Real-time Gait Analysis Research Articles

Synchronization of Neurophysiological and Biomechanical Data in a Real-Time Virtual Gait Analysis System (GRAIL): A Proof-of-Principle Study.

The investigation of gait and its neuronal correlates under more ecologically valid conditions as well as real-time feedback visualization is becoming increasingly important in neuro-motor rehabilitation research. The Gait Real-time Analysis Interactive Lab (GRAIL) offers advanced opportunities for gait and gait-related research by creating more naturalistic yet controlled environments through immersive virtual reality. Investigating the neuronal aspects of gait requires parallel recording of brain activity, such as through mobile electroencephalography (EEG) and/or mobile functional near-infrared spectroscopy (fNIRS), which must be synchronized with the kinetic and /or kinematic data recorded while walking. This proof-of-concept study outlines the required setup by use of the lab streaming layer (LSL) ecosystem for real-time, simultaneous data collection of two independently operating multi-channel EEG and fNIRS measurement devices and gait kinetics. In this context, a customized approach using a photodiode to synchronize the systems is described. This study demonstrates the achievable temporal accuracy of synchronous data acquisition of neurophysiological and kinematic and kinetic data collection in the GRAIL. By using event-related cerebral hemodynamic activity and visually evoked potentials during a start-to-go task and a checkerboard test, we were able to confirm that our measurement system can replicate known physiological phenomena with latencies in the millisecond range and relate neurophysiological and kinetic data to each other with sufficient accuracy.

A Comparison of the Immediate Effects of Verbal and Virtual Reality Feedback on Gait in Children with Cerebral Palsy.

Different types of feedback are used during gait training in children with cerebral palsy (CP), including verbal (VB) and virtual reality (VR) feedback. Previous studies on VR feedback showed positive effects on the targeted gait parameter. However, both positive and negative side effects on other parameters were seen as well. The literature on the effect of VB feedback is lacking and, to our knowledge, both feedback methods have not yet been compared. In this monocentric study with a single-session intervention protocol, children with CP completed a training session on the Gait Real-Time Analysis Interactive Lab (GRAIL) and received both VB and VR feedback on hip extension, in randomized order. Outcome parameters were continuous gait curves of sagittal kinematics and hip kinetics, specific features of hip angle and moment, sagittal gait variable scores and gait profile scores. Improvement of the targeted gait parameter was seen both after VB and VR feedback, with a small advantage for VR over VB feedback. Furthermore, positive side effects on knee and ankle sagittal kinematics were seen. However, the overall gait profile score did not improve, most likely due to negative compensatory strategies. In conclusion, children with CP can adapt gait in response to both VB and VR feedback, with VR feedback producing a slightly better effect. Due to secondary effects on parameters other than the targeted parameter, the overall gait did not improve.

Efficacy of Walking Adaptability Training on Walking Capacity in Ambulatory People With Motor Incomplete Spinal Cord Injury: A Multicenter Pragmatic Randomized Controlled Trial

Background and Objective Balance and walking capacity are often impaired in people with motor incomplete spinal cord injury (iSCI), frequently resulting in reduced functional ambulation and participation. This study aimed to assess the efficacy of walking adaptability training compared to similarly dosed conventional locomotor and strength training for improving walking capacity, functional ambulation, balance confidence, and participation in ambulatory people with iSCI. Methods We conducted a 2-center, parallel-group, pragmatic randomized controlled trial. Forty-one people with iSCI were randomized to 6 weeks of (i) walking adaptability training (11 hours of Gait Real-time Analysis Interactive Lab (GRAIL) training—a treadmill in a virtual reality environment) or (ii) conventional locomotor and strength training (11 hours of treadmill training and lower-body strength exercises). The primary measure of walking capacity was maximal walking speed, measured with an overground 2-minute walk test. Secondary outcome measures included the Spinal Cord Injury Functional Ambulation Profile (SCI-FAP), the Activities-specific Balance Confidence (ABC) scale, and the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P). Results No significant difference in maximal walking speed between the walking adaptability (n = 17) and conventional locomotor and strength (n = 18) training groups was found 6 weeks after training at follow-up (−0.05 m/s; 95% CI = −0.12-0.03). In addition, no significant group differences in secondary outcomes were found. However, independent of intervention, significant improvements over time were found for maximal walking speed, SCI-FAP, ABC, and USER-P restrictions scores. Conclusions. Our findings suggest that walking adaptability training may not be superior to conventional locomotor and strength training for improving walking capacity, functional ambulation, balance confidence, or participation in ambulatory people with iSCI. Trial Registration Dutch Trial Register; Effect of GRAIL training in iSCI.

Quantifying walking speeds in relation to ankle biomechanics on a real-time interactive gait platform: a musculoskeletal modeling approach in healthy adults.

Background: Given the inherent variability in walking speeds encountered in day-to-day activities, understanding the corresponding alterations in ankle biomechanics would provide valuable clinical insights. Therefore, the objective of this study was to examine the influence of different walking speeds on biomechanical parameters, utilizing gait analysis and musculoskeletal modelling. Methods: Twenty healthy volunteers without any lower limb medical history were included in this study. Treadmill-assisted gait-analysis with walking speeds of 0.8m/s and 1.1m/s was performed using the Gait Real-time Analysis Interactive Lab (GRAIL®). Collected kinematic data and ground reaction forces were processed via the AnyBody® modeling system to determine ankle kinetics and muscle forces of the lower leg. Data were statistically analyzed using statistical parametric mapping to reveal both spatiotemporal and magnitude significant differences. Results: Significant differences were found for both magnitude and spatiotemporal curves between 0.8m/s and 1.1m/s for the ankle flexion (p < 0.001), subtalar force (p < 0.001), ankle joint reaction force and muscles forces of the M. gastrocnemius, M. soleus and M. peroneus longus ( = 0.05). No significant spatiotemporal differences were found between 0.8m/s and 1.1m/s for the M. tibialis anterior and posterior. Discussion: A significant impact on ankle joint kinematics and kinetics was observed when comparing walking speeds of 0.8m/s and 1.1m/s. The findings of this study underscore the influence of walking speed on the biomechanics of the ankle. Such insights may provide a biomechanical rationale for several therapeutic and preventative strategies for ankle conditions.

Autonomous Control of Music to Retrain Walking After Stroke

Background Post-stroke care guidelines highlight continued rehabilitation as essential; however, many stroke survivors cannot participate in outpatient rehabilitation. Technological advances in wearable sensing, treatment algorithms, and care delivery interfaces have created new opportunities for high-efficacy rehabilitation interventions to be delivered autonomously in any setting (ie, clinic, community, or home). Methods We developed an autonomous rehabilitation system that combines the closed-loop control of music with real-time gait analysis to fully automate patient-tailored walking rehabilitation. Specifically, the mechanism-of-action of auditory-motor entrainment is applied to induce targeted changes in the post-stroke gait pattern by way of targeted changes in music. Using speed-controlled biomechanical and physiological assessments, we evaluate in 10 individuals with chronic post-stroke hemiparesis the effects of a fully-automated gait training session on gait asymmetry and the energetic cost of walking. Results Post-treatment reductions in step time (Δ: −12 ± 26%, P = .027), stance time (Δ: −22 ± 10%, P = .004), and swing time (Δ: −15 ± 10%, P = .006) asymmetries were observed together with a 9 ± 5% reduction (P = .027) in the energetic cost of walking. Changes in the energetic cost of walking were highly dependent on the degree of baseline energetic impairment (r =− .90, P < .001). Among the 7 individuals with a baseline energetic cost of walking larger than the normative value of healthy older adults, a 13 ± 4% reduction was observed after training. Conclusions The closed-loop control of music can fully automate walking rehabilitation that markedly improves walking after stroke. Autonomous rehabilitation delivery systems that can safely provide high-efficacy rehabilitation in any setting have the potential to alleviate access-related care gaps and improve long-term outcomes after stroke.

Barefoot walking is more stable in the gait of balance recovery in older adults

BackgroundPerturbation-based balance training on a treadmill is an emerging method of gait stability training with a characteristic task nature that has had positive and sustained effects on balance recovery strategies and fall reduction. Little is known about the effects produced by shod and barefoot walking. We aimed to investigate which is more appropriate, shod or barefoot walking, for perturbation-based balance training in older adults.MethodsFourteen healthy older adults (age: 68.29 ± 3.41 years; body height: 1.76 ± 0.10 m; body mass: 81.14 ± 14.52 kg) performed normal and trip-like perturbed walking trials, shod and barefoot, on a treadmill of the Gait Real-time Analysis Interactive Lab. The marker trajectories data were processed by Human Body Model software embedded in the Gait Offline Analysis Tool. The outcomes of stride length variability, stride time variability, step width variability, and swing time variability were computed and statistically analyzed by a two-way repeated-measures analysis of variance (ANOVA) based on gait pattern (normal gait versus perturbed recovery gait) and footwear condition (shod versus barefoot).ResultsFootwear condition effect (p = 0.0310) and gait pattern by footwear condition interaction effect (p = 0.0055) were only observed in swing time variability. Gait pattern effects were detected in all four outcomes of gait variability.ConclusionsSwing time variability, independent of gait speed, could be a valid indicator to differentiate between footwear conditions. The lower swing time variability in perturbed recovery gait suggests that barefoot walking may be superior to shod walking for perturbation-based balance training in older adults.

Visual Feedback and Virtual Reality in Gait Rehabilitation of Hemiparetic Children and Teenagers after Acquired Brain Injury: A Pilot Study

Hemiparesis as a term refers to a neurological disorder that can be extremely variable, especially with regard to walking abilities. Few works have assessed the use of virtual reality and biofeedback in children and adolescents with hemiparesis. The aim of this study is to provide insights about the rehabilitation of hemiparetic children and teenagers with visual biofeedback in a virtual reality environment. Six hemiparetic subjects (mean age 13.13 years, age range (7-18), 4 males) received 20 personalized rehabilitation GRAIL (Gait Real-time Analysis Interactive Lab) sessions plus 20 sessions of traditional physiotherapy. After an initial evaluation of rehabilitation needs, training focused on gait pattern correction (GP), walking endurance (WE), or gross motor functions (GMFs). All subjects were assessed for their gait analysis by GRAIL, the Gross Motor Function Measure (GMFM), and the 6-Minute Walking Test (6MWT) before and after rehabilitation. All subjects reached their rehabilitation goals, save for one who showed reduced collaboration. In addition, 4 subjects reached a better GP, 3 subjects reported improvements in WE, and 2 subjects improved in GMF. This personalized training with visual biofeedback delivered in a VR setting appears to be effective in modifying motor control and improving gait pattern, in addition to resistance and functional activities, in subjects with hemiparesis.

The Dual-Task Effects of Conversating While Walking on Gait Spatiotemporal Parameters in Healthy Young Adults

Introduction: Conversating during walking is an everyday multitask activity, if compromised may lead to gait instability or a fall. This study aims to examine the dual tasking effects of conversating while walking on gait performance conducted in a virtual reality environment. Methods: This cross-sectional study recruited thirty healthy individuals (27.3 ± 5.7 years, 23 males) through a purposive sampling method. They completed two sessions of three-minute tasks on an instrumented dual-belt treadmill: silent walking (ST) and conversational dual-tasking (DTC) in a Real-Time Gait Analysis Lab, Cardiff University. The gait speed, stride length, and step width between ST and DTC were compared using a two-way repeated-measures analysis of variance (RM ANOVA), with a p-value of 0.05. Results: The results of this study showed a significant (Cond x phase) interaction effect on gait speed, stride length, and step width (p&lt;0.05), respectively. In contrast, no significant (Cond) main effect was found on any gait parameters (p&gt;0.05). Conclusion: Conversating while walking may influence gait performance, possibly through the extrinsic focus of attentiveness in healthy adults.

Lower extremity joint compensatory effects during the first recovery step following slipping and stumbling perturbations in young and older subjects

BackgroundThe lower extremity may play a crucial role in compensating for gait perturbations. The study aimed to explore the mechanism of perturbation compensation by investigating the gait characteristics and lower extremity joint moment effects in young (YS) and older subjects (OS) during the first recovery gait following slipping (slipping_Rec1) and stumbling (stumbling_Rec1).MethodAn automatic perturbation-triggered program was developed using D-Flow software based on the Gait Real-time Analysis Interactive Lab to induce the two aforementioned perturbations. Marker trajectories and ground reaction forces were recorded from 15 healthy YS (age: 26.53 ± 3.04 years; body height: 1.73 ± 0.07 m; body mass: 66.81 ± 11.44 kg) and 15 healthy OS (age: 68.33 ± 3.29 years; body height: 1.76 ± 0.10 m; body mass: 81.13 ± 13.99 kg). The Human Body Model was used to compute the variables of interest. One-way analysis of variance and independent samples t-test statistical analyses were performed.ResultsIn slipping_Rec1 and stumbling_Rec1, the change in gait pattern was mainly reflected in a significant increase in step width, no alterations in step length and stance/swing ratio were revealed. Based on perturbed task specificity, lower extremity joint moments increased or decreased at specific phases of the gait cycle in both YS and OS in slipping_Rec1 and stumbling_Rec1 compared to normal gait. The two perturbed gaits reflected the respective compensatory requirements for the lower extremity joints, with both sagittal and frontal joint moments producing compensatory effects. The aging effect was not reflected in the gait pattern, but rather in the hip extension moment during the initial stance of slipping_Rec1.ConclusionsSlipping appears to be more demanding for gait recovery than stumbling. Gait perturbation compensatory mechanisms for OS should concentrate on ankle strategy in the frontal plane and counter-rotation strategy around the hip.

Compensatory Responses During Slip-Induced Perturbation in Patients With Knee Osteoarthritis Compared With Healthy Older Adults: An Increased Risk of Falls?

Background: Functional impairment of the knee joint affected by osteoarthritis and loss of muscle strength leads to a significant increase in the number of falls. Nevertheless, little is known about strategies for coping with gait perturbations in patients with knee osteoarthritis (KOA). Thus, this study aimed to examine the compensatory strategies of patients with KOA in response to a backward slip perturbation compared with healthy older adults. Methods: An automated perturbation program was developed by using D-Flow software based on the Gait Real-time Analysis Interactive Lab, and an induced backward slip perturbation was implemented on nine patients with severe KOA (68.89 ± 3.59 years) and 15 age-matched healthy older adults (68.33 ± 3.29 years). Step length, gait speed, range of motion, vertical ground reaction forces, lower extremity joint angles, and joint moments were computed and analyzed. Results: Compared with older adults, patients with KOA had significantly lower step length, gait speed, and vertical ground reaction forces in both normal walking and the first recovery step following backward slip perturbations. Inadequate flexion and extension of joint angles and insufficient generation of joint moments predispose patients with KOA to fall. Hip extension angle and flexion moment, knee range of motion, and vertical ground reaction forces are key monitoring variables. Conclusion: The risk of falls for patients with KOA in response to backward slip perturbations is higher. Patients with KOA should focus not only on quadriceps muscle strength related to knee range of motion but also on improving hip extensor strength and activation through specific exercises. Targeted resistance training and perturbation-based gait training could be better options.

Did you even see that? visual sensory processing of single stimuli under different locomotor loads.

Modern living and working environments are more and more interspersed with the concurrent execution of locomotion and sensory processing, most often in the visual domain. Many job profiles involve the presentation of visual information while walking, for example in warehouse logistics work, where a worker has to manage walking to the correct aisle to pick up a package while being presented with visual information over data-glasses concerning the next order. Similar use-cases can be found in manufacturing jobs, for example in car montage assembly lines where next steps are presented via augmented reality headsets while walking at a slow pace. Considering the overall scarcity of cognitive resources available to be deployed to either the cognitive or motor processes, task performance decrements were found when increasing load in either domain. Interestingly, the walking motion also had beneficial effects on peripheral contrast detection and the inhibition of visual stream information. Taking these findings into account, we conducted a study that comprised the detection of single visual targets (Landolt Cs) within a broad range of the visual field (-40° to +40° visual angle) while either standing, walking, or walking with concurrent perturbations. We used questionnaire (NASA-TLX), behavioral (response times and accuracy), and neurophysiological data (ERPs and ERSPs) to quantify the effects of cognitive-motor interference. The study was conducted in a Gait Real-time Analysis Interactive Laboratory (GRAIL), using a 180° projection screen and a swayable and tiltable dual-belt treadmill. Questionnaire and behavioral measures showed common patterns. We found increasing subjective physical workload and behavioral decrements with increasing stimulus eccentricity and motor complexity. Electrophysiological results also indicated decrements in stimulus processing with higher stimulus eccentricity and movement complexity (P3, Theta), but highlighted a beneficial role when walking without perturbations and processing more peripheral stimuli regarding earlier sensory components (N1pc/N2pc, N2). These findings suggest that walking without impediments can enhance the visual processing of peripheral information and therefore help with perceiving non-foveal sensory content. Also, our results could help with re-evaluating previous findings in the context of cognitive-motor interference, as increased motor complexity might not always impede cognitive processing and performance.

Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices.

With the advent of the Internet of Things (IoT), human-assistive technologies in healthcare services have reached the peak of their application in terms of diagnosis and treatment process. These devices must be aware of human movements to provide better aid in clinical applications as well as the user's daily activities. In this context, real-time gait analysis remains to be key catalyst for developing intelligent assistive devices. In addition to machine and deep learning algorithms, gait recognition systems have significantly improved in terms of high accuracy recognition. However, most of the existing models are focused on improving gait recognition while ignoring the computational overhead that affects the accuracy of detection and even remains unsuitable for real-time implementation. In this research paper, we proposed a hybrid gated recurrent unit (GRU) based on BAT-inspired extreme convolutional networks (BAT-ECN) for the effective recognition of human activities using gait data. The gait data are collected by implanting the wearable Internet of Things (WIoT) devices invasively. Then, a novel GRU and ECN networks are employed to extract the spatio-temporal features which are then used for classification to realize gait recognition. Extensive and comprehensive experimentations have been carried out to evaluate the proposed model using real-time datasets and also other benchmarks such as whuGait and OU-ISIR datasets. To prove the excellence of the proposed learning model, we have compared the model's performance with the other existing hybrid models. Results demonstrate that the proposed model has outperformed the other learning models in terms of high gait classification and less computational overhead.

Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment

ObjectiveTo determine whether gait and accelerometric features can predict disorientation events in young and older adults.MethodsCognitively healthy younger (18–40 years, n = 25) and older (60–85 years, n = 28) participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab (GRAIL) system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized mixed effect models. The accuracy of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines (SVM) and Hidden Markov models (HMM).ResultsBayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors.ConclusionDisorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events.

Development and Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment

Gait training in a virtual reality (VR) environment is promising for children affected by different disorders. However, the efficacy of VR therapy is still under debate, and more research is needed to clarify its effects on clinical conditions. The combination of VR with neuroimaging methods, such as the electroencephalography (EEG), might help in answering this need. The aim of the present work was to set up and test a system for the multimodal analysis of the gait pattern during VR gait training of pediatric populations by analyzing the EEG correlates as well as the kinematic and kinetic parameters of the gait. An EEG system was integrated with the Gait Real-time Analysis Interactive Lab (GRAIL). We developed and validated, with healthy adults (n = 5) and children (n = 4, healthy or affected by cerebral palsy (CP)), the hardware and software integration of the two systems, which allowed the synchronization of the acquired signals and a reliable identification of the initial contact (IC) of each gait cycle, showing good sensitivity and critical success index values. Moreover, we tested the multimodal acquisition by successfully analyzing EEG data and kinematic and kinetic parameters of one healthy child and one child with CP. This system gives the possibility of monitoring the effect of the VR therapy and studying the neural correlates of gait.

MATLAB-based tools for automated processing of motion tracking data provided by the GRAIL.

The ability for independent bipedal locomotion is an important prerequisite for autonomous mobility and participation in everyday life. Walking requires not only a functional musculoskeletal unit but relies on coordinated activation of muscles and may even require cognitive resources. The time-resolved monitoring of the position of joints, feet, legs and other body segments relative to each other alone or in combination with simultaneous recording of ground reaction forces and concurrent measurement of electrical muscle activity, using surface electromyography, are well-established tools for the objective assessment of gait. The Gait Real-time Analysis Interactive Lab (GRAIL) has been introduced for gait analysis in a highly standardized and well-controlled virtual environment. However, apart from high computing capacity and sophisticated software required to run the system, handling of GRAIL data is challenging due to the utilization of different software packages resulting in a huge amount of data stored using different file formats and different sampling rates. These issues make gait analysis even with such a sophisticated instrument rather tedious, especially within the frame of an experimental or clinical study. A user-friendly Matlab based toolset for automated processing of motion capturing data recorded using the GRAIL, with the inherent option for batch analysis was developed. The toolset allows the reading, resampling, filtering and synchronization of data stored in different input files recorded with the GRAIL. It includes a coordinate-based algorithm for the detection of initial contact and toe-off events to split and normalize data relative to gait cycles. Batch processing of multiple measurements and automatic detection of outliers is possible. The authors hope that the toolset will be useful to the research community and invite everyone to use, modify or implement it in their own work.

A simulator for both manual and powered wheelchairs in immersive virtual reality CAVE

A large number of people in the world need to use a wheelchair because of different disabilities. Driving a wheelchair requires complex physical and cognitive abilities which need to be trained. Virtual training helps users acquire driving skills in a safe environment. The aim of this paper is to describe and technically validate simulation models for both manual (MW) and powered wheelchairs (PW) based on immersive virtual reality CAVE (VR). As VR system, the Gait Real-time Analysis Interactive Lab (GRAIL) was used, a CAVE equipped with a motion platform with two degrees of freedom and an optoelectronic motion capture system. A real wheelchair was positioned onto the motion platform with rear wheels free to turn in MW modality, and a commercial joystick was installed on an armrest to simulate the PW modality. Passive markers were used to track the wheel rotation, the joystick and the user hand motion. Custom D-flow applications were developed to manage virtual scene response to user actions. Overground tests, based on single wheel rotation, were performed to verify the simulation model reliability. Quantitative results demonstrated that the MW simulator kinematics was consistent with a real wheelchair overground in the absence of wheel slip and inertia (median error for MW 0.40 °, no systematic bias p = 0.943, high correlation rho > 0.999, p < 0.01). The proposed solution is flexible and adaptable to different wheelchairs, joysticks and optoelectronic systems. The main limitation is the absence of force feedback. Nevertheless, it is a reliable prototype that can be used to validate new virtual scenarios as well as for wheelchair training. The next steps include the system validation with real end users and assessment of the simulator effectiveness as a training tool.

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review.

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

A New Force Myography-Based Approach for Continuous Estimation of Knee Joint Angle in Lower Limb Amputees and Able-Bodied Subjects.

In this paper, we present a new method for estimating knee joint angle using force myography. The technique utilized force myogram signals from thigh muscles while subjects walked on a treadmill at different speeds, i.e., slow, medium, fast, and run. An eight-channel in-house force myography (FMG) data acquisition system was developed to collect the data wirelessly from seven healthy subjects and a transfemoral amputee. An artificial neural network was employed to estimate the knee joint angle from force myogram signals. The root-mean-square error across the healthy subjects was 6.9±1.5° at slow (1.5km/hr), 6.5±1.3° at medium (4km/hr), 7.4±2.2° at fast (6km/hr) speeds, and 8.1±2.2° while running (8km/hr). The root-mean-square error, across the trials, for the transfemoral amputee was 4.0±1.2° at slow (1km/hr), 3.2±0.6° at medium (2km/hr) and 3.8±0.9° at fast (3km/hr) speeds. The proposed approach is useful in real-time gait analysis. The system is easily wearable, convenient in out-door use, portable, and commercially viable.

Effect of Virtual Reality Gait Training on Participation in Survivors of Subacute Stroke: A Randomized Controlled Trial.

ObjectiveAfter stroke, people experience difficulties with walking that lead to restrictions in participation in daily life. The purpose of this study was to examine the effect of virtual reality gait training (VRT) compared to non–virtual reality gait training (non-VRT) on participation in community-living people after stroke.MethodsIn this assessor-blinded, randomized controlled trial with 2 parallel groups, people were included between 2 weeks and 6 months after stroke and randomly assigned to the VRT group or non-VRT group. Participants assigned to the VRT group received training on the Gait Real-time Analysis Interactive Lab (GRAIL), and participants assigned to the non-VRT group received treadmill training and functional gait exercises without virtual reality. Both training interventions consisted of 12 30-minute sessions during 6 weeks. The primary outcome was participation measured with the restrictions subscale of the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P) 3 months postintervention. Secondary outcomes included subjective physical functioning, functional mobility, walking ability, dynamic balance, walking activity, fatigue, anxiety and depression, falls efficacy, and quality of life.ResultsTwenty-eight participants were randomly assigned to the VRT group and 27 to the non-VRT group, of whom 25 and 22 attended 75% or more of the training sessions, respectively. No significant differences between the groups were found over time for the USER-P restrictions subscale (1.23; 95% CI = −0.76 to 3.23) or secondary outcome measures. Patients’ experiences with VRT were positive, and no serious adverse events were related to the interventions.ConclusionsThe effect of VRT was not statistically different from non-VRT in improving participation in community-living people after stroke.ImpactAlthough outcomes were not statistically different, treadmill-based VRT was a safe and well-tolerated intervention that was positively rated by people after stroke. VR training might, therefore, be a valuable addition to stroke rehabilitation.Lay SummaryVRT is feasible and was positively experienced by people after stroke. However, VRT was not more effective than non-VRT for improving walking ability and participation after stroke.

Reliability of gait parameters in male and female healthy adults during self-paced treadmill-based walking

Background/aims The purpose of this study was to examine the intra-rater reliability of the Gait Real-time Analysis Interactive Laboratory system during self-paced mode, in repeated gait analysis of healthy individuals. Methods A total of 10 healthy men (age: 35.4 ± 13.3 years; body mass index: 25.2 ± 4.3) and 10 healthy women (age: 41.1 ± 16.4 years; body mass index: 24.5 ± 2.6) were split into two groups according to sex. They walked on a split-belt, self-paced treadmill. Each participant completed two gait assessments separated by an average of 7 ± 3 days. Key gait kinematic, kinetic and spatial-temporal parameters were analysed. The interclass correlation coefficient, standard error of measurement and minimum detectable change were calculated to evaluate the reliability of these gait parameters. Results There was a high repeatability of spatial temporal and excellent repeatability of kinematic and kinetic parameters in both groups. Conclusions The findings suggest that the Gait Real-time Analysis Interactive Laboratory system in self-paced mode is a good instrument to evaluate gait parameters for women as well as men.