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Using a motion sensor to categorize nonspecific low back pain patients: A machine learning approach

Abdollahi, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.3390/s20123600
  3. Publisher: MDPI AG , 2020
  4. Abstract:
  5. Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation. © MDPI AG. All rights reserved
  6. Keywords:
  7. Classification ; Objective clinical decision-making ; Pattern recognition ; STarT back screening tool ; Trunk kinematics ; Wearable systems ; Cost effectiveness ; Diagnosis ; Kinematics ; Learning algorithms ; Motion sensors ; Support vector machines ; Clinical settings ; Health care application ; Inertial measurement unit ; Machine learning approaches ; Machine learning models ; Model development ; Multi layer perceptron ; Socio-economic consequences ; Learning systems
  8. Source: Sensors (Switzerland) ; Volume 20, Issue 12 , 2020 , Pages 1-16
  9. URL: https://www.mdpi.com/1424-8220/20/12/3600