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A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

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Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran
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Physiotherapy Research Center, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran 1616913111, Iran
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Physical Therapy Department, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz 7194733669, Iran
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Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences, Shiraz 7194733669, Iran
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Department of Physical Therapy, University of Social Welfare and Rehabilitation Sciences, Tehran 1985713871, Iran
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Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
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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(10), 2902; https://doi.org/10.3390/s20102902
Received: 28 April 2020 / Revised: 12 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
(This article belongs to the Section Biomedical Sensors)
The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart “sensor-based” practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today’s smartphones, as objective tools for various health applications towards personalized precision medicine. View Full-Text
Keywords: wearable inertial sensor; low back pain (LBP) classification; clinical settings; quantitative screening wearable inertial sensor; low back pain (LBP) classification; clinical settings; quantitative screening
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Davoudi, M.; Shokouhyan, S.M.; Abedi, M.; Meftahi, N.; Rahimi, A.; Rashedi, E.; Hoviattalab, M.; Narimani, R.; Parnianpour, M.; Khalaf, K. A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings. Sensors 2020, 20, 2902.

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