Gait Biofeedback Research Articles

Gait speed and individual characteristics are related to specific gait metrics in neurotypical adults

Gait biofeedback is a well-studied strategy to reduce gait impairments such as propulsion deficits or asymmetric step lengths. With biofeedback, participants alter their walking to reach the desired magnitude of a specific parameter (the biofeedback target) with each step. Biofeedback of anterior ground reaction force and step length is commonly used in post-stroke gait training as these variables are associated with self-selected gait speed, fall risk, and the energy cost of walking. However, biofeedback targets are often set as a function of an individual’s baseline walking pattern, which may not reflect the ideal magnitude of that gait parameter. Here we developed prediction models based on speed, leg length, mass, sex, and age to predict anterior ground reaction force and step length of neurotypical adults as a possible method for personalized biofeedback. Prediction of these values on an independent dataset demonstrated strong agreement with actual values, indicating that neurotypical anterior ground reaction forces can be estimated from an individual’s leg length, mass, and gait speed, and step lengths can be estimated from individual’s leg length, mass, age, sex, and gait speed. Unlike approaches that rely on an individual’s baseline gait, this approach provides a standardized method to personalize gait biofeedback targets based on the walking patterns exhibited by neurotypical individuals with similar characteristics walking at similar speeds without the risk of over- or underestimating the ideal values that could limit feedback-mediated reductions in gait impairments.

Immediate improvements in post-stroke gait biomechanics are induced with both real-time limb position and propulsive force biofeedback

BackgroundParetic propulsion [measured as anteriorly-directed ground reaction forces (AGRF)] and trailing limb angle (TLA) show robust inter-relationships, and represent two key modifiable post-stroke gait variables that have biomechanical and clinical relevance. Our recent work demonstrated that real-time biofeedback is a feasible paradigm for modulating AGRF and TLA in able-bodied participants. However, the effects of TLA biofeedback on gait biomechanics of post-stroke individuals are poorly understood. Thus, our objective was to investigate the effects of unilateral, real-time, audiovisual TLA versus AGRF biofeedback on gait biomechanics in post-stroke individuals.MethodsNine post-stroke individuals (6 males, age 63 ± 9.8 years, 44.9 months post-stroke) participated in a single session of gait analysis comprised of three types of walking trials: no biofeedback, AGRF biofeedback, and TLA biofeedback. Biofeedback unilaterally targeted deficits on the paretic limb. Dependent variables included peak AGRF, TLA, and ankle plantarflexor moment. One-way repeated measures ANOVA with Bonferroni-corrected post-hoc comparisons were conducted to detect the effect of biofeedback on gait biomechanics variables.ResultsCompared to no-biofeedback, both AGRF and TLA biofeedback induced unilateral increases in paretic AGRF. TLA biofeedback induced significantly larger increases in paretic TLA than AGRF biofeedback. AGRF biofeedback increased ankle moment, and both feedback conditions increased non-paretic step length. Both types of biofeedback specifically targeted the paretic limb without inducing changes in the non-paretic limb.ConclusionsBy showing comparable increases in paretic limb gait biomechanics in response to both TLA and AGRF biofeedback, our novel findings provide the rationale and feasibility of paretic TLA as a gait biofeedback target for post-stroke individuals. Additionally, our results provide preliminary insights into divergent biomechanical mechanisms underlying improvements in post-stroke gait induced by these two biofeedback targets. We lay the groundwork for future investigations incorporating greater dosages and longer-term therapeutic effects of TLA biofeedback as a stroke gait rehabilitation strategy.Trial registrationNCT03466372

Participant-Level Analysis of the Effects of Interventions on Patient-Reported Outcomes in Patients With Chronic Ankle Instability.

Intervention studies for chronic ankle instability (CAI) often focus on improving physical impairments of the ankle complex. However, using an impairments-focused approach may miss psychological factors that may mediate function and recovery. Patient-reported outcome (PRO) measures can be used to assess several dimensions of the health-related quality of life (HRQoL) and deliver enhanced patient-centered care. Therefore, the purpose of this investigation was to evaluate group-level improvements in HRQoL and treatment response rates following various interventions in patients with CAI. Cross-sectional. Data from 7 previous studies were pooled by the chronic ankle instability outcomes network for participant-level analysis, resulting in 136 patients with CAI. Several interventions were assessed including balance training, gait biofeedback, joint mobilizations, stretching, and strengthening, with treatment volume ranging from 1 to 4weeks. Outcome measures were PROs that assessed ankle-specific function (Foot and Ankle Ability Measure), injury-related fear (Tampa Scale of Kinesiophobia and Fear Avoidance Belief Questionnaire), and global well-being (Disablement in the Physically Active); the PROs assessed varied between studies. Preintervention to postintervention changes were evaluated using separate Wilcoxon signed-rank tests and effect sizes, and a responder analysis was conducted for each PRO. Significant, moderate to large improvements were observed in PROs that assessed ankle-specific function, injury-related fear, and global well-being following intervention (P < .001). Responder rates ranged from 39.0% to 53.3%, 12.8% to 51.4%, and 37.8% for ankle specific function, injury-related fear, and global well-being, respectively. Various interventions can lead to positive improvements in HRQoL in patients with CAI. Treatment response rates at improving HRQoL are similar to response rates at improving impairments such as balance, further reinforcing the need for individualized treatment approaches when treating a patient with CAI.

Utility of Gait Biofeedback Training to Improve Walking Biomechanics in Patients With Chronic Ankle Instability: A Critically Appraised Topic.

Chronic ankle instability (CAI) is a condition that involves feelings of the ankle "giving way," pain, and decreased self-reported function. Individuals with CAI often demonstrate persistent biomechanical impairments during gait that are associated with repetitive lateral ankle sprains (LAS) and the development of early onset ankle posttraumatic osteoarthritis (OA). Traditional rehabilitation strategies have not successfully improved these reported aberrant gait biomechanics; thus, traditional rehabilitation may not effectively reduce the risk of recurrent LAS and ankle OA among individuals with CAI. Conversely, targeted gait training with biofeedback may be effective at decreasing the risk of recurring LAS and ankle OA if these rehabilitation strategies can promote individuals with CAI to develop a gait strategy that protects against subsequent LAS and ankle OA. Can targeted gait biofeedback interventions cause individuals with CAI to implement a walking gait pattern that is not associated with recurrent LAS and ankle OA? Five studies assessed gait biofeedback interventions targeting plantar pressure and/or ankle kinematics involving visual biofeedback (n = 3), auditory biofeedback (n = 1), and haptic biofeedback (n = 1). Plantar pressure was medially shifted during a single session while receiving biofeedback (n = 2), immediately after biofeedback (n = 1), and 5 minutes after receiving biofeedback (n = 1) in 3 studies. One study demonstrated reduced ankle inversion after 8 sessions of biofeedback training. One study did not substantially improve plantar pressure while receiving visual feedback. Targeted gait training strategies appear effective in acutely altering gait biomechanics in individuals with CAI while receiving, and immediately after, biofeedback has been removed. Long-term outcomes are not currently established for gait training strategies in those with CAI. Limited evidence (grade B) suggests that targeted gait biofeedback strategies can alter specific CAI gait biomechanics to a strategy not associated with recurrent LAS, and ankle OA immediately, and after, multiple sessions of gait training.

Gait biofeedback training in people with Parkinson’s disease: a pilot study

BackgroundPeople with Parkinson’s disease (PD) are at a high risk of falls, with ~ 60% experiencing a fall each year. Greater mediolateral head and pelvis motion during gait are known to increase the risk of falling in PD, however the ability to modify these aspects of gait has not been examined. Thus, this study aimed to examine whether mediolateral trunk, head and pelvis motion during walking could be successfully decreased in people with PD using real-time biofeedback.MethodsParticipants were provided with real-time biofeedback regarding their mediolateral trunk lean via a visual projection whilst walking along an 8-m indoor walkway. Using the feedback provided, they were asked to reduce the magnitude of their mediolateral trunk lean. Gait was recorded for four conditions (i) Baseline, (ii) Intervention, (iii) immediately Post-Intervention, and (iv) 1-week Follow-Up. Biomechanical variables associated with falls risk were compared between conditions, including normalised mediolateral motion, gait velocity and stride length.ResultsA reduction in mediolateral trunk lean, step length and gait velocity from Baseline to the Intervention and Post-intervention conditions was observed. Contrary to this, increased normalised ML pelvis and trunk motion was observed between the Baseline and Intervention conditions, but returned to Baseline levels in the Post-Intervention condition.ConclusionsResults from the current study suggest that real-time visual biofeedback may be effective at modifying specific gait characteristics that are associated with falls in PD. Further research is required to better understand the influence of this intervention approach on falls incidence.Trial registration Australian New Zealand Clinical Trials Registry ACTRN12620000994987. Registered 10 June 2020 - Retrospectively registered, https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380324

Timing of propulsion-related biomechanical variables is impaired in individuals with post-stroke hemiparesis

BackgroundIn individuals with post-stroke hemiparesis, reduced paretic leg propulsion, measured through anterior ground reaction forces (AGRF), is a common and functionally-relevant gait impairment. Deficits in other biomechanical variables such as plantarflexor moment, ankle power, and ankle excursion contribute to reduced propulsion. While reduction in the magnitude of propulsion post-stroke is well studied, here, our objective was to compare the timing of propulsion-related biomechanical variables. Research questionAre there differences in the timing of propulsion and propulsion-related biomechanical variables between able-bodied individuals, the paretic leg, and non-paretic leg of post-stroke individuals? MethodsNine able-bodied and 13 post-stroke individuals completed a gait analysis session comprising treadmill walking trials at each participant’s self-selected speed. Two planned independent sample t-tests were conducted to detect differences in the timing of dependent variables between the paretic versus non-paretic leg post-stroke and paretic leg versus the dominant leg of able-bodied individuals. ResultsPost-stroke individuals demonstrated significantly earlier timing of peak AGRF of their paretic leg versus their non-paretic leg and able-bodied individuals. Post-stroke participants displayed earlier timing of peak power of their paretic leg versus their non-paretic leg and able-bodied individuals, and earlier timing of peak ankle moment of the paretic leg versus able-bodied. No significant differences were detected in the timing of peak ankle angle. SignificanceThe earlier onset of peak AGRF, peak ankle power, and peak ankle moment may be an important, under-studied biomechanical factor underlying stroke gait impairments, and a potential therapeutic target for stroke gait retraining. Future investigations can explore the use of interventions such as gait biofeedback to normalize the timing of these peaks, thereby improving propulsion and walking function post-stroke.

Gluteal Activity During Gait in Patients With Chronic Ankle Instability Following Rehabilitation: A Randomized Controlled Trial.

Ankle positioning gait biofeedback (GBF) has improved ankle inversion for patients with chronic ankle instability. However, the effects on proximal deficits remain unknown. The purpose of this study was to determine the effects of impairment-based rehabilitation with GBF and without biofeedback on gluteal activity during walking in patients with chronic ankle instability. Randomized controlled trial. Eighteen patients with chronic ankle instability (14 women and 4 men; age 22 [4] y; height 171 [10]cm; mass 71.6 [13.8]kg) were recruited from a university setting, following International Ankle Consortium guidelines. Patients were randomly allocated to GBF or without biofeedback groups (N = 9 per group). Both groups performed 4 weeks of exercises and treadmill walking. The GBF group alone received feedback on frontal ankle positioning at initial contact during walking. Ultrasound videos of the gluteus maximus and medius were recorded during walking at baseline and follow-up by a blinded clinician. Gluteal activity ratios were obtained at each 10% of the gait cycle. Statistical parametric mapping repeated-measures analysis of variance were used to compare groups and time points. Both groups demonstrated significantly increased gluteus medius activity across the gait cycle compared with baseline (P < .01, mean differences: 0.13-0.21, Hedge g: 0.97-1.89); however, there were no significant between-group differences. There were no statistically significant changes noted for the gluteus maximus. No adverse events were observed. Impairment-based rehabilitation led to increased gluteus medius activity, but GBF did not provide any additional improvement to this parameter. Clinicians may consider implementing impairment-based strengthening interventions to improve gluteus medius function during gait for patients with CAI.

Real-time kinematic-based detection of foot-strike during walking

Detection of foot-strike events is an integral part of gait analysis, as it allows the temporal registration of gait cycles. At the same time, it is necessary to register gait phases in real-time for applications such as wearable assistive devices and gait biofeedback that work synchronously with the human gait. Although many algorithms have been proposed for detecting foot-strikes with either wearable (e.g. Inertial Measurement Units (IMUs)) or non-wearable (e.g. force plates) sensors, there is a great need for real-time algorithms that rely only on recording the kinematics of the leg motion. This work proposes a novel and efficient kinematic algorithm, called the Foot VErtical & Sagittal Position Algorithm (F-VESPA), which has several advantages over existing methods. First, it accurately estimates foot-strike events using kinematic data without requiring access to future data points, hence achieving reduced latency during real-time implementation. Moreover, it does not require tuning of the utilized parameters, rendering it robust to different subjects and treadmill speeds. The algorithm is tested in a large set of subjects across various treadmill speeds, and it is shown to outperform even offline implementations of existing prominent kinematic algorithms. Using a 150 Hz data collection system, the F-VESPA achieved a median of 33 ms for the total true errors in detecting foot-strike. The F-VESPA is a highly responsive kinematic algorithm that can detect foot-strike events in real-time, with high accuracy, robustness and reduced latency, enabling real-time temporal registration of gait cycles.

Biofeedback for Post-stroke Gait Retraining: A Review of Current Evidence and Future Research Directions in the Context of Emerging Technologies.

Real-time gait biofeedback is a promising rehabilitation strategy for improving biomechanical deficits in walking patterns of post-stroke individuals. Because wearable sensor technologies are creating avenues for novel applications of gait biofeedback, including use in tele-health, there is a need to evaluate the state of the current evidence regarding the effectiveness of biofeedback for post-stroke gait training. The objectives of this review are to: (1) evaluate the current state of biofeedback literature pertaining to post-stroke gait training; and (2) determine future research directions related to gait biofeedback in context of evolving technologies. Our overall goal was to determine whether gait biofeedback is effective at improving stroke gait deficits while also probing why and for whom gait biofeedback may be an efficacious treatment modality. Our literature review showed that the effects of gait biofeedback on post-stroke walking dysfunction are promising but are inconsistent in methodology and therefore results. We summarize sources of methodological heterogeneity in previous literature, such as inconsistencies in feedback target, feedback mode, dosage, practice structure, feedback structure, and patient characteristics. There is a need for larger-sample studies that directly compare different feedback parameters, employ more uniform experimental designs, and evaluate characteristics of potential responders. However, as these uncertainties in existing literature are resolved, the application of gait biofeedback has potential to extend neurorehabilitation clinicians' cues to individuals with post-stroke gait deficits during ambulation in clinical, home, and community settings, thereby increasing the quantity and quality of skilled repetitions during task-oriented stepping training. In addition to identifying gaps in previous research, we posit that future research directions should comprise an amalgam of mechanism-focused and clinical research studies, to develop evidence-informed decision-making guidelines for gait biofeedback strategies that are tailored to individual-specific gait and sensorimotor impairments. Wearable sensor technologies have the potential to transform gait biofeedback and provide greater access and wider array of options for clinicians while lowering rehabilitation costs. Novel sensing technologies will be particularly valuable for telehealth and home-based stepping exercise programs. In summary, gait biofeedback is a promising intervention strategy that can enhance efficacy of post-stroke gait rehabilitation in both clinical and tele-rehabilitation settings and warrants more in-depth research.

Comparison of the effects of real-time propulsive force versus limb angle gait biofeedback on gait biomechanics

BackgroundReduced forward propulsion during gait, measured as the anterior component of the ground reaction force (AGRF), may contribute to slower walking speeds in older adults and gait dysfunction in individuals with neurological impairments. Trailing limb angle (TLA) is a clinically important gait parameter that is associated with AGRF generation. Real-time gait biofeedback can induce modifications in targeted gait parameters, with potential to modulate AGRF and TLA. However, the effects of real-time TLA biofeedback on gait biomechanics have not been studied thus far. Research questionWhat are the effects of unilateral, real-time, audiovisual trailing limb angle biofeedback on gait biomechanics in able-bodied individuals? MethodsTen able-bodied adults participated in one session of treadmill-based gait analyses comprising 60-second walking trials under three conditions: no biofeedback, AGRF biofeedback, and TLA biofeedback. Biofeedback was provided unilaterally to the right leg. Dependent variables included AGRF, TLA, ankle moment, and ankle power. One-way repeated measures ANOVA with post-hoc tests were conducted to determine the effect of the biofeedback conditions on gait parameters. ResultsCompared to no biofeedback, both AGRF and TLA biofeedback induced significant increases in targeted leg AGRF without concomitant changes to the non-targeted leg AGRF. Targeted leg TLA was significantly larger during TLA biofeedback compared to AGRF biofeedback. Only AGRF biofeedback induced significant increases in ankle power; and only the TLA biofeedback condition induced increases in the non-targeted leg TLA. SignificanceOur novel findings provide support for the feasibility and promise of TLA as a gait biofeedback target. Our study demonstrates that comparable magnitudes of feedback-induced increases in AGRF in response to AGRF and TLA biofeedback may be achieved through divergent biomechanical strategies. Further investigation is needed to uncover the effects of TLA biofeedback on gait parameters in individuals with neuro-pathologies such as spinal cord injury or stroke.

Gait biofeedback and impairment‐based rehabilitation for chronic ankle instability

Our purpose was to analyze the effects of 4weeks of visual gait biofeedback (GBF) and impairment-based rehabilitation on gait biomechanics and patient-reported outcomes (PROs) in individuals with chronic ankle instability (CAI). Twenty-seven individuals with CAI participated in this randomized controlled trial (14 received no biofeedback (NBF), 13 received GBF). Both groups received 8 sessions of impairment-based rehabilitation. The GBF group received visual biofeedback to reduce ankle frontal plane angle at initial contact (IC) during treadmill walking. The NBF group walked for equal time during rehabilitation but without biofeedback. Dependent variables included three-dimensional kinematics and kinetics at the ankle, knee, and hip, electromyography amplitudes of 4 lower extremity muscles (tibialis anterior, fibularis longus, medial gastrocnemius, and gluteus medius), and PROs (Foot and Ankle Ability Measure Activities of Daily Living (FAAM-ADL), FAAM-Sport, Tampa Scale of Kinesiophobia (TSK), and Global Rating of Change (GROC)). The GBF group significantly decreased ankle inversion at IC (MD:-7.3º, g=1.6) and throughout the entire stride cycle (peak inversion: MD:-5.9º, g=1.2). The NBF group did not have significantly altered gait biomechanics. The groups were significantly different after rehabilitation for the FAAM-ADL (GBF: 97.1±2.3%, NBF: 92.0±5.7%), TSK (GBF: 29.7±3.7, NBF: 34.9±5.8), and GROC (GBF: 5.5±1.0, NBF:3.9±2.0) with the GBF group showing greater improvements than the NBF group. There were no significant differences between groups for kinetics or electromyography measures. The GBF group successfully decreased ankle inversion angle and had greater improvements in PROs after intervention compared to the NBF group. Impairment-based rehabilitation combined with visual biofeedback during gait training is recommended for individuals with CAI.

Transfer Learning Effects of Biofeedback Running Retraining in Untrained Conditions.

Running gait retraining via peak tibial shock biofeedback has been previously shown to reduce impact loading and mitigate running-related symptoms. In previous research, peak tibial shock is typically measured and trained for one limb at a single constant training speed during all training sessions. The goal of this study was to determine how runners transfer learning in the trained limb to the untrained limb at different unconstrained speeds. Thirteen runners (3 females, age = 41.1 ± 6.9 yr, running experience = 6.8 ± 4.4 yr, weekly running distance = 30.7 ± 22.2 km) underwent running gait biofeedback retraining via continuous tibial acceleration measured at the right distal tibia. Before and after the training, participants were asked to run at their self-selected constrained training speeds (2.8 ± 0.2 m·s) and at 110% and 90% of the training speed. Pretraining and posttraining peak tibial shock values for each limb were compared. Participants reduced peak tibial shock in the trained limb by 35% to 37% (P < 0.05, Cohen's d = 0.78-0.85), and in the untrained limb by 20% to 23% (P < 0.05, Cohen's d = 0.51-0.71) across the three testing speeds. The reduction was not significantly different between the trained and untrained limbs (P = 0.31-0.79, Cohen's d = 0.18-0.45). Similarly, there was no difference in peak tibial shock reduction among the three running speeds (P = 0.48-0.61, Cohen's d = 0.06-0.45). Participants demonstrated transfer learning effects evidenced by concomitant reduced peak tibial shock in the untrained limb, and the learning effects were retrained when running at a 10% variance of the training speed.

Effects of real-time gait biofeedback on paretic propulsion and gait biomechanics in individuals post-stroke

ObjectivesGait training interventions that target paretic propulsion induce improvements in walking speed and function in individuals post-stroke. Previously, we demonstrated that able-bodied individuals increase propulsion unilaterally when provided real-time biofeedback targeting anterior ground reaction forces (AGRF). The purpose of this study was to, for the first time, investigate short-term effects of real-time AGRF gait biofeedback training on post-stroke gait.MethodsNine individuals with post-stroke hemiparesis (6 females, age = 54 ± 12.4 years 39.2 ± 24.4 months post-stroke) completed three 6-minute training bouts on an instrumented treadmill. During training, visual and auditory biofeedback were provided to increase paretic AGRF during terminal stance. Gait biomechanics were evaluated before training, and during retention tests conducted 2, 15, and 30 minutes post-training. Primary dependent variables were paretic and non-paretic peak AGRF; secondary variables included paretic and non-paretic peak trailing limb angle, plantarflexor moment, and step length. In addition to evaluating the effects of biofeedback training on these dependent variables, we compared effects of a 6-minute biofeedback training bout to a non-biofeedback control condition.ResultsCompared to pre-training, significantly greater paretic peak AGRFs were generated during the 2, 15, and 30-minute retention tests conducted after the 18-minute biofeedback training session. Biofeedback training induced no significant effects on the non-paretic leg. Comparison of a 6-minute biofeedback training bout with a speed-matched control bout without biofeedback demonstrated a main effect for training type, with greater peak AGRF generation during biofeedback.DiscussionOur results suggest that AGRF biofeedback may be a feasible and promising gait training strategy to target propulsive deficits in individuals post-stroke.

Effects of Real-time Gait Biofeedback on Paretic Propulsion and Gait Biomechanics in Individuals Post-Stroke

Effects of Real-time Gait Biofeedback on Paretic Propulsion and Gait Biomechanics in Individuals Post-Stroke

Real-time gait event detection using wearable sensors

Real-time gait event detection is a requirement for functional electrical stimulation and gait biofeedback. This gait event detection should ideally be achieved using an ambulatory system of durable, lightweight, low-cost sensors. Previous research has reported issues with durability in footswitch systems. Therefore, this study describes the development and assessment of novel detection algorithms using footswitch and accelerometer sensors on 12 healthy individuals. Subjects were equipped with one force sensitive resistor on the heel, one accelerometer at the foot, and one accelerometer at the knee. Subjects performed 10, 8-m walking trials in each of three conditions: normal, slow, and altered (reduced knee ROM) walking. Data from a subset of four subjects were used to develop prediction algorithms for initial contact (IC). Subsequently, these algorithms were tested on the remaining eight subjects against standard forceplate IC data (threshold of 5 N on a rising edge). The footswitch force threshold algorithm was most accurate for IC detection (mean absolute error of 2.4 ± 2.1 ms) and was significantly more accurate ( p < 0.001) than the optimal accelerometer algorithm (mean absolute error of 9.5 ± 9.0 ms). The optimal accelerometer algorithm used data from both accelerometers, with IC determined from the second derivative of foot fore-aft acceleration. The error results for footswitch and accelerometer algorithms are lower (∼60%) than in previous research on ambulatory real-time gait event detection systems. Currently, footswitch systems must be recommended over accelerometer systems for accurate detection of IC, however, further research into accelerometer algorithms is merited due to its advantages as a durable, low-cost sensor.