Title :
Automatic segmentation of triaxial accelerometry signals for falls risk estimation
Author :
Redmond, Stephen J. ; Scalzi, Maria Elena ; Narayanan, Michael R. ; Lord, Stephen R. ; Cerutti, Sergio ; Lovell, Nigel H.
Author_Institution :
Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW, Australia
fDate :
Aug. 31 2010-Sept. 4 2010
Abstract :
Falls-related injuries in the elderly population represent one of the most significant contributors to rising health care expense in developed countries. In recent years, falls detection technologies have become more common. However, very few have adopted a preferable falls prevention strategy through unsupervised monitoring in the free-living environment. The basis of the monitoring described herein was a self-administered directed-routine (DR) comprising three separate tests measured by way of a waist-mounted triaxial accelerometer. Using features extracted from the manually segmented signals, a reasonable estimate of falls risk can be achieved. We describe here a series of algorithms for automatically segmenting these recordings, enabling the use of the DR assessment in the unsupervised and home environments. The accelerometry signals, from 68 subjects performing the DR, were manually annotated by an observer. Using the proposed signal segmentation routines, an good agreement was observed between the manually annotated markers and the automatically estimated values. However, a decrease in the correlation with falls risk to 0.73 was observed using the automatic segmentation, compared to 0.81 when using markers manually placed by an observer.
Keywords :
accelerometers; biomechanics; biomedical measurement; geriatrics; medical signal processing; patient monitoring; automatic signal segmentation; elderly; fall risk estimation; feature extraction; self-administered directed-routine; triaxial accelerometry; waist-mounted triaxial accelerometer; Acceleration; Biomedical measurements; Feature extraction; Observers; Robustness; Timing; Acceleration; Accidental Falls; Aged; Aged, 80 and over; Algorithms; Automation; Genotype; Humans; Materials Testing; Middle Aged; Models, Statistical; Monitoring, Ambulatory; Risk; Signal Processing, Computer-Assisted;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
Print_ISBN :
978-1-4244-4123-5
DOI :
10.1109/IEMBS.2010.5627384