Gait Sub-phases Research Articles

Curve Walking Reveals More Gait Impairments in Older Adults with Mild Cognitive Impairment than Straight Walking: A Kinect Camera-Based Study.

Mild cognitive impairment (MCI) can be an early sign of Alzheimer's disease and other types of dementia detectable through gait analysis. Curve walking, which demands greater cognitive and motor skills, may be more sensitive in MCI detection than straight walking. However, few studies have compared gait performance in older adults with and without MCI in these conditions. To compare the capability of curve and straight walking tests for the detection of MCI among older adults. We employed a Kinect v.2 camera to record the gait of 55 older adults (30 healthy controls, 25 with MCI) during single-task straight and curve walking tests. We examined 50 gait markers and conducted statistical analyses to compare groups and conditions. The trail was approved with protocol No. IR.SEMUMS.REC.1398.237 by the ethics committee of Semnan University of Medical Sciences in Iran. Older adults with MCI exhibited more compromised gait performance, particularly during curve walking. Curve walking outperformed straight walking in MCI detection, with several gait markers showing significant differences between healthy controls and MCI patients. These markers encompass average velocity, cadence, temporal markers (e.g., gait cycle subphase durations), spatial markers (e.g., foot position changes during gait subphases), and spatiotemporal markers (e.g., step and stride velocities). Our study suggests curve walking as a more informative and challenging test for MCI detection among older adults, facilitating early diagnosis using non-invasive, cost-effective tools like the Kinect v.2 camera, complementing cognitive assessments in early diagnosis, and tracking MCI progression to dementia.

Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier

Knee orthosis plays a vital role in enhancing the wellbeing and quality of life of individuals suffering from knee arthritis. This study explores a machine-learning-based methodology for predicting a user’s gait subphase using inertial measurement units (IMUs) for a semiactive orthosis. A musculoskeletal simulation is employed with the help of existing experimental motion-capture data to obtain essential metrics related to the gait cycle, which are then normalized and scaled. A meticulous data capture methodology using foot switches is used for precise synchronization with IMU data, resulting in comprehensive labeled subphase datasets. The integration of simulation results and labeled datasets provides activation data for effective knee flexion damping following which multiple supervised machine learning algorithms are trained and evaluated for performances. The time series forest classifier emerged as the most suitable algorithm, with an accuracy of 86 percent, against randomized convolutional kernel transform, K-neighbor time series classifier, and long short-term memory–fully convolutional network, with accuracies of 68, 76, and 78, respectively, showcasing exceptional performance scores, thereby rendering it an optimal choice for identifying gait subphases and achieving the desired level of damping for magnetorheological brake-mounted knee orthosis based on simulated results.

Abnormal gait partitioning and real-time recognition of gait phases in children with cerebral palsy

ObjectiveSince the gait of children with cerebral palsy (CP) lacks distinct foot event characteristics, it’s difficult to accurately distinguish the key gait events, especially in the stance phase. To solve the problem of gait phase detection for children with CP, a unique gait classification system is designed to detect the walking maneuvers during a gait cycle. This system can detect all gait phases of CP patients, and send the kinematic information to a rule-based algorithm through a series of sensors installed in the legs to identify the salient features of the signal. MethodsIn the light of the quasi-stiffness gait phase transformation as the classification guide, two sets of motion signal feature sets are extracted from the leg feature signals of 10 CP children, and two gait stage recognition algorithms are proposed to divide the six gait sub-phases. ResultsThe proposed algorithms of peak-zero-based angular velocity and thresholding-based angular realize the effective recognition of gait phases, especially for the stance phase. ConclusionThe proposed methods have the potential to obtain correct decoding of motion information and recognize gait phases. Significance: This study provides a new idea to solve the problem of difficult identification of gait phase for children with CP, which can be applied to a behavior-driven lower limb exoskeleton robot system for assisting walking and gait correction.

Estimation of Gait Subphase Time Parameters Based on a Human Electrostatic Field Detection System

The parameters of gait phases obtained by detecting gait events can provide a robust basis for the pathological diagnosis and rehabilitation of patients with lower limb disorders. In this paper, a method is proposed to divide the inner-stance phase and inner-swing phase by detecting the gait events of heel-off (HO) and swing to the highest position (SHP) in the stepping electrostatic gait signal (EGS) and to estimate the subphase time parameters. The gait events are detected by using the local extreme points of the integrated signal of the EGS. Four subphase temporal parameters are obtained after combining the local extrema of the EGS representing the initial contact (IC) and toe-off (TO) for the division of the inner-stance and inner-swing phases. Compared with the motion capture system (VICON), the detection accuracy of the proposed method reaches 98.5%, and the measurement error is within the range of ±60 ms. Pearson’s coefficient ‘r’ was used to verify the correlation between the four gait subphases estimated by the two methods, and excellent agreement was obtained (r > 0.95, p < 0.05). Most of the measurement results of the four subphase temporal gait parameters are within the 95% confidence interval of the Bland–Altman analysis chart. The repeatability of the proposed method was calculated through intraclass correlation coefficients (ICC) and showed good test-retest reliability (ICC > 0.85, p < 0.01). This method detects gait events and estimates subphase duration with high accuracy and can provide a foundation for the gait analysis of EGSs.

Variation of body weight supported treadmill training parameters during a single session can modulate muscle activity patterns in post-stroke gait

Evidence supporting the benefits of locomotor training (LT) to improve walking ability following stroke are inconclusive and could likely be improved with a better understanding of the effects of individual parameters i.e., body weight support (BWS), speed, and therapist assistance and their interactions with walking ability and specific impairments. We evaluated changes in muscle activity of thirty-seven individuals with chronic stroke (> 6 months), in response to a single session of LT at their self-selected or fastest-comfortable speed (FS) with three levels of BWS (0%, 15%, and 30%), and at FS with 30% BWS and seven different combinations of therapist assistance at the paretic foot, non-paretic foot, and trunk. Altered Muscle Activation Pattern (AMAP), a previously developed tool in our lab was used to evaluate the effects of LT parameter variation on eight lower-extremity muscle patterns in individuals with stroke. Repeated-measures mixed-model ANOVA was used to determine the effects of speed, BWS, and their interaction on AMAP scores. The Wilcoxon-signed rank test was used to determine the effects of therapist-assisted conditions on AMAP scores. Increased BWS mostly improved lower-extremity muscle activity patterns, but increased speed resulted in worse plantar flexor activity. Abnormal early plantar flexor activity during stance decreased with assistance at trunk and both feet, exaggerated plantar flexor activity during late swing decreased with assistance to the non-paretic foot or trunk, and diminished gluteus medius activity during stance increased with assistance to paretic foot and/or trunk. Therefore, different sets of training parameters have different immediate effects on activation patterns of each muscle and gait subphases.

Phase-dependent Modulation of Muscle Activity and Intermuscular Coupling During Walking in Patients After Stroke.

Many patients experience motor and sensory impairments after stroke, leading to gait disturbances. Analysis of muscle modulation mode during walking can provide evidence for neurological changes after stroke, while how stroke affects individual muscle activity and muscular coordination in certain gait sub-phases remains unclear. The purpose of the present study is to comprehensively investigate phase-dependent ankle muscle activity and intermuscular coupling patterns in post-stroke patients. In this experiment, 10 post-stroke patients, 10 young healthy subjects and 10 elderly healthy subjects were recruited. All subjects were asked to walk at their preferred speeds on the ground, and surface electromyography (sEMG) and marker trajectory data were collected simultaneously. The gait cycle of each subject was divided into four substages according to the labeled trajectory data. On this basis, fuzzy approximate entropy (fApEn) was used to analyze the complexity of ankle muscle activity during walking. And the transfer entropy (TE) was applied to reflect directed information transmission between ankle muscles. Results found that complexity of ankle muscles activities in patients after stroke showed similar trends to that in healthy subjects. Different from healthy subjects, the complexity of ankle muscle activity in patients with stroke tends to increase in most of the gait sub-phases. While TE values between the ankle muscles in patients with stroke tend to decrease throughout the gait cycle, especially in the second double support stage. Compared with age-matched healthy subjects, patients recruit more motor units throughout their gait while increasing muscle coupling to achieve gait function. The combined application of fApEn and TE provide a more comprehensive understanding of phase-dependent muscle modulation mechanisms in post-stroke patients.

Modular analysis of pooled muscle and kinematic data reveals the dynamics of the gait sub-phases

Modular analysis of pooled muscle and kinematic data reveals the dynamics of the gait sub-phases

Gait Subphases Classification Based on Hidden Markov Models using in-shoes Capacitive Pressure Sensors: Preliminary Results.

Gait cycle analysis is widely practiced to determine alterations of normal walking. The challenge is to choose the ideal systems that suit the studies. One possibility is to measure the interaction of the sole and the support surface and detect gait events related to the positioning of the foot. This work proposes a gait subphase classification based on Hidden Markov Model that identifies gait stance subphases from a foot pressure measurement. A sensorized insole was used to record the pressure under the foot with eight custom-made capacitive sensors. Tests were performed on six volunteers with a 10-meter trial test. Mean cadence and stance/swing ratio were calculated. These parameters match the normal range for the age of the volunteers found in the literature. The results show that the proposed model can classify the gait in 5 subphases using the Center of Pressure (CoP) anteroposterior position and velocity as input. Changes in the slope of the CoP marks the step between subphases. Clinical Relevance- Most gait studies are performed in highly equipped gait laboratories. Due to technical requirements and the high cost of implementing a gait laboratory, access to these services is difficult for a large population. For this reason, it is necessary to develop equipment, devices, and algorithm to further study pathological and healthy gait.

Performance Index for in Home Assessment of Motion Abilities in Ataxia Telangiectasia: A Pilot Study

Background. It has been shown in the very recent literature that human walking generates rhythmic motor patterns with hidden time harmonic structures that are represented (at the subject’s comfortable speed) by the occurrence of the golden ratio as the the ratio of the durations of specific walking gait subphases. Such harmonic proportions may be affected—partially or even totally destroyed—by several neurological and/or systemic disorders, thus drastically reducing the smooth, graceful, and melodic flow of movements and altering gait self-similarities. Aim. In this paper we aim at, preliminarily, showing the reliability of a technologically assisted methodology—performed with an easy to use wearable motion capture system—for the evaluation of motion abilities in Ataxia-Telangiectasia (AT), a rare infantile onset neurodegenerative disorder, whose typical neurological manifestations include progressive gait unbalance and the disturbance of motor coordination. Methods. Such an experimental methodology relies, for the first time, on the most recent accurate and objective outcome measures of gait recursivity and harmonicity and symmetry and double support subphase consistency, applied to three AT patients with different ranges of AT severity. Results. The quantification of the level of the distortions of harmonic temporal proportions is shown to include the qualitative evaluations of the three AT patients provided by clinicians. Conclusions. Easy to use wearable motion capture systems might be used to evaluate AT motion abilities through recursivity and harmonicity and symmetry (quantitative) outcome measures.

Gait phase detection by using a portable system and artificial neural network

Gait phases are important to evaluate the walking function and to identify the characteristics of pathological gaits. However, it is difficult to differentiate gait phases outside gait laboratories, thus, this study aimed to develop a method to detect 8 gait sub-phases using a wearable multiple sensor system and artificial neural network (ANN). Motion sensors were used to acquire the acceleration of lower limbs, and force sensitive resistors were used to detect contact state and force between the foot and the ground. Walking was recorded using a high-speed camera. Two feed forward back-propagation (BP) neural networks were developed. The resilient BP algorithm was used to train ANN. A total of 66 volunteers participated in this study. For the stance and swing phase detection, simulation of the training data showed an accuracy of 98.0 ​%. The data from the test set showed a recognition accuracy of 97.75 ​%. Because the ending point of the last phase ‘Terminal Swing’ is always 100 ​% GC, we only listed seven phases. The prediction accuracy of seven phases were: 35.9 ​%, 63.8 ​%, 93.6 ​%, 94.9 ​%, 94.8 ​%, 97.9 ​% and 98 ​% using the limb acceleration data only. The average accuracy for seven phases were 68 ​%, 91.3 ​%, 97.8 ​%, 98.9 ​%, 98.8 ​%, 99.1 ​%, and 99.5 ​% using the limb acceleration and foot pressure data for fast, normal, and slow gait speeds. This study provides a new method for eight gait sub-phases detection with high accuracy combining a wearable system and ANN, which may make gait phase analysis possible under free-living conditions.

Knee and ankle disorders during functional gait in mucopolysaccharidosis type VI

ABSTRACT To evaluate knee and ankle disorders during functional gait assessment in individuals with Mucopolysaccharidosis type VI (MPS VI). 19 subjects were included in this cross-sectional study and allocated in three groups according to age: Children Group (n=11); Adolescent Group (n=4); and Adult Group (n=4). Subphases of one gait cycle were analyzed: Initial contact; Medium support, and Pre-Balance. All volunteers with MPS VI presented greater knee and ankle flexion angles, in all gait subphases, when compared to the normal values defined by literature (p&lt;0.05). Initial contact subphase: knee flexion angle ranging from 8.5º to 15º; Ankle = Child Group −23.73º ± 8.53º; Adolescent Group = −25º ± 11.22º; Adult Group = −27.75º ± 3.3º. Medium support subphase: Knee = Child Group 19.64º ± 10.47º; Adolescent Group 16.75º ± 10.34º; Adult Group = 21.25º ± 12.84º. Ankle = Child Group −18.82º ± 8.91º ± 8.53º; Adolescent Group = −16.5º ± 9.33º; Adult Group = −22.25º ± 4.19º. Pre-Balance subphase: Knee = Child Group 22.72º ± 13.49º; Adolescent Group 21.25º ± 7.97º; Adult Group = 27º ± 16.27º. Ankle = Child Group -15º ± 9.76º; Adolescent Group = −15.75º ± 5.31º; Adult Group = −14.75º ± 3.86º. In this study, MPS VI individuals presented hyperflexion of knee and ankle as the main joint disorders during functional gait, regardless of age.

Front crawl stroke in swimming: Phase durations and self-similarity

Human movements, such as walking and running, are able to generate rhythmic motor patterns, with the consequent appearance of hidden time-harmonic structures. Such harmonic structures are represented (at comfortable speed) by the occurrence of the golden ratio as ratio of durations of specific walking and running gait sub-phases. Preliminary experimental evidences suggest that front crawl swimming may behave, under this point of view, like walking and running. This paper aims to demonstrate that a mathematical connection between the golden ratio and the front crawl swimming stroke actually exists, at a pace that plays the role of the comfortable speed in walking and running. Generalized Fibonacci sequences are used to this purpose. They rely on the durations of aggregate phases of the front crawl swimming stroke with a clear physical meaning, while characterizing self-similarity of front crawl strokes in its simple nature and enhanced (stronger) variant. Experimental data on front crawl swimmers illustrate the theoretical derivations, suggesting that the pace playing the role of the comfortable speed in walking and running is the middle/long-distance one, while showing that the self-similarity level increases with the swimming technique and the enhanced self-similarity is associated with the performance of top-level swimmers.

Impact of foot modeling on the quantification of the effect of total ankle replacement: A pilot study

BackgroundKinematic and kinetic foot models showed that computing ankle joint angles, moments and power with a one-segment foot modeling approach alters kinematics and tends to overestimate ankle joint power. Nevertheless, gait studies continue to implement one-segment foot models to assess the effect of total ankle replacement. Research questionThe objective of this pilot study was to investigate the effect of the foot modeling approach (one-segment versus multi-segment) on how total ankle replacement is estimated to benefit or degrade the patient’s biomechanical performance. MethodsTen subjects with post-traumatic ankle osteoarthritis scheduled for total ankle replacement and 10 asymptomatic subjects were recruited. A one-segment and a multi-segment foot model were used to calculate intrinsic foot joints kinematics and kinetics during gait. A linear mixed model was used to investigate the effect of the foot model on ankle joint kinematic and kinetic analysis and the effect of total ankle replacement. ResultsDifferences in range of motion due to the foot model effect were significant for all the gait subphases of interest except for midstance. Peak power generation was significantly overestimated when computed with the one-segment foot model. Ankle and shank-calcaneus joint dorsi-/plantarflexion range of motion did not increase post-operatively except during the loading response phase. A significant ‘group’ effect was found for stance and pre-swing phase range of motion, with total ankle replacement patients showing lower range of motion values than controls for dorsi/plantarflexion. SignificanceThe outcome of this study showed that the ‘foot model’ had a significant effect on estimates of range of motion and power generation. The findings in our study therefore emphasize the clinical interest of multi-segment foot modeling when assessing the outcome of a therapeutic intervention.

Investigation the effects of reading-tasking on gait parameters in healthy children

Kinematic and kinetic foot models showed that computing ankle joint angles, moments and power with a one-segment foot modeling approach alters kinematics and tends to overestimate ankle joint power. Nevertheless, gait studies continue to implement one-segment foot models to assess the effect of total ankle replacement.The objective of this pilot study was to investigate the effect of the foot modeling approach (one-segment versus multi-segment) on how total ankle replacement is estimated to benefit or degrade the patient’s biomechanical performance.Ten subjects with post-traumatic ankle osteoarthritis scheduled for total ankle replacement and 10 asymptomatic subjects were recruited. A one-segment and a multi-segment foot model were used to calculate intrinsic foot joints kinematics and kinetics during gait. A linear mixed model was used to investigate the effect of the foot model on ankle joint kinematic and kinetic analysis and the effect of total ankle replacement.Differences in range of motion due to the foot model effect were significant for all the gait subphases of interest except for midstance. Peak power generation was significantly overestimated when computed with the one-segment foot model. Ankle and shank-calcaneus joint dorsi-/plantarflexion range of motion did not increase post-operatively except during the loading response phase. A significant ‘group’ effect was found for stance and pre-swing phase range of motion, with total ankle replacement patients showing lower range of motion values than controls for dorsi/plantarflexion.The outcome of this study showed that the ‘foot model’ had a significant effect on estimates of range of motion and power generation. The findings in our study therefore emphasize the clinical interest of multi-segment foot modeling when assessing the outcome of a therapeutic intervention.

Lesser magnitudes of lower extremity variability during terminal swing characterizes walking patterns in children with autism

BackgroundAnecdotally, children with Autism Spectrum Disorder have highly variable lower extremity walking patterns, yet, this has not been sufficiently quantified. As such, the purpose of this study was to examine walking pattern variability by way of lower extremity coordination and spatio-temporal characteristics in children with autism compared with individuals with typical development during over-ground walking. MethodsBilateral continuous relative phase variability was computed for the thigh-leg, leg-foot, and thigh-foot segment couples for 11 children with autism and 9 children with typical development at each gait sub-phase. Furthermore, left and right stride lengths and stride width were computed and compared. The Model Statistic was utilized to test for statistical differences in variability between each child with autism to an aggregate group with typical development. Effect sizes were computed to determine the meaningfulness between responses for children with autism and typical development. Coefficient of variation and effect sizes were computed for stride lengths and stride width. FindingsAnalysis revealed that children with autism exhibited differences in variability in each gait sub-phase. Notably, all but two children with autism exhibited lesser variability in all segment couples during terminal swing. Differences in stride lengths were relatively minimal, however, greater coefficient of variation magnitudes in stride width were observed in children with autism. InterpretationThis finding reveals that children with autism may have limited or a preferred movement strategy when preparing the foot for ground contact. The findings from this study suggest variability may be an identifiable characteristic during movement in children with autism.

Modular motor control of the sound limb in gait of people with trans-femoral amputation

BackgroundThe above-knee amputation of a lower limb is a severe impairment that affects significantly the ability to walk; considering this, a complex adaptation strategy at the neuromuscular level is needed in order to be able to move safely with a prosthetic knee. In literature, it has been demonstrated that muscle activity during walking can be described via the activation of a small set of muscle synergies. The analysis of the composition and the time activation profiles of such synergies have been found to be a valid tool for the description of the motor control schemes in pathological subjects.MethodsIn this study, we used muscle synergy analysis techniques to characterize the differences in the modular motor control schemes between a population of 14 people with trans-femoral amputation and 12 healthy subjects walking at two different (slow and normal self-selected) speeds. Muscle synergies were extracted from a 12 lower-limb muscles sEMG recording via non-negative matrix factorization. Equivalence of the synergy vectors was quantified by a cross-validation procedure, while differences in terms of time activation coefficients were evaluated through the analysis of the activity in the different gait sub-phases.ResultsFour synergies were able to reconstruct the muscle activity in all subjects. The spatial component of the synergy vectors did not change in all the analysed populations, while differences were present in the activity during the sound limb’s stance phase. Main features of people with trans-femoral amputation’s muscle synergy recruitment are a prolonged activation of the module composed of calf muscles and an additional activity of the hamstrings’ module before and after the prosthetic heel strike.ConclusionsSynergy-based results highlight how, although the complexity and the spatial organization of motor control schemes are the same found in healthy subjects, substantial differences are present in the synergies’ recruitment of people with trans femoral amputation. In particular, the most critical task during the gait cycle is the weight transfer from the sound limb to the prosthetic one. Future studies will integrate these results with the dynamics of movement, aiming to a complete neuro-mechanical characterization of people with trans-femoral amputation’s walking strategies that can be used to improve the rehabilitation therapies.

A Low-Cost End-to-End sEMG-Based Gait Sub-Phase Recognition System.

As surface electromyogram (sEMG) signals have the ability to detect human movement intention, they are commonly used to be control inputs. However, gait sub-phase classification typically requires monotonous manual labeling process, and commercial sEMG acquisition devices are quite bulky and expensive, thus current sEMG-based gait sub-phase recognition systems are complex and have poor portability. This study presents a low-cost but effective end-to-end sEMG-based gait sub-phase recognition system, which contains a wireless multi-channel signal acquisition device simultaneously collecting sEMG of thigh muscles and plantar pressure signals, and a novel neural network-based sEMG signal classifier combining long-short term memory (LSTM) with multilayer perceptron (MLP). We evaluated the system with subjects walking under five conditions: flat terrain at 5 km/h, flat terrain at 3 km/h, 20 kg backpack at 5 km/h, 20 kg shoulder bag at 5 km/h and 15° slope at 5 km/h. Experimental results show that the proposed method achieved average classification accuracies of 94.10%, 87.25%, 90.71%, 94.02%, and 87.87%, respectively, which were significantly higher than existing recognition methods. Additionally, the proposed system had a good real-time performance with low average inference time in the range of 3.25 ~ 3.31 ms.

Adaptive control of an actuated ankle foot orthosis for paretic patients

This paper deals with the control of an active ankle foot orthosis (AAFO) to assist the gait of paretic patients. The AAFO system is driven by both, the residual human torque delivered by the muscles spanning the ankle joint and the AAFO’s actuator’s torque. A model reference adaptive control is proposed to assist dorsiflexion and plantar-flexion movements of the ankle joint during level walking. Unlike most classical model-based controllers, the proposed one does not require any prior estimation of the system’s (AAFO-wearer) parameters. The ankle reference trajectory is updated online based on the main gait cycle events and is adapted with respect to the self-selected speed of the wearer. The adaptive desired ankle trajectory is estimated using cubic spline interpolations between the different key events of the gait cycle. The closed-loop input-to-state stability of the AAFO-wearer system with respect to a bounded human muscular torque is proved by a Lyapunov analysis. Experimental results obtained from three healthy subjects and one paretic patient, show satisfactory results in terms of tracking performance and ankle assistance throughout the full gait cycle. The experiments also show good performance at different walking speeds and with different gait sub-phase duration proportions.

SEMG-signal and IMU sensor-based gait sub-phase detection and prediction using a user-adaptive classifier

This paper presents a gait sub-phase detection and prediction approach using surface electromyogram (sEMG) signals, pressure sensors, and the knee angle for a lower-limb power-assist robot. Pattern recognition and machine learning models using sEMG signals have several inherent problems for gait sub-phase detection. These problems are due to recognition delay, lack of consideration for the unique characteristics of sEMG signals based on the subject, and meaningless features. To solve these problems, we propose a new labeling technique based on the heel and toe, a muscle and feature selection, a user-adaptive classifier using a weighted voting technique to achieve gait sub-phase detection, and a gait sub-phase prediction technique using interpolation. Experimental results show that the average accuracies of the proposed labeling, the muscle and feature selection, and the user-adaptive classifier using weighted voting are 7%, 12%, and 17% better, respectively, than the existing methods using physical sensors. Results also show that the average prediction time of the proposed method is 80% faster than the existing methods.

Altered muscle activation patterns (AMAP): an analytical tool to compare muscle activity patterns of hemiparetic gait with a normative profile

BackgroundStroke survivors often have lower extremity sensorimotor impairments, resulting in an inability to sufficiently recruit muscle activity at appropriate times in a gait cycle. Currently there is a lack of a standardized method that allows comparison of muscle activation in hemiparetic gait post-stroke to a normative profile.MethodsWe developed a new tool to quantify altered muscle activation patterns (AMAP). AMAP accounts for spatiotemporal asymmetries in stroke gait by evaluating the deviations of muscle activation specific to each gait sub-phase. It also recognizes the characteristic variability within the healthy population. The inter-individual variability of normal electromyography (EMG) patterns within some sub-phases of the gait cycle is larger compared to others, therefore AMAP penalizes more for deviations in a gait sub-phase with a constant profile (absolute active or inactive) vs variable profile. EMG data were collected during treadmill walking, from eight leg muscles of 34 stroke survivors at self-selected speeds and 20 healthy controls at four different speeds. Stroke survivors’ AMAP scores, for timing and amplitude variations, were computed in comparison to healthy controls walking at speeds matched to the stroke survivors’ self-selected speeds.ResultsAltered EMG patterns in the stroke population quantified using AMAP agree with the previously reported EMG alterations in stroke gait that were identified using qualitative methods. We defined scores ranging between ±2.57 as “normal”. Only 9% of healthy controls were outside “normal” window for timing and amplitude. Percentages of stroke subjects outside the “normal” window for each muscle were, Soleus = 79%; 73%, Medial Gastrocnemius = 62%; 79%, Tibialis Anterior = 62%; 59%, and Gluteus Medius = 48%; 51% for amplitude and timing component respectively, alterations were relatively smaller for the other four muscles. Paretic-propulsion was negatively correlated to AMAP scores for the timing component of Soleus. Stroke survivors’ self-selected walking speed was negatively correlated with AMAP scores for amplitude and timing of Soleus but only amplitude of Medial gastrocnemius (p < 0.05).ConclusionsOur results validate the ability of AMAP to identify alterations in the EMG patterns within the stroke population and its potential to be used to identify the gait phases that may require more attention when developing an optimal gait training paradigm.Trial registrationClinicalTrials.govNCT00712179, Registered July 3rd 2008