A machine learning approach for the design optimization of a multiple magnetic and inertial sensors wearable system for the spine mobility assessment

Abstract

BackgroundRecently, magnetic and inertial measurement units (MIMU) based systems have been applied in the spine mobility assessment; this evaluation is essential in the clinical practice for diagnosis and treatment evaluation. The available systems are limited in the number of sensors, and neither develops a methodology for the correct placement of the sensors, seeking the relevant mobility information of the spine.MethodsThis work presents a methodology for analyzing a system consisting of sixteen MIMUs to reduce the amount of information and obtain an optimal configuration that allows distinguishing between different body postures in a movement. Four machine learning algorithms were trained and assessed using data from the range of motion in three movements (Mov.1—Anterior hip flexion; Mov.2—Lateral trunk flexion; Mov.3—Axial trunk rotation) obtained from 12 patients with Ankylosing Spondylitis.ResultsThe methodology identified the optimal minimal configuration for different movements. The configuration showed good accuracy in discriminating between different body postures. Specifically, it had an accuracy of 0.963 ± 0.021 for detecting when the subject is upright or bending in Mov.1, 0.944 ± 0.038 for identifying when the subject is flexed to the left or right in Mov.2, and 0.852 ± 0.097 for recognizing when the subject is rotated to the right or left in Mov.3.ConclusionsOur results indicate that the methodology developed results in a feasible configuration for practical clinical studies and paves the way for designing specific IMU-based assessment instruments.Trial registration: Study approved by the Local Ethics Committee of the General Hospital of Mexico “Dr. Eduardo Liceaga” (protocol code DI/03/17/471).