Identification of fall-risk factor degradations using quality of balance measurements
| aut.embargo | No | en_NZ |
| aut.thirdpc.contains | No | en_NZ |
| aut.thirdpc.permission | No | en_NZ |
| aut.thirdpc.removed | No | en_NZ |
| dc.contributor.advisor | Hewson, David, John | |
| dc.contributor.advisor | Taylor, Denise | |
| dc.contributor.advisor | McNair, Peter, John | |
| dc.contributor.author | Bassement, Jennifer | |
| dc.date.accessioned | 2015-06-08T00:05:34Z | |
| dc.date.available | 2015-06-08T00:05:34Z | |
| dc.date.copyright | 2014 | |
| dc.date.created | 2015 | |
| dc.date.issued | 2014 | |
| dc.date.updated | 2015-06-05T14:47:06Z | |
| dc.description.abstract | Falls concern a third of the people aged over 65y and lead to the loss of functional ability. The detection of risks factors of falls is essential for early interven- tion. Six intrinsic risk factors of fall: vision, vestibular system, joint range of motion, leg muscle strength, joint proprioception and foot cutaneous propriocep- tion were assessed with clinical tests before and after temporarily degradation. Standing balance was recorded on a force plate. From the force plate, 198 parameters of the centre of pressure displacement were computed. The parame- ters were used as variables to build neural network and logistic regression model for discriminating conditions. Feature selection analysis was per- formed to reduce the number of variables. Several models were built including 3 to 10 condi- tions. Models with 5 or less conditions appeared acceptable but better performance was found with models including 3 conditions. The best accuracy was 92% for a model including ankle range of motion, fatigue and vision contrast conditions. Qualities of balance parameters were able to diag- nose impairments. However, the efficient models included only a few conditions. Models with more conditions could be built but would require a larger number of cases to reach high accuracy. The study showed that a neural network or a logistic model could be used for the diagnosis of balance impairments. Such a tool could seriously improve the prevention and rehabilitation practice. | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/8839 | |
| dc.language.iso | en | en_NZ |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Medical sciences | en_NZ |
| dc.subject | Fall (accidents) in old age | en_NZ |
| dc.subject | Equilibrium | en_NZ |
| dc.subject | Artificial intelligence | en_NZ |
| dc.subject | Signal processing | en_NZ |
| dc.subject | Posture | en_NZ |
| dc.subject | Diagnosis | en_NZ |
| dc.title | Identification of fall-risk factor degradations using quality of balance measurements | en_NZ |
| dc.type | Thesis | |
| thesis.degree.discipline | ||
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Doctoral Theses | |
| thesis.degree.name | Doctor of Philosophy | en_NZ |