Attempted symmetry affects dynamic gait stability in individuals with lower-limb amputation.

BACKGROUND: Gait in people with lower limb amputation (LLA) is typically asymmetrical. Reducing this asymmetry is often attempted to minimise the impact of secondary health issues. However, temporal-spatial asymmetry in gait of people with LLA has also been shown to underpin dynamic stability. RESEARCH QUESTION: The current study aimed to identify the effects of acute attempts to achieve temporal-spatial symmetry on the dynamic stability of people with unilateral transtibial amputation (UTA). The secondary aim of this study was to identify the corresponding biomechanical adaptations during attempted symmetrical gait. METHODS: Eleven people with UTA walked along a 15 m walkway in four different conditions: normal (NORM), attempted symmetrical step length and step frequency (SYMSL+SF) attempted symmetrical step length (SYMSL) and attempted symmetrical step frequency (SYMSF). Dynamic stability was measured using the backward (BW) and medio-lateral (ML) margins of stability (MoS). RESULTS: Results indicate that attempting SYMSF had a positive effect on gait stability in BW and ML directions, while attempting SYMSL had a potentially negative effect, although these results did not appear to be significant. The absence of clustering in principal component analysis, supported the lack of significant results, indicating no features differentiating between conditions of attempted symmetry. Conversely, there was clustering by limbs which were associated with differences in knee and ankle joint angles between the prosthetic and non-prosthetic limbs, and clustering by individuals highlighting the importance of patient-specific analysis. CONCLUSION: The data suggests that attempted symmetrical gait reduces asymmetry but also affects dynamic stability.

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.

Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category's cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.

The Effect of Cognitive Task Complexity on Healthy Gait in the Walking Corsi Test.

Dual-task activities are essential within everyday life, requiring visual-spatial memory (VSM) and mobility skills. Navigational memory is an important component of VSM needed to carry out everyday activities, but this is often not included in traditional tests such as the Corsi block tapping test (CBT). The Walking Corsi Test (WalCT) allows both VSM and navigational memory to be tested together, as well as allowing measures of gait to be collected, thus providing a more complete understanding of dual-task function. The aim of this study was to investigate the effect of an increasingly complex cognitive task on gait in a healthy adult population, using the WalCT and body-worn inertial measurement unit (IMU) sensors. Participants completed both the CBT and WalCT, where they were asked to replicate increasingly complex sequences until they were no longer able to carry this out correctly. IMU sensors were worn on the shins throughout the WalCT to assess changes in gait as task complexity increased. Results showed that there were significant differences in several gait parameters between completing a relatively simple cognitive task and completing a complex task. The type of memory used also appeared to have an impact on some gait variables. This indicates that even within a healthy population, gait is affected by cognitive task complexity, which may limit function in everyday dual-task activities.

Exploring the interaction of knee and ankle component use on mobility test performance in people with unilateral transfemoral amputation.

BACKGROUND: Ankle-foot and knee components are important determinants of mobility for individuals with transfemoral amputation. Individually, advanced ankle-foot and knee components have been shown to benefit mobility in this group of people. However, it is not clear what effect a variety of combinations of ankle-foot and knee components have on mobility test performance. OBJECTIVES: To assess whether outcomes from mobility tests in people with unilateral transfemoral amputation are influenced by varying combinations of ankle-foot and knee components. STUDY DESIGNS: Repeated measures. METHODS: Nine adults with unilateral transfemoral amputation completed the two-minute walk test, the timed up-and-go test, the L-test, and a custom locomotion course in four randomized prosthetic conditions. These conditions were each a combination of an ankle-foot component (rigid, nonarticulating [RIG] or hydraulically articulating [HYD]) and a knee component (non-microprocessor-controlled [NMPK] or microprocessor-controlled [MPK]). The test-retest reliability and concurrent validity of the custom locomotion course were also established. RESULTS: The best performance in all mobility tests was associated with the MPK + HYD combination, followed by the MPK + RIG, NMPK + HYD, and NMPK + RIG combinations. This effect was statistically significant for the two-minute walk test (P = 0.01, = 0.36) and on threshold for the L-test (P = 0.05, = 0.36), but not statistically significant for the locomotion course (P = 0.07, = 0.38) or the timed up-and-go test (P = 0.12, = 0.22). Locomotion course performance had good to excellent test-retest reliability and strong concurrent validity. CONCLUSION: Using a combination of a HYD ankle-foot and a MPK knee resulted in the highest performance in mobility tests. This was observed in contrast to combinations of prosthetic components that included a rigid ankle-foot component and/or a NMPK knee component.

Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses.

Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg-1) and joint powers (W.kg-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors' knowledge, this is the first study to optimise the development of a machine learning algorithm.