The healthcare systems are experiencing heavy workload and high cost caused by
ageing population. The assisted monitoring systems for the elderly persons, and
persons with chronic diseases, promises great potential to provide them with care
and comfort at the privacy of their own homes and as a result help reduce healthcare
costs. This requires a monitoring system capable of detecting daily human activities
in living spaces. In this work we discuss different challenges to design such a system,
present an activity data visualization tool designed to study human activities in a
living space and propose a two stage, supervised statistical model for detecting the
activities of daily living (ADL) from non-visual sensor data streams. A novel data
segmentation is proposed for accurate prediction at the first stage. We present a novel
error correction structure for the second stage to boost the accuracy by correcting
the misclassification from the first stage.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/14399 |
| Date | 08 December 2011 |
| Creators | Kalra, Love |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
Page generated in 0.0019 seconds