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Open AccessArticle

Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach

1
Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
2
College of Business and Law, RMIT University, Melbourne VIC 3000, Australia
3
Department of Physiotherapy, Physiotherapy Research Center, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran 1616913111, Iran
4
Mechanical Engineering Department, Sharif University of Technology, Tehran 1136511155, Iran
5
Department of Biomedical Engineering and Health Engineering Innovation Center, Khalifa University of Science and Technology, P.O. Box 127788 Abu Dhabi, UAE
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3600; https://doi.org/10.3390/s20123600
Received: 21 May 2020 / Revised: 22 June 2020 / Accepted: 23 June 2020 / Published: 26 June 2020
(This article belongs to the Special Issue Sensor-Based Systems for Kinematics and Kinetics)
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. View Full-Text
Keywords: objective clinical decision-making; wearable systems; trunk kinematics; pattern recognition; classification; STarT back screening tool objective clinical decision-making; wearable systems; trunk kinematics; pattern recognition; classification; STarT back screening tool
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Abdollahi, M.; Ashouri, S.; Abedi, M.; Azadeh-Fard, N.; Parnianpour, M.; Khalaf, K.; Rashedi, E. Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach. Sensors 2020, 20, 3600.

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