The development of a segment-based musculoskeletal model of the lower limb: introducing F ree B ody

Abstract

Traditional approaches to the biomechanical analysis of movement are joint-based; that is the mechanics of the body are described in terms of the forces and moments acting at the joints, and that muscular forces are considered to create moments about the joints. We have recently shown that segment-based approaches, where the mechanics of the body are described by considering the effect of the muscle, ligament and joint contact forces on the segments themselves, can also prove insightful. We have also previously described a simultaneous, optimization-based, musculoskeletal model of the lower limb. However, this prior model incorporates both joint- and segment-based assumptions. The purpose of this study was therefore to develop an entirely segment-based model of the lower limb and to compare its performance to our previous work. The segment-based model was used to estimate the muscle forces found during vertical jumping, which were in turn compared with the muscular activations that have been found in vertical jumping, by using a Geers' metric to quantify the magnitude and phase errors. The segment-based model was shown to have a similar ability to estimate muscle forces as a model based upon our previous work. In the future, we will evaluate the ability of the segment-based model to be used to provide results with clinical relevance, and compare its performance to joint-based approaches. The segment-based model described in this article is publicly available as a GUI-based Matlab® application and in the original source code (at ).

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

“…Data filtering was performed in Matlab using a low-pass fourth order Butterworth filter with a cutoff frequency of 10 Hz. 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.…”

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

“…Filtering was uniformly applied to kinematic and kinetic data to prevent the introduction of artifacts resulting from incongruences between ground reaction force data and segment accelerations [21]. 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].…”

“…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.…”