Heiko Schlarb

Predictive musculoskeletal simulation using optimal control: effects of added limb mass on energy cost and kinematics of walking and running

When designing sports equipment, it is often desirable to predict how certain design parameters will affect human performance. In many instances, this requires a consideration of human musculoskeletal mechanics and adaptive neuromuscular control. Current computational methods do not represent these mechanisms, and design optimization typically requires several iterations of prototyping and human testing. This paper introduces a computational method based on musculoskeletal modeling and optimal control, which has the capability to predict the effect of mechanical equipment properties on human performance. The underlying assumption is that users will adapt their neuromuscular control according to an optimality principle, which balances task performance with a minimization of muscular effort. The method was applied to the prediction of metabolic cost and limb kinematics while running and walking with weights attached to the body. A two-dimensional musculoskeletal model was used, with nine kinematic degrees of freedom and 16 muscles. The optimal control problem was solved for two walking speeds and two running speeds, and at each speed, with 200 g and 400 g masses placed at the thigh, knee, shank and foot. The model predicted an increase in energy expenditure that was proportional to the added mass and the effect was largest for a mass placed on the foot. Specifically, the model predicted an energy cost increase of 0.74% for each 100 g mass added to the foot during running at 3.60 m/s. The model also predicted that stride length would increase by several millimetres in the same condition, relative to the model without added mass. These predictions were consistent with previously published human studies. Peak force and activation remained the same in most muscles, but increased by 26% in the hamstrings and by 17% in the rectus femoris for running at 4.27 m/s with 400 g added mass at the foot, suggesting muscle-specific training effects. This work demonstrated that a musculoskeletal model with optimal control can predict the effect of mechanical devices on human performance, and could become a useful tool for design optimization in sports engineering. The theoretical background of predictive simulation also helps explain why human athletes have specific responses when exercising in an altered mechanical environment.

Inertial sensor based and shoe size independent gait analysis including heel and toe clearance estimation

Falls are a major cause for morbidity and mortality in the ageing society. Inertial sensor based gait assessment including the analysis of the heel and toe clearance can be an indicator for the risk of falling. This paper presents a method for calculating the continuous heel and toe clearance without the knowledge of the shoe dimensions as well as the foot angle in the sagittal plane. These gait parameters were validated using an optical motion capture system. 20 healthy subjects from 3 different age groups (young, mid age, old) performed gait trials with different stride lengths and stride velocities. We obtained low mean absolute errors, low standard deviations and high Pearson correlations (0.91-0.99) for all gait parameters. In summary, we implemented a viable algorithm for the calculation of the heel and toe clearance without knowing the shoe dimensions as well as the foot angle in sagittal plane. We conclude that the given method is applicable for a mobile and unobtrusive gait assessment for healthy subjects from all age classes.

Pattern classification of foot strike type using body worn accelerometers

The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner.

A wireless trigger for synchronization of wearable sensors to external systems during recording of human gait

Mobile gait analysis focuses on the automatic extraction of gait parameters from wearable sensor data. However, development of algorithms for this task requires kinematic data with accurate and highly synchronous ground truth. In this paper we present a wireless trigger system which allows reliable synchronization of wearable sensors to external systems providing ground truth. To demonstrate the applicability of the system for mobile gait analysis, a Shimmer wireless sensor node with inertial sensors was mounted at the heel of a running shoe and synchronized with an external VICON motion capturing system using the wireless trigger system. Inertial sensor data were recorded during walking and running with the shoe, while kinematic and kinetic ground truth was acquired from the synchronized VICON system. Evaluation of delay and jitter of the system showed a mean delay of 2 ms and low jitter of 20 us. Recording was highly synchronous and the collected kinematics had a correlation of up to 0.99. In the future the proposed system will allow the creation of a database of inertial data from human gait with accurate ground truth synchronization.

Comparison and classification of 3D objects surface point clouds on the example of feet

One of the main tasks of shoe manufacturing is the production of well fitting shoes for different specialized markets. The key to conduct this properly is the analysis of the factors that influence the variations of the foot shape. In this paper methods and results of clustering and analysis of 3D foot surfaces are presented. The data were collected from a study with more than 12,000 feet that have been laser-scanned. The database contains point clouds acquired from persons coming from different regions of the world. Furthermore, additional personal data were collected. Two different methods for quantifying the similarity of 3D surface point clouds are therefore developed. The first method generally works on nearly arbitrary 3D surface point clouds, while the second one is specialized on foot data sets. These similarity measures were used on the data sets of the foot-shape study, together with clustering and feature quality evaluation methods. The purpose was to obtain information about the impact of, and the relationship among, the different factors influencing the shape of a foot. Through the observations of the experiments presented here it was possible to build up a hierarchy of different levels of feature-groups determined by their impact on the foot shape. Furthermore, an investigation of the quality and amount of impact of the features, according to their ability to separate specific subgroups of persons, is shown. Based on these results it was possible to select those features, which result in the largest effect when designing shoes for e.g. the Asian versus European markets.

Effect of torsional stiffness on biomechanical variables of the lower extremity during running

Torsion, the relative in-/eversion between forefoot and rearfoot, is a concept that has been incorporated into running shoes for almost 30 years. Studies have shown an influence of footwear torsional stiffness on lower extremity biomechanics during running but results are inconclusive. However, the influence of the torsion axis of the shoe on kinematics and kinetics of running has not been examined. Therefore, the goal was to examine the effect of shoes with a specially designed torsion element on running biomechanics of the lower extremities. Twenty runners performed heel–toe running at 4.0 ms−1 with three shoes and barefoot. All shoes had a torsion element based on a rearfoot and a forefoot element connected by bushings that had a defined rotation axis. The torsional stiffness was altered by modifications made to the torsion element and the surrounding midsole. A force plate and camera system were used to collect kinetics and kinematics. Foot torsion, ankle eversion, ankle and knee moments in the frontal and transverse plane and ground reaction forces were compared between conditions using paired t-tests. The shoe with the lowest torsional stiffness did not result in larger torsion range of motion compared to a stiffer shoe. Ankle eversion decreased with decreasing torsional stiffness while the changes in ankle kinetics were not consistent between the frontal and transverse plane. Torsional stiffness did not have a systematic influence on knee joint kinetics. While shoe torsional stiffness influences foot kinematics significantly, it does not affect lower extremity running biomechanics in a way that would alter the risk of running injuries.

Evaluation of a Kinematically-Driven Finite Element Footstrike Model

A dynamic finite element model of a shod running footstrike was developed and driven with 6 degree of freedom foot segment kinematics determined from a motion capture running trial. Quadratic tetrahedral elements were used to mesh the footwear components with material models determined from appropriate mechanical tests. Model outputs were compared with experimental high-speed video (HSV) footage, vertical ground reaction force (GRF), and center of pressure (COP) excursion to determine whether such an approach is appropriate for the development of athletic footwear. Although unquantified, good visual agreement to the HSV footage was observed but significant discrepancies were found between the model and experimental GRF and COP readings (9% and 61% of model readings outside of the mean experimental reading ± 2 standard deviations, respectively). Model output was also found to be highly sensitive to input kinematics with a 120% increase in maximum GRF observed when translating the force platform 2 mm vertically. While representing an alternative approach to existing dynamic finite element footstrike models, loading highly representative of an experimental trial was not found to be achievable when employing exclusively kinematic boundary conditions. This significantly limits the usefulness of employing such an approach in the footwear development process.

The function of rearfoot technologies in running shoes is gender-dependent

The results of this study show that the integrated 2 lateral wedge tested did not shift the center of pressure laterally during stance, and so consequently did not decrease the peak knee abduction joint moments during walking. While prior studies have shown that lateral wedges decrease knee abduction moments (Erhart et al. 2010), the magnitudes of these lateral wedge were always greater than 4 . The authors were unable to find any published example of a lateral wedge of less than 4 having a reducing effect on knee joint loading. Therefore, it is likely that the 2 lateral wedge of the shoe tested in this investigation was too small to elicit a change in the center of pressure position and the knee abduction moment.