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) / (Record no. 615942)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01608nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kazim, Assad Ali |
| 245 ## - TITLE STATEMENT | |
| Title | 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) / |
| Statement of responsibility, etc. | Assad Ali Kazim |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 70p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Non-specific lower back pain remains a global physiological disability surpassing<br/>pathological diseases. Its diagnosis through modern machines are expensive and frequent<br/>doses of radiation lead to deterioration of body cells. Recently with development of allied<br/>technologies such as Motion Capture (MoCap) can evaluate skeletal motion of the patient.<br/>This technology vastly used in cinematography has a hidden usage for diagnosis of patients<br/>with NSLBP. Through various sampling of motion and effective utilisation of Machine<br/>Learning, we can classify a healthy patient from NSLBP patient. Various supervised<br/>learning models such as Scalar Vector Machine (SVM), Random Forest (RF), XGBoost<br/>and ANN have shown promising results which reflects that such AI tools can predict<br/>patients having NSLBP. Effective utilization can lead to exact determination of location<br/>where said problem is being developed in the patient through various motion examinations. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Asim Waris |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/57255">http://10.250.8.41:8080/xmlui/handle/123456789/57255</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 01/26/2026 | 610 | SMME-TH-1205 | Thesis |