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Ergonomics. 2018 Jul 3:1-9. doi: 10.1080/00140139.2018.1481230. [Epub ahead of print]

Using a deep learning network to recognise low back pain in static standing.

Author information

1
a Department of Environmental Health , Harvard T.H. Chan School of Public Health , Boston , MA , USA.
2
b Department of Neurosurgery - Pain Division, School of Medicine , West Virginia University , Morgantown , WV , USA.
3
c The Ergonomics Laboratory, Department of Industrial and Management Systems Engineering , West Virginia University , Morgantown , WV , USA.
4
d Edward P. Fitts Department of Industrial and Systems Engineering , North Carolina State University , Raleigh , NC , USA.

Abstract

Low back pain (LBP) remains one of the most prevalent musculoskeletal disorders, while algorithms that able to recognise LBP patients from healthy population using balance performance data are rarely seen. In this study, human balance and body sway performance during standing trials were utilised to recognise chronic LBP populations using deep neural networks. To be specific, 44 chronic LBP and healthy individuals performed static standing tasks, while their spine kinematics and centre of pressure were recorded. A deep learning network with long short-term memory units was used for training, prediction and implementation. The performance of the model was evaluated by: (a) overall accuracy, (b) precision, (c) recall, (d) F1 measure, (e) receiver-operating characteristic and (f) area under the curve. Results indicated that deep neural networks could recognise LBP populations with precision up to 97.2% and recall up to 97.2%. Meanwhile, the results showed that the model with the C7 sensor output performed the best. Practitioner summary: Low back pain (LBP) remains the most common musculoskeletal disorder. In this study, we investigated the feasibility of applying artificial intelligent deep neural network in detecting LBP population from healthy controls with their kinematics data. Results showed a deep learning network can solve the above classification problem with both promising precision and recall performance.

KEYWORDS:

Low back pain; balance control; deep neural network; long-short-term memory; motion analysis

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