Hip precautions after primary total hip arthroplasty: ‘It's what we’ve always done’

Mandel, R.; Bruce, G; Moss, R; Carrington, Richard; Gilbert, Anthony; Jaggi, Anju

doi:10.1016/j.physio.2017.11.181

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar work has used sensors on the distal shank or multiple sensors, 11,14,17,18,26 whereas this system is designed for all of the hip rehabilitation exercises to work with an IMU placed on the thigh, as currently many users may be prevented from reaching below the knee, particularly after total hip replacement. 38 The knee rehabilitation exercises have an IMU located at the midpoint of the shin, ensuring that users are able to place the sensor easily and maintain the same position for all exercises, although in both cases, further 'real-world' validation is required to ensure that IMU placement by untrained patients does not detract from accuracy.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

Argent

Drummond

Remus

et al. 2019

Journal of Rehabilitation and Assistive Technologies Engineerin

View full text Add to dashboard Cite

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

show abstract