Forceplate-instrumented Treadmill Research Articles

Adaptations in Gait Mechanics When Walking in an Ankle Exoskeleton

Exoskeletons are robotic, brace-like devices that can apply assistive torques to joints, supplementing biological torques. For example, ankle exoskeletons are often designed to assist during the push-off phase of walking to reduce energy expenditure. However, the mechanism by which these exoskeletons reduce metabolic costs is often unclear due to complex human-machine interactions. Here, our purpose was to determine what lower-limb gait kinematic, kinetic, and muscle activity changes underly metabolic cost reductions during ankle exoskeleton-assisted walking. We had healthy participants (n=6) perform two, 12-minute walking trials, one without assistive torques applied (Exo Off) and one with assistive torques applied (Exo On). Participants walked on a force plate instrumented treadmill (Bertec) and were instrumented with optical motion capture (Qualisys) to measure lower-limb gait kinematics and kinetics using Opensim, electromyography (Delsys) to measure the muscle activity of nine lower-limb muscles of the dominant limb, and indirect calorimetry (Cosmed) to measure metabolic energy expenditure (Figure 1A). We aggregated the motion capture, electromyography, and metabolic data for a final analysis using custom scripts (Matlab). We found that metabolic energy expenditure was reduced by 9.52  1.25% (p=0.011; Figure 1B). This, however, did not coincide with any changes in biological ankle angle, torque, or power. However, there was a trending reduction in peak soleus activity (p=0.052). Surprisingly, we did find a reduction in knee power (p=7.3x10-3; Figure 1C) and an accompanying decrease in semitendinosus activity (p=5.9x10-3). Despite the exoskeleton acting at the ankle joint, we found the clearest benefits at the knee joint—highlighting that complex, multi-joint adaptations may underly cost savings. We are continuing data collection to increase our statistical power and better understand the mechanisms that underly exoskeleton assistance.

Running gait adaptations among adolescent runners with soft tissue impairments following lateral ankle sprains.

Lateral ankle sprains (LAS) frequently lead to residual soft tissue impairments, often attributed to biomechanical dysfunction during movement. To compare running biomechanics between adolescent runners with soft tissue pathologies following LAS (injured) and healthy runners (control) and between limbs. Retrospective cohort study. Hospital-affiliated sports injury prevention center. Twenty-five adolescent runners with a history of LAS and current ankle impingement or tendinopathy (23 female, 2 male; age: 15 ± 2 years; body mass index [BMI]: 19.5 ± 2.5 kg/m2 ; symptom duration: 1.1 ± 0.9 years), and 23 healthy controls without any LAS history (19 female, 4 male; age: 15 ± 1 years; BMI: 19.2 ± 2.7 kg/m2 ) were included in this study. All participants completed a clinical gait assessment in which they ran at a self-selected speed on a force-plate instrumented treadmill, while two video cameras recorded two-dimensional sagittal and coronal views. Foot rotation, step width, contact time, and cadence were compared between groups and limbs (involved, uninvolved [or "better" for bilateral cases]) using a multivariate analysis of variance (MANOVA). Rearfoot landing and foot strike type were compared between groups and limbs using a chi-square analysis. The injured group had significantly increased step width (F = 4.71, p = .04; mean difference [MD] with SE: 1.5 [0.7] cm) compared to controls. The injured groups' involved limb had longer contact time (F = 4.62, p = .03; MDgroup : 12 [7] ms, MDlimb : 22 [11] ms) with more internal foot rotation (F = 14.60, p < .001; MDgroup : 2.2 [1.2] degrees, MDlimb : 4.2 [1.3] degrees) compared to controls and their contralateral limb. There were no significant differences for cadence (F = 2.43, p = .13; MD: 4 [3] steps/min), foot landing (X2 = 1.28, p = .53), or foot strike (X2 = 1.24, p = .54). Spatiotemporal and kinematic running adaptations may predispose young runners with initial LAS to secondary soft tissue dysfunction due to loss of stability from ligamentous structures and an overreliance on myotendinous control. Clinicians may consider targeting these maladaptations during gait-training interventions.

Mechanical loading prediction through accelerometry data during walking and running

ABSTRACT Currently, there is no way to assess mechanical loading variables such as peak ground reaction forces (pGRF) and peak loading rate (pLR) in clinical settings. The purpose of this study was to develop accelerometry-based equations to predict both pGRF and pLR during walking and running. One hundred and thirty one subjects (79 females; 76.9 ± 19.6 kg) walked and ran at different speeds (2–14 km·h−1) on a force plate–instrumented treadmill while wearing accelerometers at their ankle, lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland–Altman plots. Our pGRF prediction equation was compared with a reference equation previously published. Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration rate for pLR prediction. All pGRF equation coefficients of determination were above 0.96, and a good agreement between actual and predicted pGRF was observed, with a mean absolute percent error (MAPE) below 7.3%. Accuracy indices from our equations were better than previously developed equations. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF. Walking and running pGRF can be predicted with high accuracy by accelerometry-based equations, representing an easy way to determine mechanical loading in free-living conditions. The pLR prediction equations yielded a somewhat lower prediction accuracy compared with the pGRF equations.

How fiber dynamics of plantarflexor and dorsiflexor muscles based on EMG-driven approach can explain the metabolic cost at different gait speeds.

The aim of this study was to investigate the fiber dynamics of plantarflexor and dorsiflexor muscles and their association with the net metabolic rate (NCw). Metabolic, kinematic, kinetic, and electromyography measurements were made on seven young subjects while they walked on a force-plate instrumented treadmill at 1.00, 1.20, 1.40, 1.60, and 1.8m/s for 1:30 min. The net metabolic rate was computed, and a one degree-of freedom EMG-driven approach was used to extract the force generation ability (Fability), and active force-length (fAL) and force-velocity (fV) multiplier of each muscle. A one-way (speeds) repeated measures ANOVA was performed for each muscle and a multiple linear regression model was used to explain NCw. Fability was significantly affected by gait speed for the GasMed and the SOL muscles. The decrease of Fability for the SOL and the GasMed was accompanied by a decrease in the force-velocity multiplier. The peak muscle force for the SOL increased for the lowest speed compared to the higher speed, and for the TibAnt increased at high speed compared to low speed. In addition, Fability fAL, and fV of the SOL predicted over 58% of NCw and FMax of the TibAnt accounts for 39.9% of the variance in NCw. The increase of NCw with gait speed over the preferred walking speed can be partially explained by the decreasing capacity of the SOL muscle to produce muscle force and more specifically by the force-velocity relationship and an increase in muscle force for the TibAnt.

Effects of footwear cushioning on leg and longitudinal arch stiffness during running

During running, humans increase leg stiffness on more compliant surfaces through an in-series spring relationship to maintain constant support mechanics. Following this notion, the compliant midsole material of standard footwear may cause individuals to increase leg stiffness while running, especially in footwear with very thick midsoles. Recently, researchers have also proposed that footwear stiffness can affect the stiffness of the foot’s longitudinal arch (LA) via a similar mechanism. To test these ideas, we used 3D motion capture to record 20 participants running on a forceplate-instrumented treadmill while barefoot, and while wearing three types of sandals composed of materials ranging an order of magnitude in Young’s modulus: ethylene vinyl acetate (EVA), and two varieties of polyurethane rubber (R30 and R60). We calculated leg stiffness using standard methods, and measured LA stiffness based on medial midfoot kinematics. While there was an overall significant effect of footwear on leg stiffness (P = 0.047), post-hoc tests revealed no significant differences among individual pairs of conditions, and there was no effect of footwear on LA stiffness. However, participants exhibited significantly greater LA compression when barefoot than when running in EVA (P = 0.004) or R30 (P = 0.036) sandals. These results indicate that standard footwear midsole materials are too stiff to appreciably affect leg stiffness during running, meaning that increasing midsole thickness is unlikely to cause individuals to alter their leg stiffness. However, use of footwear does cause individuals to restrict LA compression when compared to running barefoot, and further research is needed to understand why.

Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor.

The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to estimate the vGRF require a force plate. However, in real-world scenarios, gait monitoring would not be limited to a laboratory setting. This paper reports a novel solution using machine learning algorithms to estimate the vGRF and the timing and magnitude of its peaks from data collected by a single inertial measurement unit (IMU) on one of the lower limb locations. Nine volunteers participated in this study, walking on a force plate-instrumented treadmill at various speeds. Four IMUs were worn on the foot, shank, distal thigh, and proximal thigh, respectively. A random forest model was employed to estimate the vGRF from data collected by each of the IMUs. We evaluated the performance of the models against the gold standard measurement of the vGRF generated by the treadmill. The developed model achieved a high accuracy with a correlation coefficient, root mean square error, and normalized root mean square error of 1.00, 0.02 body weight (BW), and 1.7% in intra-participant testing, and 0.97, 0.10 BW, and 7.15% in inter-participant testing, respectively, for the shank location. The difference between the reference and estimated passive force peak values was 0.02 BW and 0.14 BW with a delay of −0.14% and 0.57% of stance duration for the intra- and inter-participant testing, respectively; the difference between the reference and estimated active force peak values was 0.02 BW and 0.08 BW with a delay of 0.45% and 1.66% of stance duration for the intra- and inter-participant evaluation, respectively. We concluded that vertical ground reaction force can be estimated using only a single IMU via machine learning algorithms. This research sheds light on the development of a portable wearable gait monitoring system reporting the real-time vGRF in real-life scenarios.

The human foot functions like a spring of adjustable stiffness during running.

Like other animals, humans use their legs like springs to save energy during running. One potential contributor to leg stiffness in humans is the longitudinal arch (LA) of the foot. Studies of cadaveric feet have demonstrated that the LA can function like a spring, but it is unknown whether humans can adjust LA stiffness in coordination with more proximal joints to help control leg stiffness during running. Here, we used 3D motion capture to record 27 adult participants running on a forceplate-instrumented treadmill, and calculated LA stiffness using beam bending and midfoot kinematics models of the foot. Because changing stride frequency causes humans to adjust overall leg stiffness, we had participants run at their preferred frequency and frequencies 35% above and 20% below preferred frequency to test for similar adjustments in the LA. Regardless of which foot model we used, we found that participants increased LA quasi-stiffness significantly between low and high frequency runs, mirroring changes at the ankle, knee and leg overall. However, among foot models, we found that the model incorporating triceps surae force into bending force on the foot produced unrealistically high LA work estimates, leading us to discourage this modeling approach. Additionally, we found that there was not a consistent correlation between LA height and quasi-stiffness values among the participants, indicating that static LA height measurements are not good predictors of dynamic function. Overall, our findings support the hypothesis that humans dynamically adjust LA stiffness during running in concert with other structures of the leg.

Biomechanical walking mechanisms underlying the metabolic reduction caused by an autonomous exoskeleton.

BackgroundAnkle exoskeletons can now reduce the metabolic cost of walking in humans without leg disability, but the biomechanical mechanisms that underlie this augmentation are not fully understood. In this study, we analyze the energetics and lower limb mechanics of human study participants walking with and without an active autonomous ankle exoskeleton previously shown to reduce the metabolic cost of walking.MethodsWe measured the metabolic, kinetic and kinematic effects of wearing a battery powered bilateral ankle exoskeleton. Six participants walked on a level treadmill at 1.4 m/s under three conditions: exoskeleton not worn, exoskeleton worn in a powered-on state, and exoskeleton worn in a powered-off state. Metabolic rates were measured with a portable pulmonary gas exchange unit, body marker positions with a motion capture system, and ground reaction forces with a force-plate instrumented treadmill. Inverse dynamics were then used to estimate ankle, knee and hip torques and mechanical powers.ResultsThe active ankle exoskeleton provided a mean positive power of 0.105 ± 0.008 W/kg per leg during the push-off region of stance phase. The net metabolic cost of walking with the active exoskeleton (3.28 ± 0.10 W/kg) was an 11 ± 4 % (p = 0.019) reduction compared to the cost of walking without the exoskeleton (3.71 ± 0.14 W/kg). Wearing the ankle exoskeleton significantly reduced the mean positive power of the ankle joint by 0.033 ± 0.006 W/kg (p = 0.007), the knee joint by 0.042 ± 0.015 W/kg (p = 0.020), and the hip joint by 0.034 ± 0.009 W/kg (p = 0.006).ConclusionsThis study shows that the ankle exoskeleton does not exclusively reduce positive mechanical power at the ankle joint, but also mitigates positive power at the knee and hip. Furthermore, the active ankle exoskeleton did not simply replace biological ankle function in walking, but rather augmented the total (biological + exoskeletal) ankle moment and power. This study underscores the need for comprehensive models of human-exoskeleton interaction and global optimization methods for the discovery of new control strategies that optimize the physiological impact of leg exoskeletons.

Gait Parameter Estimation From a Miniaturized Ear-Worn Sensor Using Singular Spectrum Analysis and Longest Common Subsequence

This paper presents a new approach to gait analysis and parameter estimation from a single miniaturized ear-worn sensor embedded with a triaxial accelerometer. Singular spectrum analysis combined with the longest common subsequence algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance, and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20min on a treadmill with an increasing incline of 2% every 2min. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance, and stride times were obtained as 35.5 ±3.99 ms, 36.9 ±3.84 ms, and 17.9 ±2.29 ms, respectively.

Joint-level mechanics of the walk-to-run transition in humans.

Two commonly proposed mechanical explanations for the walk-to-run transition (WRT) include the prevention of muscular over-exertion (effort) and the minimization of peak musculoskeletal loads and thus injury risk. The purpose of this study was to address these hypotheses at a joint level by analysing the effect of speed on discrete lower-limb joint kinetic parameters in humans across a wide range of walking and running speeds including walking above and running below the WRT speed. Joint work, peak instantaneous joint power, and peak joint moments in the sagittal and frontal plane of the ankle, knee and hip from eight participants were collected for 10 walking speeds (30-120% of their WRT) and 10 running speeds (80-170% of their WRT) on a force plate instrumented treadmill. Of the parameters analysed, three satisfied our statistical criteria of the 'effort-load' hypothesis of the WRT. Mechanical parameters that provide an acute signal (peak moment and peak power) were more strongly associated with the gait transition than parameters that reflect the mechanical function across a portion of the stride. We found that both the ankle (peak instantaneous joint power during swing) and hip mechanics (peak instantaneous joint power and peak joint moments in stance) can influence the transition from walking to running in human locomotion and may represent a cascade of mechanical events beginning at the ankle and leading to an unfavourable compensation at the hip. Both the ankle and hip mechanisms may contribute to gait transition by lowering the muscular effort of running compared with walking at the WRT speed. Although few of the examined joint variables satisfied our hypothesis of the WRT, most showed a general marked increase when switching from walking to running across all speeds where both walking and running are possible, highlighting the fundamental differences in the mechanics of walking and running. While not eliciting the WRT per se, these variables may initiate the transition between stable walking and running patterns. Those variables that were invariant of gait were predominantly found in the swing phase.

Personal Motion Sensor Directed Rehabilitation after Lower Limb Reconstruction

INTRODUCTION: Open tibial fractures are managed in a variety of ways, but current practice fails to objectively assess short and long-term outcomes. Not only does this limit the scope for evaluation of management strategies, but it also prevents the provision of regular patient feedback and implementation of adaptive personalised rehabilitation programmes. As a result, total recovery time and the extent of functional recovery are likely to be suboptimal, with consequential poor rates of return to work. Advances in miniaturised, wireless sensor technology allows resource-sparing, pervasive data capture of multiple patient features in a non-specialised environment.1 Our aim was to develop and validate a robust, objective assessment method using a novel motion sensing system worn by the patient for use in both clinical and community settings (Hamlyn instrumented Functional Mobility Assessment – HiFMA). METHODS: We implemented a lightweight (7.4g) ear-worn accelerometer (e-AR sensor, Imperial College London) and tablet interface into a short activity protocol made up of tasks previously validated to assess different aspects of functional mobility.2–4 Twenty 3-month post-open tibial fracture patients and 10 healthy subjects completed the protocol, as well as a short-form health survey (SF-36) for comparison. Subjects’ performances were rated through extraction of multiple kinematic features from the accelerometer data; techniques previously validated using high-speed cameras and a force plate-instrumented treadmill.5 A psychometric evaluation was conducted to ascertain the reliability and validity of the HiFMA tool. RESULTS: The HiFMA demonstrated excellent reliability (Intraclass Correlation Coefficient > 0.90, p<0.001) and internal consistency (Cronbach’s Alpha = 0.94). Concurrent validity was demonstrated by correlation between HiFMA and SF-36 scores (Spearman’s Rho = 0.668, p = 0.005). Significant differences in HiFMA scores between healthy subjects and patients, sub-grouped according to Gustilo-Anderson fracture classification, were shown (KW-ANOVA, X2 = 18.6, p<0.001) (Figure 1).Figure 1: Performance scores from HiFMA tool compared to SF-36 health status.CONCLUSION: The HiFMA shows satisfactory reliability and validity to be considered for implementation into routine follow-up of patients with open tibial fractures. This will improve our ability to objectively profile and compare a wide variety of patient outcomes, whilst creating a platform for remote patient surveillance and early detection of complications. Furthermore, the methods described provide a real opportunity to revamp the recovery paradigm through dynamic, self-directed rehabilitation, with huge potential for positive impact in the cost and care quality of everyday clinical services.

Validation of an ear-worn sensor for gait monitoring using a force-plate instrumented treadmill

A force-plate instrumented treadmill (Hp Cosmos Gaitway) was used to validate the use of a miniaturised lightweight ear-worn sensor (7.4g) for gait monitoring. Thirty-four healthy subjects were asked to progress up to their maximum walking speed on the treadmill (starting at 5km/h, with 0.5km increments). The sensor houses a 3D accelerometer which measures medio-lateral (ML), vertical (VT) and anterior–posterior (AP) acceleration. Maximum signal ranges and zero crossings were derived from accelerometer signals per axis, having corrected for head motion and signal noise. The maximal force, measured by the instrumented treadmill correlated best with a combination of VT and AP acceleration (R-squared=0.36, p=0), and combined VT, ML, and AP acceleration (R-squared=0.36, p=0). Weight-acceptance peak force and impulse values also correlated well with VT and AP acceleration (Weight acceptance: R-squared=0.35, p=0, Impulse: 0.26, p=0), and combined VT, ML, and AP acceleration (Weight acceptance: R-squared=0.35, p=0, Impulse: 0.26, p=0). Zero crossing features on the ML axis provided an accurate prediction of the gait-cycle, with a mean difference of 0.03s (−0.01, 0.05 confidence intervals).

Pressure distribution beneath the heel while running in a cushioned shoe: A sagittal plane finite element model

During running, humans increase leg stiffness on more compliant surfaces through an in-series spring relationship to maintain constant support mechanics. Following this notion, the compliant midsole material of standard footwear may cause individuals to increase leg stiffness while running, especially in footwear with very thick midsoles. Recently, researchers have also proposed that footwear stiffness can affect the stiffness of the foot’s longitudinal arch (LA) via a similar mechanism. To test these ideas, we used 3D motion capture to record 20 participants running on a forceplate-instrumented treadmill while barefoot, and while wearing three types of sandals composed of materials ranging an order of magnitude in Young’s modulus: ethylene vinyl acetate (EVA), and two varieties of polyurethane rubber (R30 and R60). We calculated leg stiffness using standard methods, and measured LA stiffness based on medial midfoot kinematics. While there was an overall significant effect of footwear on leg stiffness (P = 0.047), post-hoc tests revealed no significant differences among individual pairs of conditions, and there was no effect of footwear on LA stiffness. However, participants exhibited significantly greater LA compression when barefoot than when running in EVA (P = 0.004) or R30 (P = 0.036) sandals. These results indicate that standard footwear midsole materials are too stiff to appreciably affect leg stiffness during running, meaning that increasing midsole thickness is unlikely to cause individuals to alter their leg stiffness. However, use of footwear does cause individuals to restrict LA compression when compared to running barefoot, and further research is needed to understand why.