Knee braces are limited to providing passive support. There is currently no brace available providing both continuous monitoring and active robot-assisted movements of the knee joint. This project aimed to develop a wearable intelligent motorised robotic knee brace to support and monitor rehabilitation for a range of knee conditions including post-surgical rehabilitation. This brace can be used at home providing ambulatory continuous passive movement obviating the need for hospital admissions. A wearable sensing system monitoring knee range of motion was developed to provide remote feedback to clinicians and real-time guidance for patients. A prototype of an exoskeleton providing dynamic motion assistance was developed to help patients complete their exercise goals and strengthen their muscles. The accuracy and reliability of those functions were validated in human participants during exercises including knee flexion/extension (FE) in bed and in chair, sit-to-stand and stand-to-sit.Abstract
Introduction
Methodology
Rehabilitation exercise is critical for patients’ recovery after knee injury or post-surgery. Unfortunately, adherence to exercise is low due to a lack of positive feedback and poor self-motivation. Therefore, it is crucial to monitor their progress and provide supervision. Inertial measurement unit (IMUs) based sensing technology can provide remote patient monitoring functions. However, most current solutions only measure the range of knee motion in one degree of freedom. The current IMUs estimate the orientation-angle based on the integrated raw data, which might lack accuracy in measuring knee motion. This study aims to develop an IMU-based sensing system using the absolute measured orientation-angle to provide more accurate comprehensive monitoring by measuring the knee rotational angles. An IMU sensing system monitoring the knee joint angles, flexion/extension (FE), adduction/abduction (AA), and internal/external (IE) was developed. The accuracy and reliability of FE measurements were validated in human participants during squat exercise using measures including root mean square error (RMSE) and correlation coefficient. The RMSE of the three knee angles (FE, AA, and IE) were 0.82°, 0.26°, and 0.11°, which are acceptable for assessing knee motion. The FE measurement was validated in human participants and showed excellent accuracy (correlation coefficient of 0.99°). Further validation of AA and IE in human participants is underway. The sensing system showed the capability to estimate three knee rotation angles (FE, AA, and IE). It showed the potential to provide comprehensive continuous monitoring for knee rehabilitation exercises, which can also be used as a clinical assessment tool.Abstract
To determine the tensile forces across the knee extensor mechanism during walking, in simulated conditions of treatment. Gait analysis of six normal subjects was performed, with full weight bearing unilateral immobilisation of the knee during walking. Measurements were taken without then with a brace, unlocked then locked at 0°, 0-10°, 0-20° and 0-30° of flexion. Mean and maximum knee flexion angles were measured, followed by calculation of the mean and maximum forces across the extensor mechanism during loading, supporting and propulsion phases of gait.Aim
Methods
