Journal Article


Brian Caulfield
Kevin T. Sweeney
Oona M. Giggins


Physiotherapy & Sport

analytical personal sensing rehabilitation 5 years male adult performance classification instrumentation humans middle aged inertial sensors exercise female accelerometry exercise therapy patient compliance classification cross sectional studies environment

Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study (2014)

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
Collections Ireland -> University College Dublin -> Insight Centre for Data Analytics
Ireland -> University College Dublin -> Institutes and Centres
Ireland -> University College Dublin -> Insight Research Collection
Ireland -> University College Dublin -> School of Public Health, Physiotherapy and Sports Science
Ireland -> University College Dublin -> College of Health and Agricultural Sciences
Ireland -> University College Dublin -> Public Health, Physiotherapy and Sports Science Research Collection

Full list of authors on original publication

Brian Caulfield, Kevin T. Sweeney, Oona M. Giggins

Experts in our system

Brian Caulfield
University College Dublin
Total Publications: 273
Kevin Sweeney
University College Dublin
Total Publications: 13