Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Benchmarking full-body inertial motion capture for clinical gait analysis
MScEng
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Stellenbosch : University of Stellenbosch
2009
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613977108807680 |
|---|---|
| access_status_str | Open Access |
| author | Cloete, Teunis |
| author2 | Scheffer, C. |
| author_browse | Cloete, Teunis Scheffer, C. |
| author_facet | Scheffer, C. Cloete, Teunis |
| author_sort | Cloete, Teunis |
| collection | Thesis |
| dc_rights_str_mv | University of Stellenbosch |
| description | MScEng |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/2922 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:44:42.460Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2009 |
| publishDateRange | 2009 |
| publishDateSort | 2009 |
| publisher | Stellenbosch : University of Stellenbosch |
| publisherStr | Stellenbosch : University of Stellenbosch |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/2922 Benchmarking full-body inertial motion capture for clinical gait analysis Cloete, Teunis Scheffer, C. University of Stellenbosch. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering. Inertial motion capture Dissertations -- Mechanical engineering Theses -- Mechanical engineering Gait in humans Neural networks (Neurobiology) MScEng Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2009. Clinical gait analysis has been proven to greatly improve treatment planning and monitoring of patients suffering from neuromuscular disorders. Despite this fact, it was found that gait analysis is still largely underutilised in general patient-care due to limitations of gait measurement equipment. Inertial motion capture (IMC) is able to overcome many of these limitations, but this technology is relatively untested and is therefore viewed as adolescent. This study addresses this problem by evaluating the validity and repeatability of gait parameters measured with a commercially available, full-body IMC system by comparing the results to those obtained with alternative methods of motion capture. The IMC system’s results were compared to a trusted optical motion capture (OMC) system’s results to evaluate validity. The results show that the measurements for the hip and knee obtained with IMC compares well with those obtained using OMC – with coefficient-of-correlation (R) values as high as 0.99. Some discrepancies were identified in the ankle-joint validity results. These were attributed to differences between the two systems with regard to the definition of ankle joint and to non-ideal IMC system foot-sensor design. The repeatability, using the IMC system, was quantified using the coefficient of variance (CV), the coefficient of multiple determination (CMD) and the coefficient of multiple correlation (CMC). Results show that IMC-recorded gait patterns have high repeatability for within-day tests (CMD: 0.786-0.984; CMC: 0.881-0.992) and between-day tests (CMD: 0.771-0.991; CMC: 0.872-0.995). These results compare well with those from similar studies done using OMC and electromagnetic motion capture (EMC), especially when comparing between-day results. Finally, to evaluate the measurements from the IMC system in a clinically useful application, a neural network was employed to distinguish between gait strides of stroke patients and those of able-bodied controls. The network proved to be very successful with a repeatable accuracy of 99.4% (1/166 misclassified). The study concluded that the full-body IMC system produces sufficiently valid and repeatable gait data to be used in clinical gait analysis, but that further refinement of the ankle-joint definition and improvements to the foot sensor are required. 2009-02-13T12:51:12Z 2010-06-01T09:01:41Z 2009-02-13T12:51:12Z 2010-06-01T09:01:41Z 2009-03 Thesis http://hdl.handle.net/10019.1/2922 en University of Stellenbosch application/pdf Stellenbosch : University of Stellenbosch |
| spellingShingle | Inertial motion capture Dissertations -- Mechanical engineering Theses -- Mechanical engineering Gait in humans Neural networks (Neurobiology) Cloete, Teunis Benchmarking full-body inertial motion capture for clinical gait analysis |
| title | Benchmarking full-body inertial motion capture for clinical gait analysis |
| title_full | Benchmarking full-body inertial motion capture for clinical gait analysis |
| title_fullStr | Benchmarking full-body inertial motion capture for clinical gait analysis |
| title_full_unstemmed | Benchmarking full-body inertial motion capture for clinical gait analysis |
| title_short | Benchmarking full-body inertial motion capture for clinical gait analysis |
| title_sort | benchmarking full body inertial motion capture for clinical gait analysis |
| topic | Inertial motion capture Dissertations -- Mechanical engineering Theses -- Mechanical engineering Gait in humans Neural networks (Neurobiology) |
| url | http://hdl.handle.net/10019.1/2922 |
| work_keys_str_mv | AT cloeteteunis benchmarkingfullbodyinertialmotioncaptureforclinicalgaitanalysis |