, Sosic R, et al.: MASA: Motif-aware state assignment in noisy time series data. arXiv preprint arXiv 2018; 1809.01819.

64. Yankov D, Keogh E, Medina J, et al.: Detecting motifs under uniform scaling. Association for Computing Machinery 2007; 844–853.

65. Specht DF: Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification. IEEE Transactions on Neural Networks 1990; 1:111–121.

66. Georgiou VL, Alevizos PD, Vrahatis MN: Novel approaches to probabilistic neural networks through bagging and evolutionary estimating of prior probabilities. Neural Processing Letters 2008; 27:153–162.

67. Zeinali Y, Story B: Competitive probabilistic neural network. Integrated Computer Aided Engineering 2017; 24:105–118.

68. Kusy M, Zajdel R: Application of reinforcement learning algorithms for the adaptive computation of the smoothing parameter for probabilistic neural network. IEEE Transactions On Neural Networks and Learning Systems 2014; 26:2163– 2175.

69. Lippman RP, Moody JE, Touretzky DS: Advances in Neural Information Processing Systems 3. Morgan Kaufmann 1991; 26–29.

70. Dorigo M, Birattari M, Stutzle T: Ant colony optimization. IEEE Computational Intelligence Magazine 2006; 1:28–39.

71. Kennedy J: Particle swarm optimization. Proc of 1995 IEEE Int Conf Neural Networks 2011; 4:1942–1948.

72. Hajela P: Genetic search-an approach to the nonconvex optimization problem. AIAA Journal 1990; 28:1205–1210.

73. Mallipeddi R, Suganthan PN, Pan QK, et al.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing 2011; 11:1679– 1696.

74. Yu J, Li V: A Social spider algorithm for global optimization. Applied Soft Computing Journal 2015; 30:614–627.

75. Grimmer K, Williams M: Gender-age environmental associates of adolescent low back pain. Applied Ergonomics 2000; 31:343–360.

76. Kelly G, Blake C, Power C, et al.: The association between chronic low back pain and sleep: a systematic review. Clinical Journal of Pain 2011; 27:169–181.

77. Miranda H, Viikari-Juntura E, Punnett L, et al.: Occupational loading, health behavior and sleep disturbance as predictors of low-back pain. Clinical Journal of Pain 2008; 34:411– 419.

78. Alsaadi S, McAuley J, Hush J, et al.: Prevalence of sleep disturbance in patients with low back pain. European Spine Journal 2010; 20:554–560.

79. Kristjansdottir G, Rhee H: Risk factors of back pain frequency in schoolchildren: A search for explanations to a public health problem. Acta Paediatrica 2002; 91:849–854.

80. Dieen JHV, Looze MPD, Hermans V: Effects of dynamic office chairs on trunk kinematics, trunk extensor EMG and spinal shrinkage. Ergonomics 2001; 44:739–750.

81. Pynt J, Higgs J, Mackey M: Seeking the optimal posture of the seated lumber spine. Theory and Practice 2001; 17:5–21.

82. Nairn BC, Azar NR, Drake J: Transient pain developers show increased abdominal muscle activity during prolonged sitting. Journal of Electromyography and Kinesiology 2013; 23:1421–1427.

83. Reenalda J, Van Geffen P, Nederhand M, et al.: Analysis of healthy sitting behavior: interface pressure distribution and subcutaneous tissue oxygenation. Journal of Rehabilitation Research and Development 2009; 46:577–586.

84. Chan SC, Ferguson SJ, Gantenbein-Ritter B: The effects of dynamic loading on the intervertebral disc. European Spine Journal 2011; 20:1796–1812.

85. Li CT, Chen CH, Chen YN, et al.: Biomechanical evaluation of a novel wheelchair backrest for elderly people. BioMedical Engineering OnLine 2015; 14:1–10.

86. Misir A, Kizkapan TB, Tas SK, et al.: Lumbar spine posture and spinopelvic parameters change in various standing and sitting postures. European Spine Journal 2019; 28:1072– 1081.

87. Panjabi M: The stabilizing system of the spine. Part I. Function, dysfunction, adaptation, and enhancement. Journal of Spinal Disorders 1992; 5:383.

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る