Estimation of foot joint kinetics in three and four segment foot models using an existing proportionality scheme: Application in paediatric barefoot walking

Recent studies which estimated foot segment kinetic patterns were found to have inconclusive data on one hand, and did not dissociate the kinetics of the chopart and lisfranc joint. The current study aimed therefore at reproducing independent, recently published three-segment foot kinetic data (Study 1) and in a second stage expand the estimation towards a four-segment model (Study 2).Concerning the reproducibility study, two recently published three segment foot models (Bruening et al., 2014; Saraswat et al., 2014) were reproduced and kinetic parameters were incorporated in order to calculate joint moments and powers of paediatric cohorts during gait. Ground reaction forces were measured with an integrated force/pressure plate measurement set-up and a recently published proportionality scheme was applied to determine subarea total ground reaction forces. Regarding Study 2, moments and powers were estimated with respect to the Instituto Ortopedico Rizzoli four-segment model. The proportionality scheme was expanded in this study and the impact of joint centre location on kinetic data was evaluated.Findings related to Study 1 showed in general good agreement with the kinetic data published by Bruening et al. (2014). Contrarily, the peak ankle, midfoot and hallux powers published by Saraswat et al. (2014) are disputed. Findings of Study 2 revealed that the chopart joint encompasses both power absorption and generation, whereas the Lisfranc joint mainly contributes to power generation.The results highlights the necessity for further studies in the field of foot kinetic models and provides a first estimation of the kinetic behaviour of the Lisfranc joint.     

Ankle and foot power in gait analysis: Implications for science,technology and clinical assessment

In human gait analysis studies, the entire foot is typically modeled as a single rigid-body segment; however, this neglects power generated/absorbed within the foot. Here we show how treating the entire foot as a rigid body can lead to misunderstandings related to (biological and prosthetic) foot function, and distort our understanding of ankle and muscle-tendon dynamics. We overview various (unconventional) inverse dynamics methods for estimating foot power, partitioning ankle vs. foot contributions, and computing combined anklefoot power. We present two case study examples. The first exemplifies how modeling the foot as a single rigid-body segment causes us to overestimate (and overvalue) muscle-tendon power generated about the biological ankle (in this study by up to 77%), and to misestimate (and misinform on) foot contributions; corroborating findings from previous multi-segment foot modeling studies. The second case study involved an individual with transtibial amputation walking on 8 different prosthetic feet. The results exemplify how assuming a rigid foot can skew comparisons between biological and prosthetic limbs, and lead to incorrect conclusions when comparing different prostheses/interventions. Based on analytical derivations, empirical findings and prior literature we recommend against computing conventional ankle power (between shank-foot). Instead, we recommend using an alternative estimate of power generated about the ankle joint complex (between shank-calcaneus) in conjunction with an estimate of foot power (between calcaneus-ground); or using a combined anklefoot power calculation. We conclude that treating the entire foot as a rigid-body segment is often inappropriate and ill-advised. Including foot power in biomechanical gait analysis is necessary to enhance scientific conclusions, clinical evaluations and technology development.     

Classification of deadlift biomechanics with wearable inertial measurement units

The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2 Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes.     

A constrained extended Kalman filter for the optimal estimate of kinematics and kinetics of a sagittal symmetric exercise

This paper presents a method for real-time estimation of the kinematics and kinetics of a human body performing a sagittal symmetric motor task, which would minimize the impact of the stereophotogrammetric soft tissue artefacts (STA). The method is based on a bi-dimensional mechanical model of the locomotor apparatus the state variables of which (joint angles, velocities and accelerations, and the segments lengths and inertial parameters) are estimated by a constrained extended Kalman filter (CEKF) that fuses input information made of both stereophotogrammetric and dynamometric measurement data. Filter gains are made to saturate in order to obtain plausible state variables and the measurement covariance matrix of the filter accounts for the expected STA maximal amplitudes. We hypothesised that the ensemble of constraints and input redundant information would allow the method to attenuate the STA propagation to the end results. The method was evaluated in ten human subjects performing a squat exercise. The CEKF estimated and measured skin marker trajectories exhibited a RMS difference lower than 4 mm, thus in the range of STAs. The RMS differences between the measured ground reaction force and moment and those estimated using the proposed method (9 N and 10 N m) were much lower than obtained using a classical inverse dynamics approach (22 N and 30 N m). From the latter results it may be inferred that the presented method allows for a significant improvement of the accuracy with which kinematic variables and relevant time derivatives, model parameters and, therefore, intersegmental moments are estimated.     

A biomechanical study of the relationship between running velocity and three-dimensional lumbosacral kinetics

Faster running is not performed with proportional increase in all joint torque/work exertions. Although previous studies have investigated lumbopelvic kinetics for a single velocity, it is unclear whether each lumbopelvic torque should increase for faster running. We examined the relationship between running velocity and lumbopelvic kinetics. We calculated the three-dimensional lumbosacral kinetics of 10 male sprinters during steady-state running on a temporary indoor running track at five target velocities: 3.0 (3.20 ± 0.16), 4.5 (4.38 ± 0.18), 6.0 (5.69 ± 0.47), 7.5 (7.30 ± 0.41), and maximal sprinting (9.27 ± 0.36 m/s). The lumbosacral axial rotation torque increased more markedly (from 0.37 ± 0.06 to 1.99 ± 0.46 Nm/kg) than the extension and lateral flexion torques. The increase in the axial rotation torque was larger above 7.30 m/s. Conversely, the extension and lateral flexion torques plateaued when running velocity increased above 7.30 m/s. Similar results were observed for mechanical work. The results indicate that faster running required larger lumbosacral axial rotation torque. Conversely, the extension and lateral flexion torques were relatively invariant to running velocity above 7 m/s, implying that faster running below 7 m/s might increase the biomechanical loads causing excessive pelvic posterior tilt and excessive pelvic drop which has the potential to cause pain/injury related to lumbopelvic extensors and lateral flexors, whereas these biomechanical loads might not relate with running velocity above 7 m/s.     

A sensitivity analysis method for the body segment inertial parameters based on ground reaction and joint moment regressor matrices

This paper presents a method allowing a simple and efficient sensitivity analysis of dynamics parameters of complex whole-body human model. The proposed method is based on the ground reaction and joint moment regressor matrices, developed initially in robotics system identification theory, and involved in the equations of motion of the human body. The regressor matrices are linear relatively to the segment inertial parameters allowing us to use simple sensitivity analysis methods. The sensitivity analysis method was applied over gait dynamics and kinematics data of nine subjects and with a 15 segments 3D model of the locomotor apparatus. According to the proposed sensitivity indices, 76 segments inertial parameters out the 150 of the mechanical model were considered as not influent for gait. The main findings were that the segment masses were influent and that, at the exception of the trunk, moment of inertia were not influent for the computation of the ground reaction forces and moments and the joint moments. The same method also shows numerically that at least 90% of the lower-limb joint moments during the stance phase can be estimated only from a force-plate and kinematics data without knowing any of the segment inertial parameters.     

A wearable solution for accurate step detection based on the direct measurement of the inter-foot distance

Accurate step detection is crucial for the estimation of gait spatio-temporal parameters. Although several step detection methods based on the use of inertial measurement units (IMUs) have been successfully proposed, they may not perform adequately when the foot is dragged while walking, when walking aids are used, or when walking at low speed. The aim of this study was to test an original step-detection method, the inter-foot distance step counter (IFOD), based on the direct measurement of the distance between feet. Gait data were recorded using a wearable prototype system (SWING^2DS), which integrates an IMU and two time-of-flight distance sensors (DSs). The system was attached to the medial side of the right foot with one DS positioned close to the forefoot (FORE_DS) and the other close to the rearfoot (REAR_DS). Sixteen healthy adults were asked to walk over ground for two minutes along a loop, including both rectilinear and curvilinear portions, during two experimental sessions. The accuracy of the IFOD step counter was assessed using a stereo-photogrammetric system as gold standard. The best performance was obtained for REAR_DS with an accuracy higher than 99.8% for the instrumented foot step and 88.8% for the non-instrumented foot step during both rectilinear and curvilinear walks. Key features of the IFOD step counter are that it is possible to detect both right and left steps by instrumenting one foot only and that it does not rely on foot impact dynamics. The IFOD step counter can be combined with existing IMU-based methods for increasing step-detection accuracy.     

A general model for estimating lower extremity inertial properties of individuals with transtibial amputation

Lower extremity joint moment magnitudes during swing are dependent on the inertial properties of the prosthesis and residual limb of individuals with transtibial amputation (TTA). Often, intact limb inertial properties (INTACT) are used for prosthetic limb values in an inverse dynamics model even though these values overestimate the amputated limb’s inertial properties. The purpose of this study was to use subject-specific (SPECIFIC) measures of prosthesis inertial properties to generate a general model (GENERAL) for estimating TTA prosthesis inertial properties. Subject-specific mass, center of mass, and moment of inertia were determined for the shank and foot segments of the prosthesis (n = 11) using an oscillation technique and reaction board. The GENERAL model was derived from the means of the SPECIFIC model. Mass and segment lengths are required GENERAL model inputs. Comparisons of segment inertial properties and joint moments during walking were made using three inertial models (unique sample; n = 9): (1) SPECIFIC, (2) GENERAL, and (3) INTACT. Prosthetic shank inertial properties were significantly smaller with the SPECIFIC and GENERAL model than the INTACT model, but the SPECIFIC and GENERAL model did not statistically differ. Peak knee and hip joint moments during swing were significantly smaller for the SPECIFIC and GENERAL model compared with the INTACT model and were not significantly different between SPECIFIC and GENERAL models. When subject-specific measures are unavailable, using the GENERAL model produces a better estimate of prosthetic side inertial properties resulting in more accurate joint moment measurements for individuals with TTA than the INTACT model.     

Overcoming the limitations of the Harmonic Ratio for the reliable assessment of gait symmetry

The Harmonic Ratio (HR) is an index based on the spectral analysis of lower trunk accelerations that is commonly used to assess the quality of gait. However, it presents several issues concerning reliability and interpretability. As a consequence, the literature provides very different values albeit corresponding to the same populations. In the present work, an improved harmonic ratio (iHR) was defined, relating the power of the intrinsic harmonics (i.e. associated with the symmetric component of gait) to the total power of the signal for each stride, leading to a normalised index ranging from 0 to 100%. The effect of the considered number of harmonics and strides on the estimate of both HR and iHR was assessed. The gait of three groups of volunteers was investigated: young healthy adults, elderly women and male trans-femoral amputees. Both HR and iHR were able to discriminate gait deviations from the gait of young healthy adults. Moreover, iHR proved to be more robust with respect to the number of considered harmonics and strides, and to exhibit a lower inter-stride variability. Additionally, using a normalised index as iHR led to a more straightforward interpretation and improved comparability. The importance of standardised conditions for the index evaluation was unveiled, and, in order to enhance the future comparability of the index, the following guidelines were presented: considering at least 20 harmonics and 20 strides; expressing the acceleration components in a repeatable, anatomical, local system of reference; and evaluating the iHR index, rather than the traditional HR.     

3D finite element model of the diabetic neuropathic foot: A gait analysis driven approach

Diabetic foot is an invalidating complication of diabetes that can lead to foot ulcers. Three-dimensional (3D) finite element analysis (FEA) allows characterizing the loads developed in the different anatomical structures of the foot in dynamic conditions. The aim of this study was to develop a subject specific 3D foot FE model (FEM) of a diabetic neuropathic (DNS) and a healthy (HS) subject, whose subject specificity can be found in term of foot geometry and boundary conditions. Kinematics, kinetics and plantar pressure (PP) data were extracted from the gait analysis trials of the two subjects with this purpose. The FEM were developed segmenting bones, cartilage and skin from MRI and drawing a horizontal plate as ground support. Materials properties were adopted from previous literature. FE simulations were run with the kinematics and kinetics data of four different phases of the stance phase of gait (heel strike, loading response, midstance and push off). FEMs were then driven by group gait data of 10 neuropathic and 10 healthy subjects. Model validation focused on agreement between FEM-simulated and experimental PP.     

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.     

A comparison of dorsal and heel plate foot tracking methods on lower extremity dynamics

The primary method to model ankle motion during inverse dynamic calculations of the lower limb is through the use of skin-mounted markers, with the foot modeled as a rigid segment. Motion of the foot is often tracked via the use of a marker cluster triad on either the dorsum, or heel, of the foot/shoe. The purpose of this investigation was to evaluate differences in calculated lower extremity dynamics during the stance phase of gait between these two tracking techniques. In an analysis of 7 subjects, it was found that sagittal ankle angles and sagittal ankle, hip and knee moments were strongly correlated between the two conditions, however, there was a significant difference in peak ankle plantar flexion and dorsiflexion angles. Frontal ankle angles were only moderately correlated and there was a significant difference in peak ankle eversion and inversion, resulting in moderate correlations in frontal plane moments and a significant difference in peak hip adductor moments. We demonstrate that the technique used to track the foot is an important consideration in interpreting lower extremity dynamics for clinical and research purposes.     

The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units

The use of inertial measurement units (IMUs) for gait analysis has emerged as a tool for clinical applications. Shank gyroscope signals have been utilized to identify heel-strike and toe-off, which serve as the foundation for calculating temporal parameters of gait such as single and double limb support time. Recent publications have shown that toe-off occurs later than predicted by the dual minima method (DMM), which has been adopted as an IMU-based gait event detection algorithm. In this study, a real-time algorithm, Noise-Zero Crossing (NZC), was developed to accurately compute temporal gait parameters. Our objective was to determine the concurrent validity of temporal gait parameters derived from the NZC algorithm against parameters measured by an instrumented walkway. The accuracy and precision of temporal gait parameters derived using NZC were compared to those derived using the DMM. The results from Bland-Altman Analysis showed that the NZC algorithm had excellent agreement with the instrumented walkway for identifying the temporal gait parameters of Gait Cycle Time (GCT), Single Limb Support (SLS) time, and Double Limb Support (DLS) time. By utilizing the moment of zero shank angular velocity to identify toe-off, the NZC algorithm performed better than the DMM algorithm in measuring SLS and DLS times. Utilizing the NZC algorithm’s gait event detection preserves DLS time, which has significant clinical implications for pathologic gait assessment.     

Estimating the L5S1 flexion/extension moment in symmetrical lifting using a simplified ambulatory measurement system

Mechanical loading of the spine has been shown to be an important risk factor for the development of low-back pain. Inertial motion capture (IMC) systems might allow measuring lumbar moments in realistic working conditions, and thus support evaluation of measures to reduce mechanical loading. As the number of sensors limits applicability, the objective of this study was to investigate the effect of the number of sensors on estimates of L5S1 moments.Hand forces, ground reaction forces (GRF) and full-body kinematics were measured using a gold standard (GS) laboratory setup. In the ambulatory setup, hand forces were estimated based on the force plates measured GRF and body kinematics that were measured using (subsets of) an IMC system. Using top-down inverse dynamics, L5S1 flexion/extension moments were calculated.RMSerrors (Nm) were lowest (16.6) with the full set of 17 sensors and increased to 20.5, 22 and 30.6, for 8, 6 and 4 sensors. Absolute errors in peak moments (Nm) ranged from 17.7 to 16.4, 16.9 and 49.3 Nm, for IMC setup’s with 17, 8, 6 and 4 sensors, respectively. When horizontal GRF were neglected for 6 sensors, RMSerrors and peak moment errors decreased from 22 to 17.3 and from 16.9 to 13 Nm, respectively.In conclusion, while reasonable moment estimates can be obtained with 6 sensors, omitting the forearm sensors led to unacceptable errors. Furthermore, vertical GRF information is sufficient to estimate L5S1 moments in lifting.     

A wearable system for pre-impact fall detection

Unique features of body segment kinematics in falls and activities of daily living (ADL) are applied to make automatic detection of a fall in its descending phase, prior to impact, possible. Fall-related injuries can thus be prevented or reduced by deploying fall impact reduction systems, such as an inflatable airbag for hip protection, before the impact. In this application, the authors propose the following hypothesis: “Thigh segments normally do not exceed a certain threshold angle to the side and forward directions in ADL, whereas this abnormal behavior occurs during a fall activity”. Torso and thigh wearable inertial sensors (3D accelerometer and 2D gyroscope) are used and the whole system is based on a body area network (BAN) for the comfort of the wearer during a long term application. The hypothesis was validated in an experiment with 21 young healthy volunteers performing both normal ADL and fall activities. Results show that falls could be detected with an average lead-time of 700 ms before the impact occurs, with no false alarms (100% specificity), a sensitivity of 95.2%. This is the longest lead-time achieved so far in pre-impact fall detection.     

A little bit faster: Lower extremity joint kinematics and kinetics as recreational runners achieve faster speeds

There appears a linear relationship between small increases in running speed and cardiovascular health benefits. Encouraging or coaching recreational runners to increase their running speed to derive these health benefits might be more effective if their joint level kinematic and kinetic strategy was understood. The aim of this investigation was to compare the peak sagittal plane motions, moments, and powers of the hip, knee and ankle at 85%, 100%, 115% and 130% of self-selected running speed. Overground running data were collected in 12 recreational runners (6 women, 6 men) with a full body marker set using a 12-camera Vicon MX system with an AMTI force plate. Kinematics and kinetics were analyzed with Vicon Nexus software. Participants chose to run at 2.6 ± 0.5 m/s (85%); 3.0 ± 0.5 m/s (100%); 3.3 ± 0.5 m/s (115%); and 3.7 ± 0.5 m/s (130%); these four speeds approximately correspond to 6:24-, 5:33-, 5:03-, and 4:30-min kilometer running paces. Running speed had a significant effect (P < 0.05) on peak kinematic and kinetic variables of the hips, knees and ankles, with peak sagittal hip moments invariant (P > 0.54) and the peak sagittal ankle power generation (P < 0.0001) the most highly responsive variable. The timing of the peak sagittal extensor moments and powers at the hip, knee and ankle were distributed across stance in a sequential manner. This study shows that running speed affects lower limb joint kinematics and kinetics and suggests that specific intersegmental kinetic strategies might exist across the narrow range of running speeds.     

A novel functional calibration method for real-time elbow joint angles estimation with magnetic-inertial sensors

Magnetic-inertial measurement units (MIMUs) are often used to measure the joint angles between two body segments. To obtain anatomically meaningful joint angles, each MIMU must be computationally aligned (i.e., calibrated) with the anatomical rotation axes. In this paper, a novel four-step functional calibration method is presented for the elbow joint, which relies on a two-degrees-of-freedom elbow model. In each step, subjects are asked to perform a simple task involving either one-dimensional motions around some anatomical axes or a static posture. The proposed method was implemented on a fully portable wearable system, which, after calibration, was capable of estimating the elbow joint angles in real time. Fifteen subjects participated in a multi-session experiment that was designed to assess accuracy, repeatability and robustness of the proposed method. When compared against an optical motion capture system (OMCS), the proposed wearable system showed an accuracy of about 4° along each degree of freedom. The proposed calibration method was tested against different MIMU mountings, multiple repetitions and non-strict observance of the calibration protocol and proved to be robust against these factors. Compared to previous works, the proposed method does not require the wearer to maintain specific arm postures while performing the calibration motions, and therefore it is more robust and better suited for real-world applications.