Dynamic instability during post-stroke hemiparetic walking

Falls and fall-related injuries cause extremely costly and potentially fatal health problems in people post-stroke. However, there is no global indicator of walking instability for detecting which individuals will have increased risk of falls. The purposes of this study were to directly quantify walking stability in stroke survivors and neurologically intact controls and to determine which stability measures would reveal the changes in walking stability following stroke. This study thus provided an initial step to establish objective measures for identifying potential fallers. Nine post-stroke individuals and nine controls walked on a treadmill at four different speeds. We computed short-term local divergence exponent (LDE) and maximum Floquet multiplier (maxFM) of the trunk motion, average and variability of dynamic margins of stability (MOS) and step spatiotemporal measures. Post-stroke individuals demonstrated larger short-term LDE (p = 0.002) and maxFM (p = 0.041) in the mediolateral (ML) direction compared to the controls but remained orbitally stable (maxFM < 1). In addition, post-stroke individuals walked with greater average step width (p = 0.003) but similar average ML MOS (p = 0.154) compared to the controls. Post-stroke individuals also exhibited greater variability in all MOS and step measures (all p < 0.005). Our findings indicate that post-stroke individuals walked with greater local and orbital instability and gait variability than neurologically intact controls. The results suggest that short-term LDE of ML trunk motion and the variability of MOS and step spatiotemporal measures detect the changes in walking stability associated with stroke. These stability measures may have the potential for identifying those post-stroke individuals at increased risk of falls.     

Time-integrated propulsive and braking impulses do not depend on walking speed

BackgroundEnhancing propulsion during walking is often a focus in physical therapy for those with impaired gait. However, there is no consensus in the literature for assessing braking and propulsion. Both are typically measured from the anterior-posterior ground reaction force (AP-GRF). While normalization of AP-GRF force by bodyweight is commonly done in the analysis, different methods for AP-GRF time axis normalization are used.Research questionDoes walking speed affect propulsion and/or braking, and how do different methods for calculating propulsion and braking impact the conclusion, in both healthy adults and those with lower limb impairment?MethodsWe investigated three different analysis methods for assessing propulsion. 1. BW-TimeIntegration: Bodyweight (BW) normalized time integration of AP-GRF (units of BWs). 2. BW-%StanceIntegration: BW normalized AP-GRF is resampled to percent stance phase prior to integration (units of BW%Stance). 3. BW-Peak: BW normalized peak force (units of BW). We applied these methods to two data sets. One data set included AP-GRFs from trials of slow, self-selected, and fast walking speeds for 203 healthy controls (HCs); a second data set included subjects with lower limb orthopedic injuries.ResultsUsing the BW-TimeIntegration method, we found no effect of walking speed on propulsion for HCs. Time integration over the longer stance phase of slower walking balanced the lower magnitude AP-GRFs of slower walking, resulting in a time-integrated impulse that was the same regardless of walking speed. In contrast, the other two methods that are not time integration methods found that propulsion increased with walking speed. Similarly, in the gait pathology data set, differences in results were found depending on the analysis method used.SignificanceFor many gait studies concerning propulsion and/or braking, the impulse measure used should be related to the body’s change of momentum, necessitating an analysis method with a time integration of the AP-GRF.     

Leaving hip rotation out of a conventional 3D gait model improves discrimination of pathological gait in cerebral palsy: A novel neural network analysis

BackgroundComplex clinical gait analysis results can be expressed as single number gait deviations by applying multivariate processing methods. The original Movement Deviation Profile (MDP) quantifies the deviation of abnormal gait using the most trusted nine dynamic joint angles of lower limbs.Research questionWhich subset of joint angles maximises the ability of the MDP to separate abnormal gait from normality? What is the effect of using the best subset in a large group of patients, and in individuals?MethodsA self-organising neural network was trained using normal gait data from 166 controls, and then the MDP of 1923 patients with cerebral palsy (3846 legs) was calculated. The same procedure was repeated with 511 combinations of the nine joint angles. The standardised distances of abnormal gait from normality were then calculated as log-transformed Z-scores to select the best combination. A mixed design ANOVA was used to assess how removing the least discriminating angle improved the separation of patients from controls. The effect of using the optimal subset of angles was also quantified for each individual leg by comparing the change in MDP to the independent FAQ levels of patients.ResultsRemoval of hip rotation significantly (p<0.0005) increased the separation of the patient group from normality (ΔZ-score 0.24) and also at FAQ levels 7-10 (ΔZ-score 0.38, 0.27, 0.22, 0.14). The MDP of individual patients changed in a wider range of -4.65 to 1.12 Z-scores and their change matched their independent FAQ scores, with less functional patients moving further from, and more functional patients moving closer to normality.SignificanceIn existing gait databases we recommend excluding hip rotation from data used to calculate the MDP. Alternatively, the calculation of hip rotation can be improved by post-hoc correction, but the ultimate solution is to use more accurate and reliable models of hip rotation.     

Prosthetic restoration of the forefoot lever after Chopart amputation and its consequences onto the limb during gait

BackgroundIn subjects with Chopart amputation the foot lever is clearly diminished. Usually high or low profile prostheses are routinely utilized to re-establish the lost forefoot lever.Research questionThe aim of this study was to investigate to what extent the proposed prostheses were able to replace the forefoot lever in chopart-amputees.MethodsAn instrumented 3D gait analysis, including plantar and socket pressure measurements, was performed in thirteen subjects with Chopart amputation using a clamshell and/or a Bellmann prosthesis including an ankle foot orthosis during level ground walking.ResultsThe largest range of motion (p < 0.05) in the ankle joint was seen for the Bellmann prosthesis (32 ± 3°) followed by the Bellmann prosthesis with ankle-foot orthosis (22 ± 6°) whereas in the clamshell prosthesis (10 ± 4°) almost no ankle motion was seen. Conversely, the highest ankle joint moment (p < 0.05) was seen for the clamshell prosthesis (1.04 ± 0.24Nm/kg) followed by the Bellmann prosthesis with ankle-foot orthosis (0.66 ± 0.14Nm/kg) and, finally, the Bellmann (0.37 ± 0.11Nm/kg) alone offering the lowest joint moment.ConclusionHigh-profile prostheses with ventral shell are more suitable to reacquire the lost forefoot lever after Chopart amputation. However, the issue of restricted range of motion in the ankle joint with the clamshell prosthesis needs to be addressed.     

Two-minute walk tests demonstrate similar age-related gait differences as a six-minute walk test

BackgroundThe six-minute walk test (6MWT) is used within clinical and research settings to assess gait performance across a variety of conditions and populations. Commonly, the test is used to identify specific aspects of gait that affect functional mobility. With the advancement of new technologies such as wireless inertial sensors, it has become possible to collect reliable, sensitive, and objective measures of gait. While the 6MWT has been accepted and used for many years, a more concise, while still objective gait analysis would likely benefit clinicians, researchers and patients.Research QuestionDoes a concise 2-minute walk test (2MWT) provide similar information regarding gait performance and gait differences as the 6MWT in healthy young (YA) and older adults (OA)?MethodsA total of thirty-one participants (sixteen young adults and fifteen older adults) conducted a continuous 6MWT at their self-selected pace. All participants wore six wireless inertial sensors which were placed on each foot, at the lumbar, sternum, and on each wrist. Once completed the 6MWT data was spliced into three, distinct two-minute segments. Spliced data was analyzed and compared between groups and segments.ResultsResults demonstrate significant age-related differences in several gait metrics, primarily with older adults showing increased spatiotemporal variability. Additionally, no significant differences were observed between the three, two-minute segments and the continuous 6MWT, with the exception of total number of strides completed.SignificanceThese results demonstrate that the 2MWT may provide a preferable alternative to assessing gait performance by reducing confounds such as fatigue while maintaining sensitivity of measuring gait performance. These improvements may be particularly beneficial when studying populations of advanced age or with neurological disorder.     

Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach

BackgroundGait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability.Research questionCan a structured machine learning approach achieve a more accurate prediction of running gait event timings from tibial accelerometry, compared to the previously utilised heuristic approaches?MethodsForce-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. 3D tibial acceleration and ground reaction force data from 93 rearfoot runners were captured. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals.ResultsBoth a structured perceptron model (median absolute error of stance time estimation: 10.00 ± 8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 ± 5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 ± 9.52 ms). Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running.SignificanceThe machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory.     

Automatic classification of gait in children with early-onset ataxia or developmental coordination disorder and controls using inertial sensors

Early-Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two conditions that affect coordination in children. Phenotypic identification of impaired coordination plays an important role in their diagnosis. Gait is one of the tests included in rating scales that can be used to assess motor coordination.A practical problem is that the resemblance between EOA and DCD symptoms can hamper their diagnosis. In this study we employed inertial sensors and a supervised classifier to obtain an automatic classification of the condition of participants. Data from shank and waist mounted inertial measurement units were used to extract features during gait in children diagnosed with EOA or DCD and age-matched controls. We defined a set of features from the recorded signals and we obtained the optimal features for classification using a backward sequential approach. We correctly classified 80.0%, 85.7%, and 70.0% of the control, DCD and EOA children, respectively. Overall, the automatic classifier correctly classified 78.4% of the participants, which is slightly better than the phenotypic assessment of gait by two pediatric neurologists (73.0%). These results demonstrate that automatic classification employing signals from inertial sensors obtained during gait maybe used as a support tool in the differential diagnosis of EOA and DCD. Furthermore, future extension of the classifier’s test domains may help to further improve the diagnostic accuracy of pediatric coordination impairment. In this sense, this study may provide a first step towards incorporating a clinically objective and viable biomarker for identification of EOA and DCD.     

Subthalamic nucleus stimulation and gait in Parkinson’s Disease: a not always fruitful relationship

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) provides efficient treatment for the alleviation of motor signs in patients with advanced Parkinson’s disease (PD), but its specific effects on gait is sometimes less successful as it may even lead to an aggravation of freezing of gait. To better understand when axial symptoms can be expected to improve and when they may worsen or be resistant to STN-DBS, we propose here a narrative review that considers the recent literature evidences based on instrumental gait analysis data. Our aim is to report about the efficacy of STN-DBS on PD gait, analyzing the clinical and procedural factors involved, and discussing the strategies for optimizing such effectiveness in patients with advanced PD.