In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model

Abstract

2Segment-based musculoskeletal models allow the prediction of muscle, ligament and joint forces without making assumptions regarding joint degrees of freedom. The 4 dataset published for the "Grand Challenge Competition to Predict In Vivo Knee Loads" provides directly-measured tibiofemoral contact forces for activities of daily living. For 6 the "Sixth Grand Challenge Competition to Predict In Vivo Knee Loads", blinded results for "smooth" and "bouncy" gait trials were predicted using a customised patient-specific 8 musculoskeletal model. For an unblinded comparison the following modifications were made to improve the predictions: 10  further customisations, including modifications to the knee centre of rotation;  reductions to the maximum allowable muscle forces to represent known loss of 12 strength in knee arthroplasty patients; and  a kinematic constraint to the hip joint to address the sensitivity of the segment-14 based approach to motion tracking artefact.For validation, the improved model was applied to normal gait, squat and sit-to-stand 16 for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific input data 18 can estimate tibiofemoral contact forces with root mean square errors (RMSEs)

Micromotion and Push‐Out Evaluation of an Additive Manufactured Implant for Above‐the‐Knee Amputees

“…The specimens were subjected to a 100 cycle 1 Hz sinusoidally varying loading regime, with an R ‐ratio of 0.1, where the peak load was equal to 1.4 times the body weight of the donor. This loading regime was quantified using a validated model (Freebody version 2) applied to transfemoral amputees . The micromotion was measured from the last 90 cycles of the test and measured per LVDT as the mean difference between the peak and trough micromotion for the 90 cycles.…”

Deep Learning for Musculoskeletal Force Prediction

“…Freebody (v2.1)6, an open-source segment-based musculoskeletal model was used for subsequent data processing to determine internal forces. The model’s predictions of tibiofemoral JRF during gait have been validated using data from instrumented prostheses8, and predicted muscle force waveforms have been shown to demonstrate high levels of concordance with known electromyography envelopes6,31. Implementation involved the determination of coordinates of internal points in a subject-specific frame of reference.…”

“…Implementation involved the determination of coordinates of internal points in a subject-specific frame of reference. This was achieved by scaling using the measurements of a gender and height-matched subject, chosen from a morphologically diverse cohort of eight subjects, for whom three-dimensional position data of internal points had been obtained using magnetic resonance imaging8. Processed data were then taken as input by a Matlab® implementation of interior points optimisation28 using static trial data for model calibration, to determine muscle and joint forces for each sampled frame.…”

Functional electrical stimulation of gluteus medius reduces the medial joint reaction force of the knee during level walking

“…An open-source musculoskeletal model, Freebody (v1.1) [22], was used for subsequent data processing to determine internal forces. The model’s predictions of tibiofemoral JRF during gait have been validated using data from instrumented prostheses [23], and predicted muscle force waveforms have been shown to demonstrate high levels of concordance with known electromyography envelopes [22, 24]. The first part of the operation of Freebody involved the determination of coordinates of internal points (for example, bony landmarks and musculotendinous intersections) in a subject-specific frame of reference.…”

“…The first part of the operation of Freebody involved the determination of coordinates of internal points (for example, bony landmarks and musculotendinous intersections) in a subject-specific frame of reference. This was achieved by scaling using the measurements of gender-matched subjects for whom three-dimensional position data of internal points were available, obtained using magnetic resonance imaging (method described in [23]). Processed data were then taken as input by a Matlab® implementation of optimisation using static trial data for model calibration, to determine muscle, joint and ligamentous forces for each sampled frame.…”