Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems |
| |
| Authors: | Mitchell Yuwono Bruce D Moulton Steven W Su Branko G Celler Hung T Nguyen |
| |
| Affiliation: | (1) Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia;(2) ICT Centre, CSIRO, Marsfield, NSW, 2122, Australia |
| |
| Abstract: | Background Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
