Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study

Hdl Handle:
http://hdl.handle.net/10147/338183
Title:
Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study
Authors:
Giggins, Oonagh M; Sweeney, Kevin T; Caulfield, Brian
Citation:
Giggins, O.M., Sweeney, K.T. & Caulfield, B. Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study. Journal of NeuroEngineering and Rehabilitation. 2014, 11 (1) pp 158
Issue Date:
27-Nov-2014
URI:
http://dx.doi.org/10.1186/1743-0003-11-158; http://hdl.handle.net/10147/338183
Abstract:
Abstract Background Accurate assessments of adherence and exercise performance are required in order to ensure that patients adhere to and perform their rehabilitation exercises correctly within the home environment. Inertial sensors have previously been advocated as a means of achieving these requirements, by using them as an input to an exercise biofeedback system. This research sought to investigate whether inertial sensors, and in particular a single sensor, can accurately classify exercise performance in patients performing lower limb exercises for rehabilitation purposes. Methods Fifty-eight participants (19 male, 39 female, age: 53.9 ± 8.5 years, height: 1.69 ± 0.08 m, weight: 74.3 ± 13.0 kg) performed ten repetitions of seven lower limb exercises (hip abduction, hip flexion, hip extension, knee extension, heel slide, straight leg raise, and inner range quadriceps). Three inertial sensor units, secured to the thigh, shin and foot of the leg being exercised, were used to acquire data during each exercise. Machine learning classification methods were applied to quantify the acquired data. Results The classification methods achieved relatively high accuracy at distinguishing between correct and incorrect performance of an exercise using three, two, or one sensor while moderate efficacy scores were also achieved by the classifier when attempting to classify the particular error in exercise performance. Results also illustrated that a reduction in the number of inertial sensor units employed has little effect on the overall efficacy results. Conclusion The results revealed that it is possible to classify lower limb exercise performance using inertial sensors with satisfactory levels of accuracy and reducing the number of sensors employed does not reduce the accuracy of the method.
Item Type:
Article
Language:
en
Keywords:
REHABILITATION

Full metadata record

DC FieldValue Language
dc.contributor.authorGiggins, Oonagh Men_GB
dc.contributor.authorSweeney, Kevin Ten_GB
dc.contributor.authorCaulfield, Brianen_GB
dc.date.accessioned2015-01-13T11:39:26Z-
dc.date.available2015-01-13T11:39:26Z-
dc.date.issued2014-11-27-
dc.identifier.citationGiggins, O.M., Sweeney, K.T. & Caulfield, B. Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study. Journal of NeuroEngineering and Rehabilitation. 2014, 11 (1) pp 158en_GB
dc.identifier.urihttp://dx.doi.org/10.1186/1743-0003-11-158-
dc.identifier.urihttp://hdl.handle.net/10147/338183-
dc.description.abstractAbstract Background Accurate assessments of adherence and exercise performance are required in order to ensure that patients adhere to and perform their rehabilitation exercises correctly within the home environment. Inertial sensors have previously been advocated as a means of achieving these requirements, by using them as an input to an exercise biofeedback system. This research sought to investigate whether inertial sensors, and in particular a single sensor, can accurately classify exercise performance in patients performing lower limb exercises for rehabilitation purposes. Methods Fifty-eight participants (19 male, 39 female, age: 53.9 ± 8.5 years, height: 1.69 ± 0.08 m, weight: 74.3 ± 13.0 kg) performed ten repetitions of seven lower limb exercises (hip abduction, hip flexion, hip extension, knee extension, heel slide, straight leg raise, and inner range quadriceps). Three inertial sensor units, secured to the thigh, shin and foot of the leg being exercised, were used to acquire data during each exercise. Machine learning classification methods were applied to quantify the acquired data. Results The classification methods achieved relatively high accuracy at distinguishing between correct and incorrect performance of an exercise using three, two, or one sensor while moderate efficacy scores were also achieved by the classifier when attempting to classify the particular error in exercise performance. Results also illustrated that a reduction in the number of inertial sensor units employed has little effect on the overall efficacy results. Conclusion The results revealed that it is possible to classify lower limb exercise performance using inertial sensors with satisfactory levels of accuracy and reducing the number of sensors employed does not reduce the accuracy of the method.-
dc.language.isoenen
dc.subjectREHABILITATIONen_GB
dc.titleRehabilitation exercise assessment using inertial sensors: a cross-sectional analytical studyen_GB
dc.typeArticleen
dc.language.rfc3066en-
dc.rights.holderOonagh M Giggins et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2014-12-22T16:03:17Z-
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