Citation

Aziz O, Robinovitch SN, Park EJ. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016; 2016: 5003-5006.

Copyright

(Copyright © 2016, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2016.7591851

PMID

28269392

Abstract

In order to perform fall risk assessments using wearable inertial sensors in older adults in their natural settings where falls are likely to occur, a first step is to automatically segment and classify sensor signals of human movements into the known 'activities of interest'. Sensor data from such activities can later be used through quantitative and qualitative analysis for differentiating fallers from non-fallers. In this study, ten young adults participated in experimental trials involving several variations of walking, transferring and sedentary activities. Data from tri-axial accelerometers and gyroscopes were used to classify the aforementioned three categories using a multiclass support vector machine algorithm. Our results showed 100% accuracy in distinguishing walking, transferring and sedentary activities using data from a three-sensor combination of sternum and both ankles.

Language: en