The contribution of multibody optimization when using inertial measurement units to compute lower-body kinematics

Biomechanical evaluation for bone arthrosis morphology based on reconstructed dynamic kinesiology

A biomechanical evaluation method for bone arthrosis morphology based on reconstructed dynamic kinesiology (RDK) is proposed. The hip joint is a ball-and-socket joint, morphologically characterized by an acetabulum with a nearly spherical concavity and uniform curvatures, where Gaussian curvature exhibits negative characteristic. Subsequently, RDK of bone joint morphology is developed, offering detailed anatomical and kinematic insights. The hip joint is taken as a verification instance, where a precise biomechanical evaluation of bone arthrosis morphology is simulated through finite element analysis (FEA). Latin Hypercube sampling (LHS) with the criterion of maximizing the minimum distance enhances uniformity and representation. The response surface is subsequently constructed by Kriging interpolation, significantly enhancing computational efficiency and FEA accuracy. Innovatively, a stress contour statistical histogram of load transfer is presented to quantitatively analyze the stress lines, supplying support for biomechanical evaluation, which is essential for accurate hip replacement planning. The instance indicates that the proposed RDK facilitates accurate biomechanical evaluations for bone arthrosis morphology, providing a critical theoretical foundation for conceptual design of ergonomic wearable devices, as well as optimization of replacement surgeries.

Validity of Wearable Inertial Sensors for Gait Analysis: A Systematic Review

Background/Objectives: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable and accessible assessments outside the lab, though challenges with sensor placement, signal selection, and algorithm design can affect accuracy. This systematic review aims to bridge the benchmarking gap between IMU-based and traditional systems, validating the use of wearable inertial systems for gait analysis. Methods: This review examined English studies between 2012 and 2023, retrieved from the Scopus database, comparing wearable sensors to optical motion capture systems, focusing on IMU body placement, gait parameters, and validation metrics. Exclusion criteria for the search included conference papers, reviews, unavailable papers, studies without wearable inertial sensors for gait analysis, and those not involving agreement studies or optical motion capture systems. Results: From an initial pool of 479 articles, 32 were selected for full-text screening. Among them, the lower body resulted in the most common site for single IMU placement (in 22 studies), while the most frequently used multi-sensor configuration involved IMU positioning on the lower back, shanks, feet, and thighs (10 studies). Regarding gait parameters, 11 studies out of the 32 included studies focused on spatial-temporal parameters, 12 on joint kinematics, 2 on gait events, and the remainder on a combination of parameters. In terms of validation metrics, 24 studies employed correlation coefficients as the primary measure, while 7 studies used a combination of error metrics, correlation coefficients, and Bland-Altman analysis. Validation metrics revealed that IMUs exhibited good to moderate agreement with optical motion capture systems for kinematic measures. In contrast, spatiotemporal parameters demonstrated greater variability, with agreement ranging from moderate to poor. Conclusions: This review highlighted the transformative potential of wearable IMUs in advancing gait analysis beyond the constraints of traditional laboratory-based systems.

Associations of back muscle endurance with occupational back muscle activity and spinal loading among subsistence farmers and office workers in Rwanda

Over the course of the physical activity transition, machines have largely replaced skeletal muscle as the source of work for locomotion and other forms of occupational physical activity in industrial environments. To better characterize this transition and its effect on back muscles and the spine, we tested to what extent typical occupational activities of rural subsistence farmers demand higher magnitudes and increased variability of back muscle activity and spinal loading compared to occupational activities of urban office workers in Rwanda, and whether these differences were associated with back muscle endurance, the dominant risk factor for back pain. Using electromyography, inertial measurement units, and OpenSim musculoskeletal modeling, we measured back muscle activity and spinal loading continuously while participants performed occupational activities for one hour. We measured back muscle endurance using electromyography median frequency analysis. During occupational work, subsistence farmers activate their back muscles and load their spines at 390% higher magnitudes and with 193% greater variability than office workers. Partial correlations accounting for body mass show magnitude and variability response variables are positively associated with back muscle endurance (R = 0.39-0.90 [P < 0.001-0.210] and R = 0.54-0.72 [P = 0.007-0.071], respectively). Body mass is negatively correlated with back muscle endurance (R = -0.60, P = 0.031), suggesting higher back muscle endurance may be also partly attributable to having lower body mass. Because higher back muscle endurance is a major factor that prevents back pain, these results reinforce evidence that under-activating back muscles and under-loading spines at work increases vulnerability to back pain and may be an evolutionary mismatch. As sedentary occupations become more common, there is a need to study the extent to which occupational and leisure time physical activities that increase back muscle endurance helps prevent back pain.

Does IMU redundancy improve multi-body optimization results to obtain lower-body kinematics? A preliminary study says no

Inertial Measurement Units (IMUs) have been proposed as an ecological alternative to optoelectronic systems for obtaining human body joint kinematics. Tremendous work has been done to reduce differences between kinematics obtained with IMUs and optoelectronic systems, by improving sensor-to-segment calibration, fusion algorithms, and by using Multibody Kinematics Optimization (MKO). However, these improvements seem to reach a barrier, particularly on transverse and frontal planes. Inspired by marker-based MKO approach performed via OpenSim, this study proposes to test whether IMU redundancy with MKO could improve lower-limb kinematics obtained from IMUs. For this study, five subjects were equipped with 11 IMUs and 30 reflective markers tracked by 18 optoelectronic cameras. They then performed gait, cycling, and running actions. Four different lower-limb kinematics were computed: one kinematics based on markers after MKO, one kinematics based on IMUs without MKO, and two based on IMUs after MKO performed with OpenSense (one with, and one without, sensor redundancy). Kinematics were compared via Root Mean Square Difference and correlation coefficients to kinematics based on markers after MKO. Results showed that redundancy does not reduce differences with the kinematics based on markers after MKO on frontal and transverse planes comparatively to classic IMU MKO. Sensor redundancy does not seem to impact lower-limb kinematics on frontal and transverse planes, due to the likelihood of the "rigid component" of soft-tissue artefact impacting all sensors located on one segment.

Gait kinematics based on inertial measurement units with the sensor-to-segment calibration and multibody optimization adapted to the patient's motor capacities, a pilot study

INTRODUCTION

Inertial Measurement Units (IMUs) offer a promising alternative to optoelectronic systems to obtain joint lower-limb kinematics during gait. However, the associated methodologies, such as sensor-to-segment (S2S) calibration and multibody optimization, have been developed mainly for, and tested on, asymptomatic subjects.

RESEARCH QUESTION

This study proposes to evaluate two personalizations of the methodology used to obtain lower-body kinematics from IMUs with pathological subjects: S2S calibration and multibody optimization.

METHODS

Based on previous studies, two decision trees were developed to select the best (in terms of accuracy and repeatability) S2S methods to be performed by the patient given his/her abilities. The multibody optimization was personalized by limiting the kinematic chain range of motion to the results of the subject's clinical examination. These two propositions were tested on 12 patients with various gait deficits. The patients were equipped with IMUs and reflective markers tracked by an optoelectronic system. They had to perform the postures and movements selected by the decision trees then walk back and forth along a walkway. Gait kinematics obtained from the IMUs directly (referred to as Direct kinematics), and after multibody optimization performed via the OpenSim software using the generic range of motion (referred to as Generic Optimized kinematics), and using the personalized range of motion (referred to as Personalized Optimized kinematics) were compared to those obtained with the Conventional Gait Model through Root Mean Square Errors (RMSE), Correlation Coefficients (CC) and Range of Motion differences (ΔROM).

RESULTS

The RMSEs were smaller than 8.1° in the sagittal plane but greater than 7.4° in the transverse plane. The CCs, between 0.71 and 0.99 in the sagittal plane, deteriorate sharply in the frontal and transverse planes where they only measured between 0.15 and 0.68. The ΔROMs were mostly below 8.3°. Optimized kinematics did not improve compared to Direct kinematics.

SIGNIFICANCE

The personalization of the proposed S2S calibration method showed encouraging results, whereas multibody optimization did not impact the resulting joint kinematics.