Spectral and time-frequency domains features for quantitative lower-limb rehabilitation monitoring via wearable inertial sensors
Loading...
Files
Accepted Version
Date
2018-03-29
Authors
Tedesco, Salvatore
Urru, Andrea
O'Flynn, Brendan
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
Inertial data represent a rich source of clinically relevant information which can provide details on motor assessment in subjects involved in a rehabilitation process. Thus, a number of metrics in the spectral and time-frequency domain has been considered to be reliable for measuring and quantifying patient progress and has been applied on the 3D accelerometer and angular rate signals collected on one impaired subject with knee injury through a wearable wireless inertial sensing system developed at the Tyndall National Institute. The subject has performed different activities evaluated across several sessions over time. Data show that most of the studied features can provide a quantitative analysis of the improvement of the subject along rehabilitation, and differentiate between impaired and unimpaired limb motor performance. The work proves that the studied features can be taken into account by clinicians and sport scientists to study the overall patients' condition and provide accurate clinical feedback as to their rehabilitative progress. The work is ongoing and additional clinical trials are currently being planned with an enhanced number of injured subjects to provide a more robust statistical analysis of the data in the study.
Description
Keywords
Legged locomotion , Sensors , Monitoring , Entropy , Frequency-domain analysis , Surgery , Inertial sensors , Spectral analysis , Time-frequency domain features , Rehabilitation monitoring
Citation
Tedesco, S., Urru, A. and O'Flynn, B. (2017) 'Spectral and time-frequency domains features for quantitative lower-limb rehabilitation monitoring via wearable inertial sensors', 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), Turin, Italy, 19-21 October. doi:10.1109/BIOCAS.2017.8325142
Link to publisher’s version
Copyright
© 2017, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.