A study on machine learning algorithms for fall detection and movement classification
| dc.contributor.advisor | Ko, Seok-Bum | en_US |
| dc.contributor.committeeMember | Teng, Daniel | en_US |
| dc.contributor.committeeMember | Ludwig, Simone | en_US |
| dc.contributor.committeeMember | Dinh, Anh | en_US |
| dc.creator | Ralhan, Amitoz Singh | en_US |
| dc.date.accessioned | 2009-12-22T14:46:28Z | en_US |
| dc.date.accessioned | 2013-01-04T05:12:43Z | |
| dc.date.available | 2011-01-04T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T05:12:43Z | |
| dc.date.created | 2009-12 | en_US |
| dc.date.issued | 2009-12-15 | en_US |
| dc.date.submitted | December 2009 | en_US |
| dc.description.abstract | Fall among the elderly is an important health issue. Fall detection and movement tracking techniques are therefore instrumental in dealing with this issue. This thesis responds to the challenge of classifying different movement types as a part of a system designed to fulfill the need for a wearable device to collect data for fall and near-fall analysis. Four different fall activities (forward, backward, left and right), three normal activities (standing, walking and lying down) and near-fall situations are identified and detected. Different machine learning algorithms are compared and the best one is used for the real time classification. The comparison is made using Waikato Environment for Knowledge Analysis or in short WEKA. The system also has the ability to adapt to different gaits of different people. A feature selection algorithm is also introduced to reduce the number of features required for the classification problem. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-12222009-144628 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Fall Detection | en_US |
| dc.subject | Feature Selection | en_US |
| dc.title | A study on machine learning algorithms for fall detection and movement classification | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | Electrical Engineering | en_US |
| thesis.degree.discipline | Electrical Engineering | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science (M.Sc.) | en_US |