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A novel approach was designed to regulate the stepping movements in human by means of noninvasive electrical transcutaneous spinal cord stimulation (TSCS) to activate the flexor/extensor motor pools of the lower limbs in the gait cycle. Selective stimulation was delivered automatically based on signals from gyroscope sensors, which served to detect the stance and swing phases. The initiation of hip extension was a trigger for stimulating extensor pools (L1) during the stance phase, and the initiation of hip flexion was a trigger for stimulating flexor motor pools (T11) during the swing phase. In healthy subjects (n = 6) walking on a treadmill, stimulation at L1 with a frequency of 15 Hz decreased the duration of stance phase by 4% (p = 0.0457), increased the amplitude of movements in the hip joint by 11% (p = 0.0266), decreased the movement amplitude in the ankle joint by 17% (p = 0.0081), and increased EMG activities of the extensors vastus lateralis (VL) (by 31%, p = 0.0441) and gastrocnemius medialis (GM) (by 17%, p = 0.0465) and flexors biceps femoris (BF) (by 26%, p = 0.4637) and tibialis anterior (TA) (by 21%, p = 0.0215) relative to walking without stimulation. Stimulation at Т11 with a frequency of 30 Hz reduced the stance phase duration by 3% (p = 0.0318) and increased the amplitude of movements in the hip joint by 12% (p = 0.0467), the knee lifting by 25% (p = 0.0001), and the terminal anthropometric point height above the surface of support by 19% (p = 0.0001). The changes were accompanied by increases in muscle activities of the flexors BF (by 18%, p = 0.230) and TA (by 14%, p = 0.0170). The reciprocity coefficient decreased in thigh muscles by 15% (p = 0.0301) and increased in shin muscles by 5% (p = 0.0452). Alternating spatiotemporal stimulation at L1 and T11 did not significantly change the durations of the gait cycle and its phases, but changed the kinematic characteristics of movements. The amplitude of movements in the hip joint increased in the stance phase. In the swing phase, higher values were observed for the amplitude of movements in the hip joint, the knee lifting, and the terminal anthropometric point height above the surface of support. EMG activities of thigh and shin muscles increased during stimulation at L1 + T11. EMG activity of extensors was higher than that of flexors in the stance phase, while flexor EMG activity exceeded extensor activity in the swing phase. Thus, the data obtained show the possibility of TSCS selectively to activate the motor pools of the lower extremities, and control their activity to regulate the phases of the stepping cycle during human locomotion.

Most patients with advanced cancer develop skeletal muscle atrophy called sarcopenia, which reduces treatment tolerance and social activity and worsens the prognosis.

Assessment of gait forms an integral part of the clinical examination of the lower limbs. Normal gait requires stability and adequate clearance and positioning of the limb throughout the gait cycle. Gait disturbances arise secondary to either musculoskeletal disorders or neuromuscular disorders. Disease processes and injuries cause characteristic changes in gait that are clinically observed dependent on the affected area (i.e. hip, knee, foot or ankle), aetiology and any resulting deformities. In this article we review the normal gait cycle and how it varies with certain disease processes and injuries.

Center of pressure (COP) trajectories of human can maintain regulation of forward progression and stability of lateral sway during walking. The insole pressure system can only detect COP trajectories of each foot during single stance. In this study, we developed artificial neural network models that could present COP trajectories in an integrated coordinate system during a complete gait cycle using pressure information of the insole system. A feed forward artificial neural network (FFANN) and a long short-term memory (LSTM) model were developed. For FFANN, among 198 pressure sensors from Pedar-X insoles, proper input variables were selected using sequential forward selection to reduce input dimension. The LSTM model used all 198 signals as inputs because of its self-learning characteristic. As results of cross-validation, the FFANN model showed correlation coefficients of 0.98-0.99 and 0.93-0.95 in anterior/posterior and medial/lateral directions, respectively. For the LSTM model, correlation coefficients were similar to those of FFANN. However, the relative root mean square error (12.5%) of the FFANN model was higher than that (9.8%) of the LSTM model in medial/lateral direction (p = 0.03). This study can be used for quantitative evaluation of clinical diagnosis and rehabilitation status for patient with various diseases through further training using varied databases. Graphical abstract Architectures of neural networks developed in this study (a feed forward artificial neural network; b LSTM network).

Gait is a key barometer to analyze human body conditions. We propose a personalized gait analysis framework which can diagnose a possible muscularskeletal disorders with a single gait cycle. Our framework built over a gait manifold which reveals the principle kinematic characteristics in the temporal pose sequence. Body parameters such as muscle, skeleton, and joint limits for an arbitrary gait cycle can be approximated by measuring similarity in the small latent space. We present a physical gait simulator to enrich the gait space paired with the body conditions.

To evaluate the kinematics/kinetics of the ankle, knee, hip in the sagittal plane in adolescents with recurrent patellar dislocation in comparison to a healthy control. Case–control study. Eighty-eight knees (67 patients) with recurrent patellar dislocation (mean age 14.8 years ± 2.8 SD) were compared to 54 healthy knees (27 individuals, 14.9 years ± 2.4 SD). Kinematics/kinetics of ankle, knee, hip, and pelvis were captured using 3D-gait analysis (VICON, 12 cameras, 200 Hz, Plug-in-Gait, two force plates). One cycle (100%) consisted of 51 data-points. The mean of six trials was computed. The loading-response increased by 0.02 s ± 0.01SE (10.8%) with dislocations (0.98% of total gait, P

Functional electrical stimulation (FES) is an effective method to induce muscle contraction and to improve movements in individuals with injured central nervous system. In order to develop the FES systems for an individual with gait impairment, an appropriate control strategy must be designed to accurate tracking performance. The goal of this study is to present a method for designing proportional-derivative (PD) and sliding mode controllers (SMC) for the FES applied to the musculoskeletal model of an ankle joint to track the desired movements obtained by experiments on two healthy individuals during the gait cycle. Simulation results of the developed controller on musculoskeletal model of the ankle joint illustrated that the SMC is able to track the desired movements more accurately than the PD controller and prevents oscillating patterns around the experimentally measured data. Therefore, the sliding mode as the nonlinear method is more robust in face to unmodeled dynamics and model errors and track the desired path smoothly. Also, the required control effort is smoother in SMC with respect to the PD controller because of the nonlinearity.

Knee osteoarthritis can alter gait variability. However, few studies have compared the temporal factors of the gait cycle between patients with knee osteoarthritis and healthy subjects. Furthermore, no studies have investigated the relationship between gait variability and potential contributing factors (knee joint functions such as muscle strength) in knee osteoarthritis. The first objective of this study was to compare gait cycle variability between female patients with knee osteoarthritis and healthy elderly women to determine gait characteristics in patients with knee osteoarthritis. The second objective was to examine whether gait cycle variability in knee osteoarthritis is associated with potential contributing factors. Twenty-four female patients diagnosed with knee osteoarthritis and 12 healthy elderly women participated. Gait cycle variability (coefficient of variation of gait cycle time), knee extension range of motion, knee extension strength, 5-meter walk test, Timed Up & Go Test, and Western Ontario and McMaster Universities Osteoarthritis Index were measured. All assessment results were compared between the knee osteoarthritis and healthy groups. Gait cycle variability was significantly higher in the knee osteoarthritis group (3.2%±1.5%) compared to the healthy group (2.1%±0.7%). A significant positive correlation was found between the gait cycle variability and 5-meter walk test (r=0.46) and Western Ontario and McMaster Universities Osteoarthritis Index (r=0.43). The gait of patients with knee osteoarthritis may be more unstable than that of healthy individuals. In addition, unstable gait may be associated with gait speed and quality of life. Therefore, we believe that rehabilitation to improve unstable gait can enhance the quality of life of patients with knee osteoarthritis.

The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been recently proposed to cope with the high intra-subject variability of muscle activation patterns and to allow the extraction of principal activations (PAs), defined as those muscle activation intervals that are strictly necessary to perform a specific task. To assess differences between different PAs, gait cycle normalization techniques are needed to handle between- and within-subject variability. The aim of this contribution is to assess the effect of two different time-normalization techniques (Linear Length Normalization and Piecewise Linear Length Normalization) on PA extraction, in terms of inter-subject similarity. Results demonstrated no statistically significant differences in the inter-subject similarity between the two tested approaches, revealing, on the average, inter-subject similarity values higher than 0.64. Moreover, a statistically significant difference in the inter-subject similarity among muscles was assessed, revealing a higher similarity of PAs extracted considering the distal lower limb muscles. In conclusion, our results demonstrated that PAs extracted from healthy subjects during a walking task at comfortable walking speed are not affected by the time-normalization approach implemented.

Human gait cycle fluctuates during walking even with a constant walking speed. Such fluctuation, referred to as the gait cycle variability, is often quantified by measuring stride intervals, i.e., time intervals between one and the subsequent heel contacts of one of the feet. It has been known that gait cycle variability in healthy young people exhibits a positive persistency, which tends to be lost by aging and neurological diseases, including Parkinson's disease, suggesting that the positive persistency reflects endogenous stability of gait. We have reported previously that the degree of positive persistency decreased during distracted walking with smartphone-gaming induced cognitive load. Here, we hypothesized that the decrease in the positive persistency is caused by a change in the neural activity at supra-spinal level. To find evidence for this hypothesis, we measured electroencephalography (EEG) during walking with and without the cognitive load. As a result, though preliminary, amplitude of the EEG at gamma band increased at a timing prior to every heel contact in the cognitive load condition compared to the control condition. This preliminary result suggests that a supra-spinal gait control mechanism is involved in the gait cycle variability.

Background Gait event detection (GED) is an important aspect in identifying and interpret a user’s gait to assess gait abnormalities and design intelligent assistive devices. Research question There is a need to develop robust GED models that can accurately detect various gait instances in different scenarios and environments. Methods This paper presents a novel method of detecting heel strikes (HS) and toe offs (TO) during the user’s gait cycle using a modified Long Short-Term Memory (LSTM) networks approach. The method was tested on a database from Movement Analysis in Real-world Environments using Accelerometers (MAREA) (n = 20 healthy subjects) that consisted of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. Modifications include oversampling, composite accelerations and optimizing the LSTM network architecture were made. Results Performance of our modified model was found to be better than six state-of-the-art GED algorithms, with a median F1 score of 0.98 for Heel Strikes and 0.98 for Toe Offs in the scenario of steady walking in an indoor environment, and a median F1 score of 0.94 for Heel Strikes and 0.68 for Toe-offs in the scenario of walking and running in an outdoor environment. Significance This paper highlights the potential of the single proposed model to be an alternative to the six GED models in gait detection under various conditions.

PURPOSE The purpose of this study was to determine the effects of treadmill gait training according to obstacle crossing on the hip, knee and ankle joint motion during gait cycle in patients with post stroke hemiplegia. METHODS Twenty-one patients with post stroke hemiplegia were divided into 3 groups as treadmill gait training with obstacle crossing group (TOG, n=7), treadmill gait training without obstacle crossing group (TGG, n=7) and control group (CON, n=7). TOG and TGG were performed treadmill gait training with and without obstacle crossing for 20 minutes, three times a week, and for 8 weeks. Hip, knee and ankle joint motion were measured before and after treadmill gait training. Statistical analyses was done using the ANCOVA and paired t-test. RESULTS Hip, knee and ankle joint motion were significantly increased in TOG and TGG after 8 weeks compared to before (p

Gait training is widely used to treat gait abnormalities. Traditional gait measurement systems are limited to instrumented laboratories. Even though gait measurements can be made in these settings, it is challenging to estimate gait parameters robustly in real-time for gait rehabilitation, especially when walking over-ground. In this paper, we present a novel approach to track the continuous gait cycle during overground walking outside the laboratory. In this approach, we instrument standard footwear with a sensorized insole and an inertial measurement unit. Artificial neural networks are used on the raw data obtained from the insoles and IMUs to compute the continuous percentage of the gait cycle for the entire walking session. We show in this paper that when tested with novel subjects, we can predict the gait cycle with a Root Mean Square Error (RMSE) of 7.2%. The onset of each cycle can be detected within an RMSE time of 41.5 ms with a 99% detection rate. The algorithm was tested with 18840 strides collected from 24 adults. In this paper, we tested a combination of fully-connected layers, an Encoder-Decoder using convolutional layers, and recurrent layers to identify an architecture that provided the best performance.

Background Between 39% and 69% of women wear high-heeled shoes. Previous studies have shown that high heels can induce adverse biomechanical effects, but variables, such as walking speed, structure, style, and material of the shoes, have not been strictly controlled. The purpose of this study is to investigate the influences of high-heeled shoe parameters on gait cycle, center of pressure trajectory, and plantar pressure in young females. Methods Twenty healthy adult females were recruited to participate in this study. Subjects walked on a treadmill at a fixed speed (1 m/s). Overall, six pairs of high-heeled shoes were evaluated, which consisted of two heel types (thin and thick) and three different heel heights (low (3 cm), medium (6 cm), and high (8.2 cm)). Subjects also wore one pair of flat shoes (with 0.6 cm heel height) as the control group, while they walked on the treadmill at the same speed. Results and their significance were evaluated using a Heel Height × Heel Type Two way repeated ANOVA Test. Results The main parameters measured were the gait cycle, center of pressure parameters, peak pressure (PP), maximum force, contact area (CA), and the force time integral (impulse). The comparison of these parameters, conducted when the volunteers wore thick heel and flat shoes at different walking conditions, indicated that thin heels caused significant increases in the preswing parameter, CA, and PP exerted on the first toe and first metatarsus. Increased heel heights yielded smaller gait line lengths, single support lines, and smaller hindfoot areas. By contrast, increased anterior–posterior positions and plantar pressure parameter values were noted in the case of the forefoot. Conclusions Data analyses showed significant differences in plantar pressure distribution associated with the heel height and heel type at increased pressure in the central forefoot region and decreased pressure in the midfoot and heel sections, which consisted of the increased anterior shift. The implication of this study is to provide scientific advice to women for their choices of high heel shoes.

We propose a method of gait recognition just from a single image for the first time, which enables latency-free gait recognition. To mitigate large intra-subject variations caused by a phase (gait pose) difference between a matching pair of input single images, we first reconstruct full gait cycles of image sequences from the single images using an auto-encoder framework, and then feed them into a state-of-the-art gait recognition network for matching. Specifically, a phase estimation network is introduced for the input single image, and the gait cycle reconstruction network exploits the estimated phase to mitigate the dependence of an encoded feature on the phase of that single image. This is called phase-aware gait cycle reconstructor (PA-GCR). In the training phase, the PA-GCR and recognition network are simultaneously optimized to achieve a good trade-off between reconstruction and recognition accuracies. Experiments on three gait datasets demonstrate the significant performance improvement of this method.

Background: Chronic ankle instability (CAI) has previously been linked to altered lower limb kinematics and muscle activation characteristics during walking, though little research has been performed analysing the full time-series across the stance and swing phases of gait. Research question: The aim of this study was to compare trunk and lower limb kinematics and muscle activity between those with chronic ankle instability and healthy controls. Methods: Kinematics and muscle activity were measured in 18 (14 males, 4 females) healthy controls (age 22.4 ± 3.6 years, height 177.8 ± 7.6 cm, mass 70.4 ± 11.9 kg, UK shoe size 8.4 ± 1.6), and 18 (13 males, 5 females) participants with chronic ankle instability (age 22.0 ± 2.7 years, height 176.8 ± 7.9 cm, mass 74.1 ± 9.6 kg, UK shoe size 8.1 ± 1.9) during barefoot walking trials, using a combined Helen Hayes and Oxford foot model. Surface electromyography (sEMG) was recorded for the tibialis anterior and gluteus medius. Full curve statistical parametric mapping was performed using independent and paired-samples T-tests. Results: No significant differences were observed in kinematic or sEMG variables between or within groups for the duration of the swing phase of gait. A significantly increased forefoot-tibia inversion was seen in the CAI affected limb when compared to the CAI unaffected limb at 4-16% stance (p = 0.039). No other significant differences were observed. Significance: There appears to be no differences in muscle activation and movement between CAI and healthy control groups. However, participants with CAI exhibited increased inversion patterns during the stance phase of gait in their affected limb compared to their unaffected limb. This may predispose those with CAI to episodes of giving way and further ankle sprains.

International audience; The hip is considered as the most loaded joint in the human body, it can support up to 4 times the weight of the body. Diseases of this joint have become very widespread and are increasingly affecting younger populations. At their advanced stage, prosthesis remains the best medical solution to repair the damaged joint. To predict early deterioration and increase the lifespan of the prosthetic solutions, it is necessary to study the biomechanical behaviour of the femoral bone tissue. Bone is a tissue, which has capacity for continuous remodelling under the mechanical stimulus. This process plays central role in the context of implants and prostheses placed in it. Possibility of foreseeing the consequences of implant placement on the mechanical fields, and consequently on the process of bone remodelling, is crucial for implant designers. Insertion of non-natural parts with different mechanical properties into the bone leads to a change in stress distribution, and thus can generate the disruption of homeostasis, in which the healthy bone is. Moreover, the repetitive or cyclic loading of bone with evolving properties can yield the fatigue and fracture of the bone tissue surrounding implant. The final aim of this work is to compare the mechanical states of healthy and implanted human femur in order to design the appropriate fatigue testing device and bone sample geometry. Numerous studies of the stress distribution in the femur during human walking can be found in literature (Polgar et al. 2003; Abdellatif and Benaoumeur 2016). However in our knowledge, the risk of cortical bone fatigue in vicinity of the implant was neither considered. In this paper, the first results concerning the stress distribution in healthy and implanted femurs are presented. The bone geometry, loading, boundary conditions and mechanical properties have been deduced from OpenSIM simulations of gait cycle and µCT scanning of the human femur.

The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: Gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle.

Background: Gait impairment in patients with Alzheimer’s disease (AD) and its relationship with cognitive function has been described, but reports of gait analysis in AD in daily living are limited. Objective: To investigate whether gait pattern of patients with AD in daily living is associated with cognitive function. Methods: Gait was recorded in 24 patients with AD and 9 healthy controls (HC) for 24 hours by using a portable gait rhythmogram. Mean gait cycle and gait acceleration were compared between the AD and HC groups. For the AD group, these gait metrics were assessed for correlations with cognitive function, as determined by the Mini Mental State Examination and Wechsler Memory Scale-Revised (WMS-R). Results: Although both gait parameters were not different between the patients with AD and HC, gait cycle in patients with AD was positively correlated with attention/concentration scores on the WMS-R (r = 0.578), and not with memory function. Patients with AD with attention scores as high as HC displayed a longer gait cycle than both HC (p = 0.048) and patients with AD with lower attention scores (p = 0.011). The patients with AD with lower attention scores showed a similar gait cycle with HC (p = 0.994). Conclusion: Patients with AD with impaired attentional function walk with faster gait cycle comparable to HC in daily living walking, which was unexpected based on previous gait analysis in clinical settings. This result probably reflects diminished consciousness to either the environment or instability of gait in the patients with AD with impaired attention.

This work aims to design a prosthesis of the pelvic bone considering the effect of muscle forces using principles of structural topology optimization. The running gait cycle is chosen since it has high active muscle forces and is divided into seven phases. The pelvic prosthesis is designed for individual seven phases with non-design domains. Material of prosthesis is the titanium alloy (Ti $$_6$$ Al $$_4$$ V) since it has good biocompatibility and high strength. The shape similarity between the designed prosthesis and the pelvic bone is also compared which has not been done before. The design space is explored by using combination of phases with an aim to find the possible best loading sequence that produces the optimal design with the highest shape similarity. Results show that the induced peak stresses in the optimal designs are low, within endurance strength of the alloy. The maximum stress, maximum displacement and compliance values are lower in optimal designs compared with pelvic bone. The optimally designed pelvic prosthesis using combination of phases has high shape similarity. A good shape similarity of the prosthesis may result in less complications in terms of adjustment of the body to the prosthesis.

Gait cycle partitioning is very important prior to human gait analysis such as gait modeling, gait recognition, gait feature analysis, etc. In this paper, we propose a new automatic gait cycle partitioning method based on two kinds of simple gait representation. Then, the propose method is applied to find keyframe corresponding the rest position for gait-based human identification system. To demonstrate the validity of the proposed method, the CASIA gait dataset A and the SOTON gait database are used to evaluate the recognition performance of the gait recognition system identifying subjects using the decision level fusion based on majority voting.

Examining a human movement can provide a wealth of information about a patient’s medical condition. The examination process can be used to diagnose abnormal changes (lesions), ability development and monitor the rehabilitation process of people with reduced mobility. There are several approaches to monitor people, among other things with sensors and various imaging and processing devices. In this case a Kinect V2 sensor and a self-developed LabView based application was used, to examine the movement of the lower limbs. The ideal gait pattern was recorded in the RoboGait training machine and the measured data was used to identify the phases of the human gait. During the evaluation, the position of the skeleton model, the associated body joints and angles can be calculated. The pre-recorded ideal and natural gait cycle can be compared.With the self-developed method the pre-recorded ideal and natural gait cycle can be compared and processed for further evaluation. The evaluated measurement data confirm that a reliable and mobile solution for gait analysis has been created.

Lower limb assistive exoskeletons for rehabilitation is a growing field of study. They allow people with spinal cord injuries and strokes to stand upright and walk. Exoskeleton controls research typically focuses on trajectory generation, motion control over the trajectories, and balance and stability. This work focuses on the generalization of people’s joint trajectories climbing a single stair, for an exoskeleton to be developed for people of all leg lengths and be used on variable stair heights. The motion of subjects of different heights, ages, and genders climbing a single stair was collected using motion capture. By using Gaussian mixed regression and Gaussian mixed models, these trajectories were combined to build a model that generalizes individuals with different leg lengths, genders, ages and stair heights. The variability of the subject’s leg length is accounted for by tracking a marker on the toe of the subject and using inverse kinematics to calculate the joint angles. The analysis in this paper shows that joint angles can be generalized to climb various stair heights. In addition, an open-source database of motion capture climbing data and an open-source framework for generating joint trajectories has been made available for public use.

In this paper, an approach based on a liquid state machine (LSM) to compute the movement profiles to achieve a gait pattern subject to different variations in its trajectory is presented. At the same time, the position of the zero moment point (ZMP) to determine the stability of the six degrees-of-freedom (6DOF) bipedal robot in the sagittal plane during the gait cycle is calculated. The system is constructed as a supervised machine learning model. The time series of the oscillating foot trajectory obtained by direct kinematics with a multilayer perceptron neural network (MLP), to strengthen the kinematic model, is considered as input values for training. The target movement profiles are acquired of a human gait cycle analysis in three different scenarios: normal gait, climbing stairs, and descending stairs. In training, this model also gets the trajectories of the ZMP position during the gait cycle, as target time series. The LSM formed by spiking neurons, considered as third-generation neural networks, is compared in the accuracy of prediction, by the dynamic time warping (DTW) technique and correlation analysis, against the human gait analysis database. With this neuronal system, the joint positions to generate a trajectory of the oscillating foot and the ZMP position of the bipedal in the sagittal plane in different scenarios are obtained, proving the robustness of the LSM.

Background This study evaluated the influence of meniscal allograft transplantation (MAT) on knee joint mechanics during normal walking using finite element (FE) analysis and biomechanical data. Methods The study included 20 patients in a transpatellar group and 25 patients in a parapatellar group. Patients underwent magnetic resonance imaging (MRI) evaluation after lateral MAT as a baseline input for three-dimensional (3D) and FE analyses. Three different models were compared for lateral MAT: intact, transpatellar approach, and parapatellar approach. Analysis was performed using high kinematic displacement and rotation inputs based on the kinematics of natural knees. ISO standards were used for axial load and flexion. Maximum contact stress on the grafted menisci and maximum shear stress on the articular surface of the knee joint were evaluated with FE analysis. Results Relatively high maximum contact stresses and maximum shear stresses were predicted in the medial meniscus and cartilage of the knee joint during the loading response for all three knee joint models. Maximum contact stress and maximum shear stress in the meniscus and cartilage increased on the lateral side after lateral MAT, especially during the first 20% of the stance phase of the gait cycle. The transpatellar approach was most similar to the intact knee model in terms of contact stresses of the lateral grafted and medial meniscus, as well as maximum shear stresses during the gait cycle. In addition, the transpatellar model had lower maximum contact stress on the menisci than did the parapatellar model, and it also had lower maximum shear stress on the tibial cartilage. Conclusions Therefore, the transpatellar approach may reduce the overall risk of degenerative osteoarthritis (OA) after lateral MAT.

The biometric method which is involved with defining pattern of movement of human limbs is called Gait. Though gait has a fixed rhythmic pattern, in case of people who are affected with neurological disease such as- Parkinson's disease (PD) the gait pattern gets distorted from normal gait pattern. In this paper, temporal variables of gait cycles are analyzed to determine the deficit in the gait cycle in PD patients. Gait cycle of 10 PD patients and 10 Controlled subjects are examined in this paper. A comparative analysis was done in respect of temporal variables such as- Single Limb Support Time and Double Limb Support Time. From the examination and investigation, it is apparent that the temporal factors of the gait cycle of PD patients are sufficiently changed in regard to the controlled subjects, all the more explicitly PD patients invest more energy in the two limbs than a single limb of a gait cycle which is absolutely in switch of the controlled subject.

Gait energy image (GEI) is considered as an effective gait representation for gait-based human identification. In gait recognition, normally, GEI is computed from one full gait cycle. However in many circumstances, such a full gait cycle might not be available due to occlusion. Thus, the GEI is not complete, giving a rise to degrading gait identification rate. In this paper, we address this issue by proposing a novel method to reconstruct a complete GEI from a few frames of gait cycle. To do so, we propose a deep learning-based approach to transform incomplete GEI to the corresponding complete GEI obtained from a full gait cycle. More precisely, this transformation is done gradually by training several fully convolutional networks independently and then combining these as a uniform model. Experimental results on a large public gait dataset, namely OULP demonstrate the validity of the proposed method for gait identification when dealing with very incomplete gait cycles.

The prosthetic knee is one of the most important parts used in manufacturing of the above knee prostheses limb and it is considered the most important component. Accordingly, it became necessary to conduct a study and evaluate for different types a prosthetic knee. In present work, the mechanism of four bar 3R20 prosthetic knee were evaluated and studied. Virtual model of four bar 3R20 prosthetic knee joint was built using Solid Work 2018 program. The fifty parts of four bar polycentric knee were designed separately and then assembled together. The kinematic analysis of four bar polycentric knee was performed using geometric analysis and Grashof law for double rocket. The results of instant center of rotation (ICR) from analytical solution was estimated using MATLAB program. The kinetic model include data of two components of forces and joint moments which used for transfemoral amputee with (age, height, weight, and stump length) of (28 years, 178 cm, 78 kg, and 29 cm) respectively. The numerical analysis of four bar prosthetic knee involved select the element type in ANSYS Workbench 19.2 program. Quadrilateral and trihedral elements were used in the finite element analysis to generate the mesh. The static analysis was investigated during different segmentation of gait cycle. The results of equivalent (von Mises) stress distribution calculated during stance phase for different loading conditions showed that the maximum stress was printed at the loading response phase with 226.93 Mpa at the root region between prosthetic knee and socket when orientation angles θ 2 = 85.5° and θ 4 = 105.4° and ICR coordinates are −0.560 mm and 77.388 mm for X,Y respectively.

In this paper, we develop an algorithm to automatically validate and segment a gait cycle in real time into three gait events, namely midstance, toe-off, and heel-strike, using inertial sensors. We first use the physical models of sensor data obtained from a foot-mounted inertial system to differentiate stationary and moving segments of the sensor data. Next, we develop an optimization routine called sparsity-assisted wavelet denoising (SAWD), which simultaneously combines linear time invariant filters, orthogonal multiresolution representations such as wavelets, and sparsity-based methods, to generate a sparse template of the moving segments of the gyroscope measurements in the sagittal plane for valid gait cycles. Thereafter, to validate any moving segment as a gait cycle, we compute the root-mean-square error between the generated sparse template and the sparse representation of the moving segment of the gyroscope data in the sagittal plane obtained using SAWD. Finally, we find the local minima for the stationary and moving segments of a valid gait cycle to detect the gait events. We compare our proposed method with existing methods, for a fixed threshold, using real data obtained from three groups, namely controls, participants with Parkinson disease, and geriatric participants. Our proposed method demonstrates an average F1 score of 87.78% across all groups for a fixed sampling rate, and an average F1 score of 92.44% across all Parkinson disease participants for a variable sampling rate.

Main To analyze spatiotemporal gait parameters and the body center of mass (CoM) energy transduction at self-selected speed walking in a group of older patients with stroke. Methods A cross-sectional study, fifteen subjects with 4.06 years post stroke hemiparesis (eleven men and four women) and fifteen healthy subjects (four men and eleven women) participate in this study. Pendulum-like determining variables; Recovery (R) and Congruity percentage (%Cong) were analyzed in addition to immediate pendular re-conversion (Rint) during the phases in which the gait cycle is usually divided in clinical evaluations. Results Healthy subjects walked faster that stroke group (p = 0.001). %Cong was significantly higher in post-stroke respect to healthy subjects (p = 0.05). Rint showed significant differences between the groups for all phases (p = 0.05). The relation between speed and R was confirmed, for healthy (r = 0.67, p = 0.006) and post-stroke subjects (r = 0.851, p = 0.001), %Cong y Rint (r = −0.79, p = 0.001), (r = −0.93, p = 0.001) and periods of double support (r = −0.76, p = 0.001), (r = 0.69, p = 0.004) respectively. Conclusion Alteration of pendular mechanism in subjects post-stroke is associated mainly with energy transduction; mechanical energy recovered during double support phases in healthy and post-stroke subjects follows a different trend, in post-stroke subjects, a longer duration of the double support is associated with less energy loss.

Although researches had analyzed gait using small sensors, they analyzed only normal gaits. Thus, a research that can overcome the spatial limitations of the existing motion analyses and diagnose abnormal gaits for medical treatment is needed. Accordingly, this research developed the portable gait detection system that can detect gait using a gyroscope, and evaluated the accuracy of the system. The results showed an average recognition error rate of 1.7% for the normal and abnormal gaits, and confirmed that the gait cycle was detected with a high degree of accuracy. Using these characteristics, we could distinguish or diagnose, and treat, an abnormal gait.

Cement is the weakest link in the composition of total hip prosthesis in terms of mechanical properties. The knowledge of the intensity and distribution of stresses on the cement attaching the implant to the bone is of great importance for understanding the condition of the prosthesis and its failure. In this study, the finite element method is used to analyze the magnitude and the equivalent Von Mises stress distribution induced in different components of the total hip prosthesis (THP) as well as the identification of the damage induced in the cement and between two cavities located in the polymethyl methacrylate (PMMA). The crack propagation is determined and localized using the extended element method (XFEM). The results show that the fracture stress of the cement in its proximal part is very important. These stresses increase considerably with the interaction of the cavities in this binder, causing damage to the cement and the loosening of the prosthesis.

The helical axis of motion (HAM), which describes the simultaneous multiplanar translations and rotations that occur within a joint, has been proposed as a single measure to characterize dynamic joint function. The objective of this study was to determine the tibiofemoral HAM during 5 discrete phases of gait. Thirty-nine knees from 20 healthy adults were imaged using high-speed biplane radiography during treadmill walking. The primary outcome measures were the intersection of the HAM with the sagittal plane of the femur, and the direction of the HAM. The intersection point translated an average of 12.7 ± 5.5% of femur condyle depth in the anterior-posterior direction and 28.6 ± 13.3% of femur condyle height in the proximal-distal direction during gait. The anterior/posterior and proximal/distal components of the HAM vector were greater during stance (5.6°±3.8° and 11.1°±5.0°, respectively) than during swing (2.0°±1.1° and 6.4°±3.8°, respectively) (p0.001) reflecting greater coupled rotations during stance. No significant side-to-side differences in intersection point location or HAM orientation were found during any of the 5 phases of gait (max difference 4.1 ± 3.4% of femur condyle depth and 13.1 ± 16.7% of femur condyle height; 12.7°±12.3° proximal/distal and 4.2°±4.5° anterior/posterior direction). Loading significantly affected HAM location and orientation (p0.001). Knowledge of healthy knee HAM and typical side-to-side differences during gait can serve as a baseline for evaluating knee motion after clinical interventions.

Gait anomalies give rise to several clinical problems in stroke survivors, which restrict their functional mobility and have a negative impact on their quality of life. Robotics-aided gait training post-stroke has proven capable of improving patients' functional walking, but so far it has not performed significantly better than conventional therapy. We hypothesize that an exoskeleton-based training program, aimed at correcting deficits in the leg joints' movement, could produce greater improvements in gait function than conventional therapy. As a first step towards testing this hypothesis, we designed an exoskeleton control to correct a typical kinematic deficit post-stroke, namely, reduced knee flexion on the paretic side during swing. The proposed control attempts to minimize this deficit by delivering assistive torque synchronized with the continuous phase of the patient's gait. Nine healthy male participants walked in a unilateral cable-driven exoskeleton while subject to an artificial knee flexion impairment produced by a custom-made knee brace. The experiments employed a treadmill featuring a variable-velocity control to allow self-selected gait speed. The artificial impairment by itself caused a significant reduction in peak flexion angle (p = 0.000129). Exoskeleton assistance compensated most of the knee flexion deficit, yielding no significant difference with unrestricted flexion (p = 0.3393). No significant changes in self-selected gait speed or stride frequency were detected. The proposed control can be expanded to correct motion deficits in other joints at different stages of the gait cycle.

One commonly used criterion in evaluating a patients' response to knee surgery or rehabilitation is bilateral symmetry. However, the natural symmetry in uninjured healthy adult knee kinematics remains relatively unknown, making it challenging to determine if clinical treatment has adequately restored bilateral symmetry. The primary purpose of this study was to determine the typical side-to-side differences in 6 degree of freedom (DOF) knee kinematics over the entire gait cycle in healthy adults using biplane radiography. Six DOF tibiofemoral kinematics were measured during treadmill walking in 19 participants using a validated volumetric model-based tracking process that matched subject-specific bone models to biplane radiographs collected at 100 images/s. Average absolute side-to-side differences in knee kinematics at foot strike were 1.3 mm or less in translation and 3.8° or less in rotation. Peak side-to-side differences in knee kinematics occurred during the swing phase and were up to 2.2 mm in translation and 7.1° in rotation. Dominant versus non-dominant leg differences were 0.8 mm and 2.8° or less at foot strike and reached maximum values of 0.8 mm and 7.2° over the full gait cycle. Statement of Clinical Significance: This study quantifies the inherent asymmetry of knee kinematics in healthy individuals over the entire gait cycle. The values of asymmetry presented here may serve as a guide for evaluating functional outcomes and restoration of so-called "normal" kinematics after injury and clinical intervention. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

In gait recognition, which has been recently regarded as a biometric recognition tool, proposed approaches assume that an individual is observed for at least one gait cycle. However, in reality, there might be available only a few frames of full gait cycle of a subject due to occlusion. Therefore, gait recognition systems would fail in these scenarios. In this paper, we propose a method to tackle this problem by proposing a gait recognition algorithm from an incomplete gait cycle information. We achieve this by 1) creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and 2) reconstructing the complete GEI from incomplete GEI using a deep auto-encoder. The experimental results on a public gait dataset demonstrate the validity of the proposed method.

Gait analysis is the systematic study of human walking. The analysis of gait signals from the lower trunk, acquired through accelerometers, begins with the proper identification of gait cycles. The goal of this work is to supplement gait-event based segmentation methods, tested for unimpaired and impaired populations, so that their need to calibrate or rely on pre-defined thresholds is overcome, and to implement strategies that reduce step-detection errors. A new system for the automatic extraction and analysis of gait cycles from acceleration signals of the lower trunk, combining knowledge from previous strategies with a dynamic time warping function, is presented. Performance was tested on gait signals from public databases. Sensitivities in step detection above 99.95% were achieved, with a positive predictive value of 100.00%. Step-correction strategies reduced the number of incorrect detections from 57 to 3 of 7056 steps. Bland-Altman plots and equivalence tests performed on cycle times by the proposed method and selected references showed good agreement, with mean differences below 0.003 s, and percent errors of 2%. This method may give place to a research tool for the automatic analysis of signals from subjects in a variety of cases.

It is well known that terrain recognition and gait cycle prediction are important for powered exoskeleton. However, only a few works have focused on the concerns of complexity of the control system caused by using redundant sensors. In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent. Based on information acquired from these two IMU sensors, two methods are proposed to achieve terrain recognition. One method uses the angle of the hip joint when the two legs intersect as the threshold of terrain recognition. It can identify the terrain (level-ground walking, ramp ascent, ramp descent) during stable walking, but it cannot recognize the transitional terrain (from level-ground walking to ramp ascent, from ramp ascent to ramp descent, and so on) and its robustness is limited. The other method selects the angle and angular velocity of the hip joints as the eigenvector, and uses SVM for terrain recognition. The accuracy of terrain recognition is improved from 69.7% to 100% after introducing the Gaussian kernel function instead of Linear kernel function. For gait cycle prediction, Wiener one step prediction is applied in predicting the GC. Compared to actual GC, the error from predicted GC based on mean prediction is more than 8.0%, while the error from Wiener on step prediction is less than 4.35%.

In this paper, a robust algorithm for gait cycle segmentation is proposed based on a peak detection approach. The proposed algorithm is less influenced by noise and outliers and is capable of segmenting gait cycles from different types of gait signals recorded using different sensor systems. The presented algorithm has enhanced ability to segment gait cycles by eliminating the false peaks and interpolating the missing peaks. The variance of segmented cycles' lengths is computed as a criterion for evaluating the performance of segmentation. The proposed algorithm is tested on gait signals of patients diagnosed with Parkinson's disease collected from three databases. The segmentation results on three types of gait signals demonstrate the capability of the proposed algorithm to segment gait cycles accurately, and have achieved better performance than the original peak detection methods.

Biomechanical stability of the human body during a gait cycle is an important parameter of biomedical assessments in the rehabilitation / recovery of people who have suffered an injury or to improve athletic performance. During the recovery actions or increasing in performance activities, can result a number of audio-visual stimuli, which may change accordingly, positively or negatively, specific actions on dedicated procedures. The research presented in this paper aims at assessing the impact of a set of auditory stimuli with negative emotional components and positive respectively, the behavior of a sample of subjects during a gait cycle. The recordings are made with afoot scansystem type "RSScan" which allows measurements of contact forces between the leg and the ground, on the entire plantar surface, measurements of the parameters during gait cycle and not least, comparative evaluation of forces, trajectories and speed changes of plantar surfaces between the left/right foot. During a gait cycle, the human subject is audio "stimulated", to track the effect of these stimulation on the biomechanical stability, on behavioral reaction of the entire human body, especially on the plantar surface which provide bipodal support. Results of this research are a starting point for specific analyzes in various biomedical or human performance situations.

The effect of loading and boundary conditions on patellar mechanics is significant due to the complications arising in patella femoral joints during total knee replacements. To understand the patellar mechanics with respect to loading and motion, a computational model representing the patella femoral joint was developed and validated against experimental results. The computational model was created in IDEAS NX and simulated in MSC ADAMS/VIEW software. The results obtained in the form of internal external rotations and anterior posterior displacements for a new and experimentally simulated specimen for patella femoral joint under standard gait condition were compared with experimental measurements performed on the Leeds ProSim knee simulator. A good overall agreement between the computational prediction and the experimental data was obtained for patella femoral kinematics. Good agreement between the model and the past studies was observed when the ligament load was removed and the medial lateral displacement was constrained. The model is sensitive to ±5 % change in kinematics, frictional, force and stiffness coefficients and insensitive to time step.

Gait recognition is a non-invasive biometric technology that can be used to identify humans in surveillance systems. It is based on the style or manner in which a person walk and can be realized with minimal amount of individual cooperation for its acquisition. However, it may causes many challenges in the form of varying viewpoints, carrying conditions and clothing variations. To tackle such limitations, we present a view-invariant gait recognition network that divide the gait cycle into five segments (GCS). The intra gait-cycle-segment (GCS) convolutional spatio-temporal relationships has been obtained by employing a 3D-CNN via. transfer learning mechanism. Later, a stacked LSTM has been trained over spatio-temporal features to learn the long and short relationship between inter gait-cycle-segment. The first step in our work is data pre-processing, in which we create silhouette stereo map (SSM) from the binary silhouettes of the gait video frame and sampled each video into a fixed 80 frames. These 80 frames SSM have been divided into 5 gait-cycle-segments (GCS) of 16 frames each. From each of these GCS, we extract spatio-temporal features using a pre-trained 3-D CNN. These features have been concatenated temporally, and an LSTM cell is used to learn the long-term dependencies between each GCS. Finally, the required class scores are computed by averaging (to handle noise) the output generated by LSTM. The network is trained in an end-to-end fashion using triplet loss function so as to learn the gait metric well using only the hard triplets. All the experiments are carried out on publicly available CASIA-B and OU-ISIR gait dataset. From the experimental results, it has been indicated that the proposed network performs better than the current state-of-the-art gait recognition systems.

The utility of wearable sensors for continuous gait monitoring has grown substantially, enabling novel applications on mobility assessment in healthcare. Existing approaches for gait cycle detection rely on predefined or experimentally tuned platform parameters and are often platform specific, parameter sensitive, and unreliable in noisy environments with constrained generalizability. To address these challenges, we introduce CyclePro, 1 novel framework for reliable and platform-independent gait cycle detection. CyclePro offers unique features: 1) it leverages physical properties of human gait to learn model parameters; 2) captured signals are transformed into signal magnitude and processed through a normalized cross-correlation module to compensate for noise and search for repetitive patterns without predefined parameters; and 3) an optimal peak detection algorithm is developed to accurately find strides within the motion sensor data. To demonstrate the efficiency of CyclePro, three experiments are conducted: a clinical study including a visually impaired group of patients with glaucoma and a control group of healthy participants; a clinical study involving children with Rett syndrome; and an experiment involving healthy participants. The performance of CyclePro is assessed under varying platform settings and demonstrates to maintain over 93% accuracy under noisy signal, varying bit resolutions, and changes in sampling frequency. This translates into a recall of 95.3% and a precision of 93.4%, on average. Moreover, CyclePro can detect strides and estimate cadence using data from different sensors, with accuracy higher than 95%, and it is robust to random sensor orientations with a recall of 91.5% and a precision of 99.2%, on average. 1 Software code and sample datasets for CyclePro are publicly available at https://github.com/y-max/CycleProa