Healthy Gait Research Articles

Learning Gait Models With Varying Walking Speeds

Lower-limb exoskeletons can reduce the therapist's burden and quantify repetitive gait training for patients with gait impairments. For patient's gait training, different walking speeds are required at different rehabilitation stages. However, due to the uniqueness of gait patterns, it is challenging for lower limb exoskeletons to generate individualized gait patterns for patients with different anthropometric parameters. This letter proposed learning-based gait models to learn and reconstruct gait patterns from healthy subject's gait database, including the Gait Parameters Model (GPM) and the Gait Trajectory Model (GTM). The GPM employs Neural Networks to predict gait parameters with a given desired walking speed and the anthropometric parameters of the subject. The GTM utilizes Kernelized Movement Primitives (KMP) to reconstruct gait patterns with the predicted gait parameters. The proposed approach has been tested on a lower limb exoskeleton named AIDER. Experimental results indicate that the reconstructed gait patterns are very similar to the subject's actual gait patterns for varying walking speeds.

GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait

The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. Therefore, we present GaitRec, a comprehensive and completely annotated large-scale dataset containing bi-lateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bi-lateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.

Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH.

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.

Real-Time Identification of Gait Events in Impaired Subjects Using a Single-IMU Foot-Mounted Device

Real time identification of gait events is a mandatory condition for adaptive or patient-tailored control of robotic devices during gait therapy. Despite most of the studies in the literature have reported high accuracy in the identification of gait phases for healthy subjects, most of them were not tested on impaired subjects and/or are not suitable for real-time implementations. In this paper, we evaluated the feasibility of some of the most known algorithms for identification of gait events. We propose a novel algorithm that exploits the advantages of the different approaches used for detection of gait events. We built a wearable sensor device with a single IMU placed back of the heel. Three subjects (a healthy subject, a hemiparetic and a myelopathic) worn the devices and performed an experimental protocol with overground and treadmill walking trials. Algorithms showed a high performance for healthy gait and their suitability for real-time implementations. However, none of the algorithms in the literature could maintain high accuracy during hemiparetic or myelopathic gait. Our algorithm obtained high accuracy for the three subjects: healthy (F1-score: 0.99), hemiparetic (0.97) and myelopathic (0.96). We aim to implement our proposal as part of the control loop of a robot during robotic gait therapy.

Bionics Design of Artificial Leg and Experimental Modeling Research of Pneumatic Artificial Muscles

In the research and development of intelligent prosthesis, some of performance test experiments are required. In order to provide an ideal experimental platform for the performance test of intelligent prosthesis, a heterogeneous biped walking robot model is proposed. Artificial leg is an important part of heterogeneous biped walking robot, and its main function is to simulate the disabled a healthy normal gait, which provides intelligent bionic legs gait to follow the target trajectory. The pneumatic artificial muscles (PAM) have good application in the artificial leg. The bionic design of artificial leg mainly includes the structure of hip joint, knee joint, and ankle joint, adopting the four-bar mechanism as the mechanical structure of the knee joint, and PAM are used as the driving source of the knee joint. Secondly, the PAM performance test platform is built to establish the relationship among output force, shrinkage rate, and input pressure under the measured isobaric conditions, and the mathematical model of PAM is established. Finally, the virtual prototype technology is used to build a joint simulation platform, and PID control algorithm is used for verification simulation. The results show that the artificial leg can follow the target trajectory.

High day-to-day repeatability of lower extremity muscle activation patterns and joint biomechanics of dual-belt treadmill gait: A reliability study in healthy young adults

PurposeThe reliability of lower extremity muscle activation patterns has not been clearly studied in a dual-belt instrumented treadmill environment. The primary study objective was to quantify the day-to-day reliability of quadriceps, hamstrings, gastrocnemius and gluteus medius activation patterns in healthy young adult gait. Secondarily, the reliability of spatiotemporal, and knee/hip motion and moment-based gait outcomes was assessed.Scope: 20 young adults were recruited and tested on two separate days. Using standardized procedures, participants were prepared for surface electromyography and lower extremity motion capture. All individuals walked on a dual-belt instrumented treadmill while muscle activation, segment motions and ground reaction forces were recorded. Sagittal plane motion and net external sagittal and frontal plane moments were calculated. Discrete biomechanical and muscle activation measures were calculated, and non-negative matrix factorization extracted amplitude and temporal muscle activation features. Intraclass Correlation Coefficients, Standard Error of Measurement and Minimum Detectable Change were calculated. ConclusionsHigh to excellent Intraclass correlation coefficients were found between visits for most primary and secondary outcomes. The absolute and relative reliability, including Minimum Detectable Change values, provided in this study support the use of dual-belt instrumented treadmill walking as an acceptable medium to collect biomechanical and lower extremity EMG outcomes for future studies.

Treadmill walking alters stride time dynamics in Parkinson’s disease

BackgroundTreadmill training may be used to improve gait rhythmicity in people with Parkinson’s disease. Treadmills, however, alter dynamical stride time fluctuations in healthy adults in a manner that mimics pathologic states, indicating the stride-to-stride fluctuations that characterize healthy gait are constrained. It is unclear if treadmills similarly alter dynamic gait properties in Parkinson’s disease. Research questionDo stride time fractal dynamics in individuals with Parkinson’s disease differ between treadmill and overground walking? MethodsFifteen participants with Parkinson’s disease and 15 healthy age-similar adults walked for 6 min in a conventional overground condition and on a treadmill while wearing inertial measurement units. Gait speed, stride times and stride time variability were measured. Fractal exponents (α) were computed with adaptive fractal analysis. Inferential statistics were analyzed with mixed model analyses of variance and post hoc simple effects tests. ResultsMean gait speeds decreased and stride times increased on the treadmill but did not differ between the Parkinson’s and control groups. Stride time variability was greater in the Parkinson’s than control group in both conditions. Most relevant to our research question, stride time fractal exponents were greater on the treadmill (mean α = .910) than overground (mean α = .797) in individuals with Parkinson’s disease, but not in healthy controls. SignificanceThe fractal scaling exponent α emanating from stride time fluctuations during treadmill walking increased toward a 1/f signal of α = 1.0 that has been interpreted as an optimal structural variability for gait. The clinical implication is that treadmill training may promote more efficient walking dynamics in people with Parkinson’s disease than conventional overground training.

The effect of mono- versus multi-segment musculoskeletal models of the foot on simulated triceps surae lengths in pathological and healthy gait

BackgroundEstimating muscle-tendon complex (MTC) lengths is important for planning of soft tissue surgery and evaluating outcomes, e.g. in children with cerebral palsy (CP). Conventional musculoskeletal models often represent the foot as one rigid segment, called a mono-segment foot model (mono-SFM). However, a multi-segment foot model (multi-SFM) might provide better estimates of triceps surae MTC lengths, especially in patients with foot deformities. Research questionWhat is the effect of a mono- versus a multi-SFM on simulated ankle angles and triceps surae MTC lengths during gait in typically developing subjects and in children with CP with equinus, cavovarus or planovalgus foot deformities? Methods50 subjects were included, 10 non-affected adults, 10 typically developing children, and 30 children with spastic CP and foot deformities. During walking trials, marker trajectories were collected for two marker models, including a mono- and multi-segment foot; respectively Newington gait model and Oxford foot model. Two musculoskeletal lower body models were constructed in OpenSim with either a mono- or multi-SFM based on the corresponding marker models. Normalized triceps surae MTC lengths (soleus, gastrocnemius medialis and lateralis) and ankle angles were calculated and compared between models using statistical parametric mapping RM-ANOVAs. Root mean square error values between simulated MTC lengths were compared using Wilcoxon signed-rank and rank-sum tests. ResultsMono-SFM simulated significantly more ankle dorsiflexion (7.5 ± 1.2°) and longer triceps surae lengths (difference; soleus:2.6 ± 0.29 %, gastrocnemius medialis:1.7 ± 0.2 %, gastrocnemius lateralis:1.8 ± 0.2%) than a multi-SFM. Differences between models were larger in children with CP compared to typically developing children and larger in the stance compared to the swing phase of gait. Largest differences were found in children with CP presenting with planovalgus (4.8 %) or cavovarus (3.8 %) foot deformities. SignificanceIt is advisable to use a multi-SFM in musculoskeletal models when simulating triceps surae MTC lengths, especially in individuals with planovalgus or cavovarus foot deformities.

Healthy Gait: Review of Anatomy and Physiology of Knee Joint

Healthy Gait: Review of Anatomy and Physiology of Knee Joint

Flat-Feet Prediction Based on a Designed Wearable Sensing Shoe and a PCA-Based Deep Neural Network Model

Gait is a significant factor that affects human health, and monitoring a person's gait with sensing devices during daily life can detect abnormal gait events that affect numerous physical health problems. In particular, flat feet can cause changes in alignment conditions of the foot, ankle, leg, pelvis and spine. The primary problem with previous studies of wearable devices for measuring gait have focused on quantitatively monitoring the degree of gait rather than the limited gait ability. The existing method of feeding back the degree of gait or activity does not consider the severity of the subject and is insufficient for qualitative evaluation or training of gait. The significance of this study is development of convenient detecting and long-term tracking tools that can be used by both patients and clinicians for prescreening flat feet and monitoring the progress of flat feet treatment. For wearable devices for flatfoot detection to be most effective, detection systems and algorithms must be accurate, robust, reliable and computationally-efficient. In this paper, we developed an integrated smart wearable gait-monitoring device comprised of three sensors: front force, rear force, and an ankle flex sensor. We propose a new flat feet detection methodology based on a dynamic sensing window and a deep neural network with scaled principal component analysis (PCA). We tested 24 subjects, including both those with healthy gait and flat-feet-affected gait. Our study shows that the proposed sensing devices could be worn comfortably. The proposed deep neural network (DNN) model outperformed the other five classifier algorithms considered, and the area under the curve (AUC) value of the method was 87.1%. This wearable device can thus be easily and simply used both by patients and doctors to monitor the progress of flat feet and prescreen for possible gait problems in daily life.

Computational analysis of EEG activity during stance and swing gait phases

Abstract Analysis of cortical electroencephalography (EEG) patterns may suggest novel neural dynamics related to healthy and diseased gait which could be critical for assessing neurological disorders. However, the dynamics of cortical involvement in walking is not clearly understood. In this study, using non-invasive EEG, we recorded and analyzed the stance and swing gait cycle phases in healthy volunteering subjects. Extracted spectral and temporal features of gait data for right toe-off and heel strike were ranked based, using machine learning algorithms to identify patterns related to swing and stance. Increased beta rhythms, positive and negative motor potentials for stance and swing could be targeted as biosignatures discriminating gait cycle phases. Identifying such biosignatures help classify stance and swing phases and may be pertinent to preclinical studies and in resource-limited environments where expensive equipment may not be accessible.

Design, Optimization and Energetic Evaluation of an Efficient Fully Powered Ankle-Foot Prosthesis With a Series Elastic Actuator

The use of powered ankle-foot prostheses for below-knee amputees leads to challenges like the peak power of the applied actuator and biomechanical features of the prosthesis foot. This paper proposes an efficient powered ankle-foot prosthesis with a series elastic actuator. By combining the geared five-bar spring (GFBS) mechanism and the traditional series elastic actuator (SEA), a series elastic with geared five-bar (SGFB) actuator is built. The new SGFB actuator has the benefits of both the GFBS and the SEA on mimicking biomechanics of the human ankle and reducing the peak power of the motor. The healthy walking gait in the experiment results indicates that the optimized SGFB prosthesis foot including a 150W Maxon DC motor can provide a 70kg subject enough net positive energy with an energy efficiency of 35.3% during normal speed walking in the treadmill trials. The experiment of the SGFB prosthesis foot in semi-active mode shows the advantage on closely mimicking the human biomechanics during the control dorsiflexion phase and the importance of injecting positive energy during the powered plantarflexion phase. The experiment results also show that the optimization of different parameters within the electromechanical model considering the efficiency of the whole drive train can effectively reduce the motor's peak power to 132 W by making the motor more effective in high-power conditions.

Neural Network Predictive Control of Swing Phase for a Variable-Damping Knee Prosthesis With Novel Hydraulic Valve

It is necessary to develop an effective knee prosthesis to recover the lost mobility of amputees. Variable-damping knee prostheses utilizing hydraulic dampers offer several advantages, but current researches are limited in approaching healthy knee behavior. To improve the gait symmetry, this study proposed a variable-damping knee prosthesis with a novel hydraulic damper using neural network predictive control (NNPC) scheme during swing phase. The external fan valve structure of the hydraulic damper can not only realize independent and continuous adjustment of flexion and extension damping by a single motor, but also can effectively avoid the damping adjustment failure caused by excessive load. NNPC was proposed as a controller to control the novel hydraulic damper during swing phase. The online simulation is carried out based on MATLAB utilizing the workbench composed of knee prosthesis prototype and self-built gait simulation and evaluation platform, so as to preliminarily verify the online feasibility and effectiveness of the algorithm at different speeds. The offline gait symmetry experiments are designed to more intuitively and effectively compare the performance of NNPC with the fuzzy logical control proposed in previous work. The results show that NNPC improves the gait symmetry from the fuzzy logical control at different walking speeds as the values of symmetry indices are significantly decrease in the range of 5.13% to 33.96%. These results indicate that the proposed variable-damping knee prosthesis can make a good performance on improving the approximation of healthy gait characteristics and meet the fundamental requirements of walking at various walking speeds.

Effects of experimentally induced fatigue on healthy older adults' gait: A systematic review.

IntroductionWhile fatigue is ubiquitous in old age and visibly interferes with mobility, studies have not yet examined the effects of self-reported fatigue on healthy older adults’ gait. As a model that simulates this daily phenomenon, we systematically reviewed eleven studies that compared the effects of experimentally induced muscle and mental performance fatigability on gait kinematics, variability, kinetics, and muscle activity in healthy older adults.MethodsWe searched for studies in databases (PubMed and Web of Science) using Fatigue, Gait, and Clinical conditions as the main terms and extracted the data only from studies that experimentally induced fatigue by sustained muscle or mental activities in healthy older adults.ResultsEleven studies were included. After muscle performance fatigability, six of nine studies observed increases in stride length, width, gait velocity (Effect Size [ES] range: 0.30 to 1.22), inter-stride trunk acceleration variability (ES: 2.06), and ankle muscle coactivation during gait (ES: 0.59, n = 1 study). After sustained mental activity, the coefficient of variation of stride outcomes increased (ES: 0.59 to 0.67, n = 1 study) during dual-task but not single-task walking.ConclusionMuscle performance fatigability affects spatial and temporal features of gait and, mainly, inter-stride trunk acceleration variability. In contrast, sustained mental activity tends only to affect step variability during dual tasking. A critical and immediate step for future studies is to determine the effects of self-reported fatigue on gait biomechanics and variability in healthy older adults to verify the viability of experimentally induced fatigue as a model for the study of gait adaptability in old age.

Autoregressive modeling to assess stride time pattern stability in individuals with Huntington\u2019s disease

BackgroundHuntington’s disease (HD) is a progressive, neurological disorder that results in both cognitive and physical impairments. These impairments affect an individual’s gait and, as the disease progresses, it significantly alters one’s stability. Previous research found that changes in stride time patterns can help delineate between healthy and pathological gait. Autoregressive (AR) modeling is a statistical technique that models the underlying temporal patterns in data. Here the AR models assessed differences in gait stride time pattern stability between the controls and individuals with HD. Differences in stride time pattern stability were determined based on the AR model coefficients and their placement on a stationarity triangle that provides a visual representation of how the patterns mean, variance and autocorrelation change with time. Thus, individuals who exhibit similar stride time pattern stability will reside in the same region of the stationarity triangle. It was hypothesized that individuals with HD would exhibit a more altered stride time pattern stability than the controls based on the AR model coefficients and their location in the stationarity triangle.MethodsSixteen control and twenty individuals with HD performed a five-minute walking protocol. Time series’ were constructed from consecutive stride times extracted during the protocol and a second order AR model was fit to the stride time series data. A two-sample t-test was performed on the stride time pattern data to identify differences between the control and HD groups.ResultsThe individuals with HD exhibited significantly altered stride time pattern stability than the controls based on their AR model coefficients (AR1 p < 0.001; AR2 p < 0.001).ConclusionsThe AR coefficients successfully delineated between the controls and individuals with HD. Individuals with HD resided closer to and within the oscillatory region of the stationarity triangle, which could be reflective of the oscillatory neuronal activity commonly observed in this population. The ability to quantitatively and visually detect differences in stride time behavior highlights the potential of this approach for identifying gait impairment in individuals with HD.

Mechanics of very slow human walking

Human walking speeds can be influenced by multiple factors, from energetic considerations to the time to reach a destination. Neurological deficits or lower-limb injuries can lead to slower walking speeds, and the recovery of able-bodied gait speed and behavior from impaired gait is considered an important rehabilitation goal. Because gait studies are typically performed at faster speeds, little normative data exists for very slow speeds (less than 0.6 ms{}^{-1}). The purpose of our study was to investigate healthy gait mechanics at extremely slow walking speeds. We recorded kinematic and kinetic data from eight adult subjects walking at four slow speeds from 0.1 ms{}^{-1} to 0.6 ms{}^{-1} and at their self-selected speed. We found that known relations for spatiotemporal and work measures are still valid at very slow speeds. Trends derived from slow speeds largely provided reasonable estimates of gait measures at self-selected speeds. Our study helps enable valuable comparisons between able-bodied and impaired gait, including which pathological behaviors can be attributed to slow speeds and which to gait deficits. We also provide a slow walking dataset, which may serve as normative data for clinical evaluations and gait rehabilitative devices.

Human Gait Recognition Using Hip, Knee and Ankle Joint Ratios

The estimation of joint angle ratios for healthy and afflicted subjects in characterizing the human gait has great significance in the development of limb prosthetics. The two dimensional analysis of human gait was performed and the ratio of hip to knee, knee to ankle, hip to ankle as well as the time taken for achieving a gait were determined. The percentage of affliction was computed based on the joint angle ratios and comparison was made with healthy gait. The joint ratios were fed as input to the driving system which comprises of six DC motors for the positioning of knee, hip and ankle during gait. Then different control strategies like P, PI and PID were tested. The t-test and ANOVA analysis were conducted between healthy, afflicted and PID controller to determine the significant difference between their joint angle ratios. The estimation of joint angle ratio improved the accuracy of the control system drive (desired position of knee, hip and ankle motors). The presence of oscillations in the output response was reduced for P and PI controllers. The implementation of PID controller eliminated the presence of peak overshoot and more settling time. Thus the joint angle ratio provides the best possible assistance to the disabled persons by appropriately compensating the affliction.

Total Ankle Arthroplasty: Is Ankle Plantarflexion Strength Important?

Category:Ankle ArthritisIntroduction/Purpose:Ankle push off power, which requires gastroc-soleus muscle strength, is a critical aspect of healthy gait and increases as gait speed increases. It is therefore surprising that one-to-two-year outcomes following total ankle arthroplasty (TAA) include improved gait speed but deficient ankle power. One possible explanation for low ankle power following TAA is ankle plantarflexion weakness. Information on plantarflexion strength is extremely limited in people before or after TAA. Evaluating plantarflexion strength may inform postoperative expectations and guide rehabilitation programs. The purpose of this study was to evaluate the change in ankle plantarflexion strength, ankle power during gait, and gait speed before and after TAA in people with end-stage ankle arthritis, and in comparison to a healthy matched control group.Methods:Twenty-five participants were included in this prospective case-control study. TAA group participants (n = 13) [mean (SD): Age 60.9 (15.3) years; BMI 30.53 (5.5) Kg/m2; 85% male] with end-stage ankle arthritis who received a TAA were evaluated preoperatively and six months postoperatively. Performance of adjunct soft tissue procedures were patient specific (5/13 participants received tendo-achilles lengthening). All patients received formal physical therapy. Healthy control participants (n=12) were matched to the TAA group on age, gender and BMI. Ankle peak isokinetic plantarflexion strength (torque at 60 and 120 degrees/second; Nm/kg) was measured with an instrumented dynamometer. Peak ankle power (joint torque x segmental velocity; W/kg) was calculated via three-dimensional multi-segment foot motion analysis while participants walked barefoot over a force plate at controlled speeds. Gait speed (m/s) was measured with the Six-Minute Walk Test. Appropriate nonparametric comparisons were made to evaluate differences across time, between limbs, and between groups.Results:Compared to preoperative values, involved limb ankle strength was preserved at 60 and 120 degrees/second following TAA (both p > 0.59). Postoperative involved limb ankle strength at both speeds were 37-56% lower than the non-involved limb and control group (all p < 0.05) (Figure 1). Similarly, involved limb ankle power was preserved following TAA (p = 0.43), but remained at least 38% lower than the non-involved limb or control group (both p < .01). A subset analysis revealed that TAA participants with tendo-achilles lengthening had 25-33% less involved limb postoperative ankle power and strength than TAA participants without tendo-achilles lengthening. Interestingly, gait speed increased following TAA (p = 0.01) and was similar to control group speeds [TAA 1.5 vs. Control 1.6 m/s; p = 0.59].Conclusion:Robust improvements in gait speed were observed following TAA. These values approached normative gait speed in spite of diminished ankle strength and power. Ankle plantarflexion weakness reduces the capacity to generate ankle power during gait, regardless of possible contributing factors (i.e. preexisting atrophy/weakness, tendo-achilles lengthening). Accordingly, improvements in gait speed were likely linked to proximal joint compensations (i.e. hip, knee). The long term consequences of plantarflexion weakness may negatively affect implant loading. Study findings provide new information and point to the importance of targeting strength during postoperative TAA rehabilitation, potentially adjusting strategies for patients receiving soft tissue lengthening procedures.

A probabilistic method to estimate gait kinetics in the absence of ground reaction force measurements

Human joint torques during gait are usually computed using inverse dynamics. This method requires a skeletal model, kinematics and measured ground reaction forces and moments (GRFM). Measuring GRFM is however only possible in a controlled environment. This paper introduces a probabilistic method based on probabilistic principal component analysis to estimate the joint torques for healthy gait without measured GRFM. A gait dataset of 23 subjects was obtained containing kinematics, measured GRFM and joint torques from inverse dynamics in order to obtain a probabilistic model. This model was then used to estimate the joint torques of other subjects without measured GRFM. Only kinematics, a skeletal model and timing of gait events are needed. Estimation only takes 0.28 ms per time instant. Using cross-validation, the resulting root mean square estimation errors for the lower-limb joint torques are found to be approximately 0.1 Nm/kg, which is 6–18% of the range of the ground truth joint torques. Estimated joint torque and GRFM errors are up to two times smaller than model-based state-of-the-art methods. Model-free artificial neural networks can achieve lower errors than our method, but are less repeatable, do not contain uncertainty information on the estimates and are difficult to use in situations which are not in the learning set. In contrast, our method performs well in a new situation where the walking speed is higher than in the learning dataset. The method can for example be used to estimate the kinetics during overground walking without force plates, during treadmill walking without (separate) force plates and during ambulatory measurements.

Clinical Applicability of an Existing Proportionality Scheme in Three-Segment Kinetic Foot Models.

An important methodological challenge in multi-segment kinetic foot models is to partition the total ground reaction force across different foot segments. Several studies applied a proportionality scheme based on the combination of a pressure- and force platform. A recent study highlighted distinct errors in the partitioning of shear forces when using this proportionality scheme. To date, the impact of this shear force partitioning error analysis on joint moment calculations using inverse dynamic calculations in pathological gait is not known. Hence, the goal of this study was to investigate the clinical applicability of an existing proportionality scheme by extending the shear force partitioning error analysis towards joint moment calculations. Both healthy (n = 10) and pathological gait (n = 10) was assessed using (I) an adjacent force plate method and (II) the estimation method based on an existing proportionality scheme. A correction factor matrix was developed to compensate for the shear force partitioning errors in the estimation method. Extending the shear force partitioning error analysis towards joint moments using inverse dynamic calculations, did not reveal significant differences when comparing corrected joint moments with the estimated joint moments in both healthy and pathological gait.