Development of Machine Learning Model for Classification of No Specific Chronic Lower Back Patients from Healthy Patients Usin Spinal Kinematic Data Through Motion Capture (MOCAP) / Assad Ali Kazim
Material type:
TextIslamabad : SMME- NUST; 2025Description: 70p. Soft Copy 30cmSubject(s): MS Biomedical Engineering (BME)DDC classification: 610 Online resources: Click here to access online
| Item type | Current location | Home library | Shelving location | Call number | Status | Date due | Barcode | Item holds |
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Thesis
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School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 610 (Browse shelf) | Available | SMME-TH-1205 |
Non-specific lower back pain remains a global physiological disability surpassing
pathological diseases. Its diagnosis through modern machines are expensive and frequent
doses of radiation lead to deterioration of body cells. Recently with development of allied
technologies such as Motion Capture (MoCap) can evaluate skeletal motion of the patient.
This technology vastly used in cinematography has a hidden usage for diagnosis of patients
with NSLBP. Through various sampling of motion and effective utilisation of Machine
Learning, we can classify a healthy patient from NSLBP patient. Various supervised
learning models such as Scalar Vector Machine (SVM), Random Forest (RF), XGBoost
and ANN have shown promising results which reflects that such AI tools can predict
patients having NSLBP. Effective utilization can lead to exact determination of location
where said problem is being developed in the patient through various motion examinations.
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