A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

Abstract

Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

A Comparative Study of Markerless Systems Based on Color-Depth Cameras, Polymer Optical Fiber Curvature Sensors, and Inertial Measurement Units: Towards Increasing the Accuracy in Joint Angle Estimation

“…Table 2 shows the maximum and minimum angles for cameras system and IMUs, compared to upper and lower bounds, respectively. The average error for the cameras system was 4.9 • , with a maximum error of 9 • , when compared with the goniometer for two values (90 • e 20 • ), which is lower than the mean error presented in Tannous et al [38] (14, 6 • ). However, in Tannous et al [38] only one camera was used, carrying a higher self-occlusion leading to errors on the angle assessment.…”

“…The average error for the cameras system was 4.9 • , with a maximum error of 9 • , when compared with the goniometer for two values (90 • e 20 • ), which is lower than the mean error presented in Tannous et al [38] (14, 6 • ). However, in Tannous et al [38] only one camera was used, carrying a higher self-occlusion leading to errors on the angle assessment. Since our system consists of two cameras, the self-occlusion decreases, consequently, reducing the errors.…”

Knee Joint Angle Measuring Portable Embedded System based on Inertial Measurement Units for Gait Analysis

“…obtained ICC values around 0.96 -0.98 and RMSE mean value of 3.96° from their lower limb fusion algorithm [32].…”

“…Therefore, it is necessary to develop methods to reduce noise, among them, Butterworth low pass filter [24], [25] and Kalman filter [7], [26]- [31] are the most used. Tannous et al [32] incorporate IMU sensors in joint rotation axes to measure ankle and knee accelerations. They proposed a real-time orientation-based fusion scheme between Kinect and IMU sensors to improve the knee-joints kinematics during functional rehabilitation of the lower limb movement.…”

An Estimation Method of Skeleton Proportion for Hand’s Motion Capture

“…Liu Bo of Beijing University of Technology used the MEMS sensor to calibrate the arm bone, the thigh bone and the head bone to realize the accurate posture positioning of the human body motion data to the human skeleton model [3]. Tannous Halim [2] and others have realized a method to improve the accuracy of human body motion attitude measurement based on the Kalman filter method. Eric Foxlin [1] and others use sensors composed of a gyroscope and accelerometer as an attitude tracking acquisition unit and propose a method for head tracking and neck movement of the human body.…”