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.     

Functional asymmetries in early learning during right,left, and bimanual performance in right‐handed subjects

Purpose:

To elucidate differences in activity and connectivity during early learning due to the performing hand.

Materials and Methods:

Twenty right‐handed subjects were recruited. The neural correlates of explicit visuospatial learning executed with the right, the left hand, and bimanually were investigated using functional magnetic resonance imaging. Connectivity analyses were carried out using the psychophysiological interactions model, considering right and left anterior putamen as index regions.

Results:

A common neural network was found for the three tasks during learning. Main activity increases were located in posterior cingulate cortex, supplementary motor area, parietal cortex, anterior putamen, and cerebellum (IV–V), whereas activity decrements were observed in prefrontal regions. However, the left hand task showed a greater recruitment of left hippocampal areas when compared with the other tasks. In addition, enhanced connectivity between the right anterior putamen and motor cortical and cerebellar regions was found for the left hand when compared with the right hand task.

Conclusion:

An additional recruitment of brain regions and increased striato‐cortical and striato‐cerebellar functional connections is needed when early learning is performed with the nondominant hand. In addition, access to brain resources during learning may be directed by the dominant hand in the bimanual task. J. Magn. Reson. Imaging 2013;37:619–631. © 2012 Wiley Periodicals, Inc.     

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.     

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting.     

Association of varus thrust with prevalent patellofemoral osteoarthritis: A cross-sectional study

This cross-sectional study investigated (i) the association of varus thrust during gait with the presence of patellofemoral osteoarthritis (PFOA) in patients with medial knee osteoarthritis (OA) and (ii) patellar alignment in the knees with varus thrust. Participants from orthopedic clinics (n = 171; mean age, 73.4 years; 71.9% female) diagnosed with radiographic medial knee OA (Kellgren/Lawrence [K/L] grade ≥1) were included in this study, and underwent gait observation for varus thrust assessment using 2D video analysis. A radiographic skyline view was used to assess the presence of medial PFOA using the grading system from the Osteoarthritis Research Society International Atlas. The tibiofemoral joint K/L grade, patellar alignment (i.e., lateral shift and tilting angle), and knee pain intensity were also evaluated as covariates. Thirty-two (18.7%) of 171 patients exhibited varus thrust and they presented significantly higher knee pain (46.0 ± 3.04 mm vs. 32.4 ± 2.73 mm; P = 0.024), a lower patellar tilting angle (P = 0.024), and a higher prevalence of PFOA compared with those without varus thrust. A logistic regression analysis with adjustment of covariates showed that varus thrust was significantly associated with higher odds of the presence of mixed and medial PFOA, and trended to significantly associate with any PFOA, including lateral PFOA. This indicates that varus thrust was associated with PFOA in a compartment-nonspecific manner in patients with medial knee OA. Varus thrust may represent a clinical disease feature of more advanced and multicompartmental disease.     

The comparison of ground reaction forces and lower limb muscles correlation and activation time delay between forward and backward walking

This study aimed to compare the ground reaction forces (GRF) and lower limb muscles correlation and activation time delay between Forward (FW) and Backward (BW) walking. Twenty-four male students participated in this research. Electromyogram activities of gluteus medius, biceps femoris, medial gastrocnemius, soleus and anterior tibialis muscles along with GRFs were measured. Each participant performed two FW and two BW trials bare foot. Statistical parametric mapping (SPM) analysis was performed over anterior-posterior and vertical GRFs time series. The paired t-test was used in SPM analysis. Cross-correlation analysis compared similarity in shape and time delay of EMG pattern. SPM analysis of GRFs showed that these two walking modes have asymmetrical kinetic behavior during most parts of stance phase. Based on cross-correlation analysis, the shape of EMG activation profiles differed, where a phase shift in the muscle activation pattern of approximately 60% occurred. This shift may indicate different control mechanisms, at the spinal level, underpin FW and BW walking modalities.