So Young Baek

We compared the ability of seven machine learning algorithms to use wearable inertial measurement unit (IMU) data to identify the severe knee loading cycles known to induce microdamage associated with anterior cruciate ligament rupture. Sixteen cadaveric knee specimens, dissected free of skin and muscle, were mounted in a rig simulating standardized jump landings. One IMU was located above and the other below the knee, the applied three-dimensional action and reaction loads were measured via six-axis load cells, and the three-dimensional knee kinematics were also recorded by a laboratory motion capture system. Machine learning algorithms were used to predict the knee moments and the tibial and femur vertical forces; 13 knees were utilized for training each model, while three were used for testing its accuracy (i.e., normalized root-mean-square error) and reliability (Bland–Altman limits of agreement). The results showed the models predicted force and knee moment values with acceptable levels of error and, although several models exhibited some form of bias, acceptable reliability. Further research will be needed to determine whether these types of models can be modified to attenuate the inevitable in vivo soft tissue motion artifact associated with highly dynamic activities like jump landings.

Injuries are often associated with rapid body segment movements. We compared Certus motion capture and APDM inertial measurement unit (IMU) measurements of tibiofemoral angle and angular velocity changes during simulated pivot landings (i.e., ~70 ms peak) of nine cadaver knees dissected free of skin, subcutaneous fat, and muscle. Data from a total of 852 trials were compared using the Bland–Altman limits of agreement (LoAs): the Certus system was considered the gold standard measure for the angle change measurements, whereas the IMU was considered the gold standard for angular velocity changes. The results show that, although the mean peak IMU knee joint angle changes were slightly underestimated (2.1° for flexion, 0.2° for internal rotation, and 3.0° for valgus), the LoAs were large, ranging from 35.9% to 49.8%. In the case of the angular velocity changes, Certus had acceptable accuracy in the sagittal plane, with LoAs of ±54.9°/s and ±32.5°/s for the tibia and femur. For these rapid motions, we conclude that, even in the absence of soft tissues, the IMUs could not reliably measure these peak 3D knee angle changes; Certus measurements of peak tibiofemoral angular velocity changes depended on both the magnitude of the velocity and the plane of measurement.

Lower limb joint kinematics have been measured in laboratory settings using fixed camera-based motion capture systems; however, recently inertial measurement units (IMUs) have been developed as an alternative. The purpose of this study was to test a quaternion conversion (QC) method for calculating the three orthogonal knee angles during the high velocities associated with a jump landing using commercially available IMUs. Nine cadaveric knee specimens were instrumented with APDM Opal IMUs to measure knee kinematics in one-legged 3–4× bodyweight simulated jump landings, four of which were used in establishing the parameters (training) for the new method and five for validation (testing). We compared the angles obtained from the QC method to those obtained from a commercially available sensor and algorithm (APDM Opal) with those calculated from an active marker motion capture system. Results showed a significant difference between both IMU methods and the motion capture data in the majority of orthogonal angles (p < 0.01), though the differences between the QC method and Certus system in the testing set for flexion and rotation angles were smaller than the APDM Opal algorithm, indicating an improvement. Additionally, in all three directions, both the limits of agreement and root-mean-square error between the QC method and the motion capture system were smaller than between the commercial algorithm and the motion capture.