Authors:
Teddy M. Cheng
1
;
Andrey V. Savkin
1
;
Branko G. Celler
1
;
Steven W. Su
2
and
Ning Wang
1
Affiliations:
1
the University of New South Wales, Australia
;
2
the University of Technology, Australia
Keyword(s):
Wearable sensors, Accelerometer, Exercise intensity, Fundamental frequency estimation, Data fusion, Kalman filtering.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Devices
;
Health Information Systems
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Computing Systems
;
Wearable Sensors and Systems
Abstract:
In this paper, we propose an algorithm for the estimation of exercise rate during a variety of exercises by using measurements from triaxial accelerometry. The algorithm involves the detection of the periodicity of the body’s accelerations, and the detected periods are then fused to form an estimate of exercise rate. Experimental results demonstrate that the algorithm is effective in different modes of exercise. The proposed algorithm will be useful in monitoring training exercises for healthy individuals and rehabilitation exercises for cardiac patients.