A wearable real-time system for physical activity recognition and fall detection
| dc.contributor.advisor | Chen, Li | en_US |
| dc.contributor.advisor | Dinh, Anh | en_US |
| dc.contributor.committeeMember | Ko, Seok-Bum | en_US |
| dc.contributor.committeeMember | Wahid, Khan A. | en_US |
| dc.contributor.committeeMember | Chen, Daniel | en_US |
| dc.creator | Yang, Xiuxin | en_US |
| dc.date.accessioned | 2010-09-21T16:45:09Z | en_US |
| dc.date.accessioned | 2013-01-04T04:59:40Z | |
| dc.date.available | 2011-09-23T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T04:59:40Z | |
| dc.date.created | 2010-09 | en_US |
| dc.date.issued | 2010-09 | en_US |
| dc.date.submitted | September 2010 | en_US |
| dc.description.abstract | This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing people’s physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall. In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application. This wearable system works in two modes: indoor and outdoor, depending on user’s demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment. For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-09212010-164509 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Accelerometer | en_US |
| dc.subject | Naive Bayes classifier | en_US |
| dc.subject | Fall detection | en_US |
| dc.subject | Physical activity recognition | en_US |
| dc.title | A wearable real-time system for physical activity recognition and fall detection | 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 |