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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.

OBJECTIVE: The aim of this study was to investigate the differences in temporal and spatial parameters in patients with and without knee osteoarthritis (OA) during different phases of gait cycle. MATERIALS AND METHODS: In this study, two groups were taken, OA group and healthy group. OA group consisted of 25 females suffering from knee OA (mean age 60.04 ± 5.74 years; mean height 150.44 ± 5.88 cm; mean weight 63.76 ± 7.28 kg), and healthy group consisted of 25 females (mean age 50.56 ± 7.03 years; mean height 155.16 ± 4.08 cm; mean weight 61.77 ± 12.03 kg). Both OA and healthy groups were assessed for temporal and spatial gait parameters by Zebris FDM Treadmill analysis system. RESULTS: Mann–Whitney U-test was used to compare the results of the study groups. Significant differences were found in the following variables – left stance phase (%), right stance phase (%), right load response (%), right mid stance (%), right preswing (%), left swing phase (%), right swing phase(%), double stance (%), right and left foot rotation (degrees), and cadence (steps/min). CONCLUSION: The spatial and temporal differences between patients with knee OA and healthy elderly females suggest that patients with knee OA adopt different strategies to reduce impact on knees. Gait dysfunction can be evaluated by assessing the various gait parameters, and treatment progression can be done in patients with knee OA. The assessment of gait by Zebris FDM Treadmill analysis system is a simple way which is done objectively.

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.

Introduction Reportedly, wearable robots, such as the hybrid assistive limb (HAL), are effective in the functional recovery of various locomotor disabilities, including disrupted walking, restricted range of motion, and muscle weakness. However, the effect of walking exercises with a HAL on the kinematic and kinetic variables of lower limb joint function is not yet fully understood. Therefore, the purpose of this study was to elucidate the effect of HAL on the kinematic and kinetic variables of lower limb function in patients 5 weeks after total knee arthroplasty (TKA). Materials and Methods Nine patients (ten knees) in the HAL training group and nine patients (nine knees) in the control group underwent TKA. HAL training was initiated 1–5 weeks after TKA, and general rehabilitation was performed in the control group. Gait analysis was performed on each patient using a motion analysis system at 5 weeks after TKA. We compared the effects of the joint angles of the walking cycle between groups, and investigated the effect of the walking cycle’s joint angles on step length. Results In the HAL group, the odds ratio of hip extension was as large as 1.741, while that of knee swing was as large as 1.501. These 2 variables were significant between the 2 groups. Knee swing and varus significantly affected step length. Conclusions Our results suggest that training by wearing HAL after TKA increased the mobility of the knee and hip joints during early postoperative walking, and that walking ability was improved by increasing the step length.

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

(1) Background: The femoroacetabular impingement (FAI) type cam leads to a conflict between the acetabular rim and a bony thickening of the femoral neck junction. While maximal excursions in flexion, adduction and internal rotation provoke pain, the aim of this study was to analyze if a cam morphology shows an impact on gait pattern. (2) Methods: Fifty-five patients with end-stage hip osteoarthritis performed gait analysis before hip replacement as well as three, six and 12 months postoperatively. Thirty-three (60%) of them presented an FAI type cam. An ANOVA was used to compare the hip angles in sagittal, frontal and transversal planes between patients with a FAI type cam (group “+cam”) and without (group “−cam”). (3) Results: Before surgery the patients of the +cam-group showed a tendency towards a reduced flexion and internal rotation at the heel strike (p &gt, 0.05). Over time, the differences were adjusted by total hip arthroplasty. (4) Conclusions: We did not find any differences in the gait analysis of patients with a FAI type cam compared to patients without.

Although the foot is involved in load-bearing and shock absorption, foot pressure (FP), ground contact area (CA), and gait cycle (GC) in flatfeet (FF) have not been examined in detail. We aimed to analyze the influence of FF on FP, CA, and GC. We included 20 and 21 women with FF and normal feet (NF), respectively. A Footscan plantar pressure plate (RsScan International, Belgium) was used to analyze FP, CA, and GC. FP was applied to the unit area of 10 compartments. GC analysis was performed using phase-time measurements by dividing the GC into four phases. In the analysis, FP and CA were compared between the FF and NF groups. A comparison of GC was similarly performed between the two groups. The data provided in this article will be useful when designing studies on the effect of foot shape on FP, CA, and GC during gait.

Background. There is evidence for violation or a complete change in the arm swing cycle during walking in a number of pathologic conditions. Aim. We assess the functional state of the shoulder and elbow joints in normal conditions and with joint hypermobility syndrome (JHS) using the kinematic instrumental method of analyzing gait. Material and methods. We studied 27 adolescent girls 1215 years old with JHS and healthy subjects. A Vicon motion capture analysis system (Vicon, Oxford, Great Britain) was used to record biomechanical parameters. Results. A decrease in limb movement amplitudes was noted in the shoulder joint around the frontal and sagittal axes in patients with JHS compared to the norm. During the arm swing cycle in the normal state, the shoulder is in a state of internal rotation, whereas in the girls with JHS, the shoulder is in a state of external rotation for most of the arm swing cycle. The elbow joint in the JHS subjects showed a significant increase in flexion angle of the forearm in the swing phase of 41.5 0.90 and a decrease in this angle in the stance phase. The JHS group also showed a decrease in power of the muscles acting on the shoulder joint. Conclusions. A common sign of changes in the range of motion of the links of the upper limb in the shoulder and elbow joints in subjects with JHS was decreased amplitude of their flexion and decreased power of the joints. In the adolescents with JHS in the shoulder joint, a significant decrease in the internal rotation angles and reduction of the limb was found.

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.

Purpose: Gait and balance are functionally related to motor performance. This study aimed to determine biomechanical features of gait in people with intellectual disabilities by reviewing articles published from 1992 to 2017. Methods: In this systematic review, all quantitative studies related to gait and balance of people with intellectual disabilities were searched from Persian databases (Magiran, Irandoc, IranMedex, MedLib, SID, and Google Scholar) and all full text articles with keywords of “intellectual disability”, “balance”, “postural control”, and “gait” were extracted from English databases (Science Direct, PubMed, EMBASE, Cochrane Review, TRIP, Pedro, CINAHL, ProQuest). Then, after complete review of articles, the English and Persian published papers on biomechanical parameters of gait cycle and balance in intellectual disabilities and related factors were extracted. Also, a “Data Extraction Form” which was developed based on research objectives, was used for data collection. Out of 56 related papers and research projects, 20 articles were excluded due to lack of meeting the inclusion criteria. Finally, 36 papers and research projects were included in this study. Results: Of the 36 remained studies, the results showed that intellectual disabilities are usually accompanied with gait disorders, especially in step length, stride length, shorter step width, and lower velocity. It also has kinetic and kinematic disorders, including reduction of the ankle production power in the push-off phase, increased hip and knee flexion at stance phase, knee limitation and plantar ankle flexion at the heel contact, and joint stiffness during the gait cycle. The results showed that individuals with intellectual disabilities had significantly lower scores in the balance tests compared to their healthy peers. Conclusion: Based on the results, the prevalence of gait disorders and balance in people with intellectual disability is high. Preventive measures should be taken in order to control gait disorders and balance. Therefore, it is suggested that occupational therapists, physiotherapists, sports experts, nurses, and other stakeholders try to find therapeutic strategies for preventing, control, and treatment of gait disorders. Also, because of the lack of intervention studies, future research must move from description to intervention studies in order to provide a guideline in clinical settings.

Approximately 80% of patients are satisfied with the outcome of arthrodesis of the hallux metatarsophalangeal (MTP) joint. The hypothesis of this study was that MTP arthrodesis does not influence the walking cycle. The aim of this study was to evaluate the effect of MTP arthrodesis on gait cycle and to measure the functional outcome of MTP arthrodesis after a minimum of two year follow-up. This was an observational prospective cohort study performed at a single centre that included 26 patients (32 ft) who underwent unilateral or bilateral MTP arthrodesis during the period 2004–2014. An X-ray assessment based on the American Orthopaedic Foot and Ankle Society score was performed pre-operatively and at the last follow-up (average follow-up duration 8.3 years). The walking cycle was analysed at the final follow-up using a GAITRite® treadmill test. Twenty unilateral and six bilateral MTP arthrodeses were included in this study. The average age of the patients was 70 years. No significant difference was found in the walking cycle between the operated and non-operated sides for unilaterally MTP arthrodesis. However, we observed a non-significant increase in the percentage of contact time after MTP arthrodesis (65% on the operated side vs. 63% on the non-operated side). We also observed a significant decrease in the average walking rate, and a decrease in walking speed, in cases of bilateral MTP arthrodesis compared with normal walking cycle data from a laboratory study. MTP joint arthrodesis does not appear to have any effect on the walking cycle, but reduced patient pain and increased the walking distance. The patients reported satisfaction with the surgical outcomes and would recommend it to a relative with the same pathology.

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.

Programming robots for performing different activities requires calculating sequences of values of their joints by taking into account many factors, such as stability and efficiency, at the same time. Particularly for walking, state of the art techniques to approximate these sequences are based on reinforcement learning (RL). In this work we propose a multi-level system, where the same RL method is used first to learn the configuration of robot joints (poses) that allow it to stand with stability, and then in the second level, we find the sequence of poses that let it reach the furthest distance in the shortest time, while avoiding falling down and keeping a straight path. In order to evaluate this, we focus on measuring the time it takes for the robot to travel a certain distance. To our knowledge, this is the first work focusing both on speed and precision of the trajectory at the same time. We implement our model in a simulated environment using q-learning. We compare with the built-in walking modes of an NAO robot by improving normal-speed and enhancing robustness in fast-speed. The proposed model can be extended to other tasks and is independent of a particular robot model.

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.

Because dementia is difficult to treat, the best way is to detect the prodromal stage, this can identify those at high risk of dementia and help to delay its onset. It is a well-known fact that gait has a high correlation with cognitive function. Considering that dementia starts with cognitive decline, investigating the association between cognitive decline and gait may contribute to the detection of elderly individuals at high risk of dementia and even the prevention of dementia. The purpose of the present study was to investigate the gait cycle and biomechanics of elderly people with and without cognitive decline. A three-dimensional motion analysis system was used, and older adults over 65 participated in this study. K-MoCA was used to assess cognitive function and, according to the results of a cognition function assessment, they were classified into two groups. Spatiotemporal variables, subdivisions of the gait cycle, joint angle, joint moment, joint power, and support moment were investigated. Significant differences between both groups appeared in the subdivisions of the gait cycle, and parameters of gait biomechanics were established. These results provide insight into the mechanism dictating the gait of elderly individuals with cognitive decline.