9 research outputs found
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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
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Assessment and understanding of unilateral trans-tibial amputee gait using principal component analysis and discriminant function analysis
The general aim of this thesis was to develop analytical techniques for the assessment and understanding of lower-limb amputee (LLA) gait. The number of individuals with lower limb amputation (LLA) worldwide is growing and being able to optimise rehabilitation and prosthetic prescriptions are becoming more important. Gait analysis may be able to inform these processes, in particular at the individual level.
In study one, a machine learning algorithm was developed and optimised using Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA) to distinguish between barefoot and shod running. An iterative process was used to optimise the algorithm, exploring all possible iterations of ten individuals out of twenty, finding the combination of people with the greatest generic features and thus the lowest error rate for classification. The outcome showed 93.5% classification accuracy between barefoot and shod running. This study demonstrated that an iteration procedure could optimise a machine learning algorithm to overcome the issues of overfitting, which is particularly useful when working with a small sample size as is common in gait analysis.
In study two, PCA and DFA were used to identify differences between the gait of individuals with unilateral trans-tibial amputation (UTTA) and able-bodied individuals. Different approaches were explored, establishing that PCA conducted on normalised temporal-waveforms yielded the best outcome. Results revealed that UTTA and able-bodied gait differed with regards to certain biomechanical variables, providing a better understanding of LLA function. Although differences between individuals with LLA and able-bodied individuals have previously been investigated, this study demonstrates that using multivariate statistical analyses a vast number of variables can be investigated simultaneously, identifying the hierarchy of variables and thus which need to be targeted during treatment.
Clinical diagnosis is based on individual patients, thus in study three PCA was used to determine whether one individual with a UTTA displayed unique gait characteristics when compared to a group of able-bodied individuals. Both covariance and correlation matrices were used during PCA, providing information about variation and magnitude of the data, respectively. Results revealed that each individual with UTTA has subject-specific gait characteristics, which highlights that this method can be used to identify variables which can be targeted during treatment.
In the fourth and final study, PCA was used to understand the effects of attempted symmetry on dynamic stability of individuals with UTTA. Although in rehabilitation symmetrical gait is often sought for since asymmetrical gait is said to cause long term adverse effects, results revealed that asymmetry might be playing a functional role and in fact aids better stability in UTTA gait. This outcome may suggest that after a certain symmetry has been reached, the target of rehabilitation may need to be reconsidered to aim for better stability.
In conclusion, multivariate statistical analysis could be used to assess and understand LLA function. In a clinical setting, the ability to identify important variables during a task, particularly at patient-specific level has the potential to improve the development of treatment recommendations. Prosthetic prescription and rehabilitation processes can be tailored and in turn the outcome may be more successful which could increase the likelihood of independent living of patients and therefore improve the quality of life of individuals with LLA
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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
Individuals with unilateral transtibial amputation and lower activity levels walk more quickly when using a hydraulically articulating versus rigidly attached prosthetic ankle-foot device
Introduction: Prosthetic ankle-foot devices incorporating a hydraulic articulation between the pylon and prosthetic foot have been shown to be beneficial to the gait of more active individuals with unilateral transtibial amputation (UTA). However, the functional benefits of using hydraulic ankle-foot devices to less active individuals with UTA are yet to be determined. The aim of the current study was to investigate the effects on gait performance of using a non-ESR foot with a hydraulic attachment, compared to an identical, rigidly attached foot during overground walking in less active individuals with UTA.
Materials and Methods: Kinematic and kinetic data were recorded while five individuals with UTA, deemed K2 activity level by their prescribing physician, performed two-minute walk tests (2MWT) and ten overground gait trials, in two conditions; using a hydraulically articulating ankle foot device (HYD) and using a rigidly attached ankle foot device (RIG).
Results: Walking speed during the 2MWT was increased by 6.5% on average, in the HYD (1.07 m/s) condition, compared to the RIG (1.01 m/s) condition (Cohen’s d = 0.4). Participants displayed more symmetrical inter-limb loading (d = 0.8), increased minimum forward centre of pressure velocity (d = 0.8), increased peak shank rotational velocity (d = 1.0) and decreased prosthetic energy efficiency (d = 0.7) when using the HYD compared to RIG device.
Conclusions: Individuals with lower activity levels walk faster and therefore further when, using a foot with a hydraulically articulating attachment, in comparison to a rigid attachment. A reduced braking effect in early stance phase, as a result of the action of the hydraulic component present in the articulating attachment, partially explains the improvement in walking performance
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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, η2p = 0.36) and on threshold for the L-test (P = 0.05, η2p = 0.36), but not statistically significant for the locomotion course (P = 0.07, η2p = 0.38) or the timed up-and-go test (P = 0.12, η2p = 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