▼  There has been a growing interest in machine-based recognition of emotions from body gait and posture, and its combination with other modalities. Applications such as human computer interaction, social robotics, and security have been the driving force behind such trend. The majority of the previous work in automatic affect perception deploys only either local features or global features. Whilst a combination of both types of features are deployed in applications such as object recognition and facial recognition, the literature does not reveal any study in affect recognition from body language using combined global and local features. In this thesis, such gap is addressed by examining how deploying a combination of local and global features can improve the recognition rate in automatic classification of emotions using gait and posture. The motion data used in the study comprising kinematic parameters associated with the gait and posture of a number of actors expressing a set of emotions, were recorded electronically using an inertia motion capture system. A combination of local and global features proposed by Kapur et al. and Zacharatos et al., respectively, were used in the classification process using WEKA classification system. Additional global features of shape flow and shaping, horizontal and vertical symmetry were added to the combination feature set to increase the performance of the classifier. The results obtained in the analysis demonstrate that deploying a combination of local and global features leads to a more robust and reliable method for automatic affect recognition from body language as it improves accuracy across a range of classifiers. This research also demonstrates that the inclusion of the additional features, which represent additional Laban Movement Analysis components, increases the maximum classification accuracy from 88.5% to 92.3%. Achieving better automatic affect recognition rates can lead to increased application of the approach, improved usefulness and reliability of such systems.

▼ Human gait identification has become an active area of research due to increased security requirements. Human gait identification is a potential new tool for identifying individuals beyond traditional methods. The emergence of motion capture techniques provided a chance of high accuracy in identification because completely recorded gait information can be recorded compared with security cameras. The aim of this research was to build a practical method of gait identification and investigate the individual characteristics of gait. For this purpose, a gait identification approach was proposed, identification results were compared by different methods, and several studies about the individual characteristics of gait were performed. This research included the following: (1) a novel, effective set of gait features were proposed; (2) gait signatures were extracted by three different methods: statistical method, principal component analysis, and Fourier expansion method; (3) gait identification results were compared by these different methods; (4) two indicators were proposed to evaluate gait features for identification; (5) novel and clear definitions of gait phases and gait cycle were proposed; (6) gait features were investigated by gait phases; (7) principal component analysis and the fixing root method were used to elucidate which features were used to represent gait and why; (8) gait similarity was investigated; (9) gait attractiveness was investigated. This research proposed an efficient framework for identifying individuals from gait via a novel feature set based on 3D motion capture data. A novel evaluating method of gait signatures for identification was proposed. Three different gait signature extraction methods were applied and compared. The average identification rate was over 93%, with the best result close to 100%. This research also proposed a novel dividing method of gait phases, and the different appearances of gait features in eight gait phases were investigated. This research identified the similarities and asymmetric appearances between left body movement and right body movement in gait based on the proposed gait phase dividing method. This research also initiated an analysing method for gait features extraction by the fixing root method. A prediction model of gait attractiveness was built with reasonable accuracy by principal component analysis and linear regression of natural logarithm of parameters. A systematic relationship was observed between the motions of individual markers and the attractiveness ratings. The lower legs and feet were extracted as features of attractiveness by the fixing root method. As an extension of gait research, human seated motion was also investigated.

▼ A system that integrates the face, a physical biometric, with gait, a behavioral biometric, for automatically recognizing human beings effectively under a wider range of conditions than a classifier which exclusively employs only one of these biometrics, is proposed. A decision-level fusion approach is adopted where the top matches of the face classifier are passed on to the gait classifier which then determines the identity of the unknown person. For face recognition, a principle components analysis-based approach, as well as a Bayesian inference-based classifier is employed, while for gait recognition, a model-based strategy is implemented, which utilizes various gait features identified as being the most pertinent for recognition based on data collected using an optoelectronic motion capture system. The integrated system is found to outperform the individual face and gait classifiers that it is composed of, thus, demonstrating the potential of using the gait to supplement the face in scenarios where the face classifier alone does not perform well due to the non-availability of high resolution face data. During the course of this research, automated face recognition was also studied in detail, with a concentration on approaches that employ statistical dimensionality reduction techniques for this task. Experiments were conducted on some of the most widely-used methods in this category to test the recognition accuracy of these methods, and various combinations thereof, for different database sizes, images resolutions and number of bits per pixel. It was found that certain combinations of some of these techniques perform better than the individual methods that those combinations are comprised of when higher resolution face images are utilized. Furthermore, a system which utilizes different resolution versions of the whole face, as well as of various facial components, in a hierarchical manner was implemented and was found to achieve higher accuracy than its single-level counterpart which uses only the highest resolution images.

▼ Person re-identification based on appearance is challenging due to varying views and lighting conditions in different cameras, or when multiple persons wear similar clothing styles and color. Considering these challenges, gait patterns provide an alternative to appearance, as gait can be captured from a distance and at a low resolution. In this paper we investigate and evaluate running gait as a unique attribute for video person re-identification in a recreational long-distance running event with 257 participants. We show that running gait recognition achieves competitive performance compared to video-based approaches in the cross-camera retrieval task and that gait and appearance features are complementary to each other. In addition, we compare gait recognition applied to walking and running sequences. An important difference is that we walk with straight arms, but run with bent arms. We propose to use human semantic parsing to create partial gait silhouettes from body parts to find the most discriminative combination. We demonstrate that the arm and leg swing are the most discriminative parts of the running gait. Our proposed method provides better recognition results by removing the torso from the silhouettes and allowing the arm swing to be more visible. Advisors/Committee Members: van Gemert, J.C. (mentor), Pintea, S. (graduation committee), Verwer, S.E. (graduation committee), Napolean, Y. (mentor), Delft University of Technology (degree granting institution).

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Organizacione nauke-Informacione tehnologije / Organizational sciences-Information technology

Intenzivan razvoj informaciono-komunikacionih tehnologija otvorio je vrata primeni biometrijskih tehnologija u menadžmentu identiteta. Biometrijski modalitet koji ima veliki potencijal za primenu u praksi je ljudski hod. Njega odlikuju neinvazivnost i neintruzivnost. Ovakve osobine posebno pogoduju primeni u uslovima tehnologije prismotre. Zahvaljujući tome, ovaj biometrijski modalitet tokom prethodnih godina izaziva veliko interesovanje akademske zajednice. Ovo interesovanje rezultiralo je razvojem velikog broja pristupa za prepoznavanje osoba na osnovu hoda. Uprkos tome, primena biometrijskih tehnologija zasnovanih na ljudskom hodu u praksi i dalje zaostaje za dobro ustanovljenim modalitetima poput otiska prsta, lica ili glasa. Glavni razlog je nedostatak odgovarajućeg pristupa koji bi omogućio stabilnu primenu u realnim uslovima. Cilj ovog rada je predlog novog postupka za prepoznavanje osoba na osnovu hoda koji bi omogućio razvoj robusnog i pristupačnog biometrijskog sistema. Inicijalno, urađen je sveobuhvatan pregled oblasti i aktuelnih istraživanja na osnovu čega je predložen novi postupak. Predloženi postupak se zasniva na ideji da se sekvenca ljudskog hoda može predstaviti kao jedna nepomična 2D slika. Ovakav postupak omogućio bi da se za potrebe prepoznavanja primene generičke metode za pretragu slika na osnovu sadržaja. Na ovakav način problem bi bio prenet iz prostorno-vremenskog domena u prostorni domen, konkretno domen 2D nepomične slike, koji je poznat i u kome postoji veliki broj dokazanih rešenja. Za potrebe akvizicije, postupak se oslanja na novu tehnologiju iz oblasti interakcije čovek-računar, Microsoft Kinect. Na osnovu predloženog postupka razvijen je modularni laboratorijski prototip kao i okruženje za testiranje i evaluaciju. Naučna zasnovanost i opravdanost predloženog postupka proverena je nizom eksperimenata. Eksperimenti su organizovani na takav način da ispitaju različite faktore koji tokom primene postupka mogu uticati na konačne performanse u prepoznavanju. Na osnovu dobijenih rezultata može se zaključiti da predloženi postupak odlilkuje visok stepen robusnosti kao i visoka preciznost u prepoznavanju...

Advisors/Committee Members: Starčević, Dušan. 1949-.

▼ Human gait recognition is a behavioral biometrics method that aims to determine the identity of individuals through the manner and style of their distinctive walk. It is still a very challenging problem because natural human gait is affected by many covariate conditions such as changes in the clothing, variations in viewing angle, and changes in carrying condition. Although existing gait recognition methods perform well under a controlled environment where the gait is in normal condition with no covariate factors, the performance drastically decreases in practical conditions where it is susceptible to many covariate factors. In the first section of this dissertation, we analyze the most important features of gait under the carrying and clothing conditions. We find that the intra-class variations of the features that remain static during the gait cycle affect the recognition accuracy adversely. Thus, we introduce an effective and robust feature selection method based on the Gait Energy Image. The new gait representation is less sensitive to these covariate factors. We also propose an augmentation technique to overcome some of the problems associated with the intra-class gait fluctuations, as well as if the amount of the training data is relatively small. Finally, we use dictionary learning with sparse coding and Linear Discriminant Analysis (LDA) to seek the best discriminative data representation before feeding it to the Nearest Centroid classifier. When our method is applied on the large CASIA-B and OU-ISIR-B gait data sets, we are able to outperform existing gait methods. In addition, we propose a different method using deep learning to cope with a large number of covariate factors. We solve various gait recognition problems that assume the training data consist of diverse covariate conditions. Recently, machine learning based techniques have produced promising results for challenging classification problems. Since a deep convolutional neural network (CNN) is one of the most advanced machine learning techniques with the ability to approximate complex non-linear functions, we develop a specialized deep CNN architecture for gait recognition. The proposed architecture is less sensitive to several cases of the common variations and occlusions that affect and degrade gait recognition performance. It can also handle relatively small data sets without using any augmentation or fine-tuning techniques. Our specialized deep CNN model outperforms the existing gait recognition techniques when tested on the CASIA-B large gait dataset.

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Lameness is characterized by abnormal gait and is an indicator of various hoof diseases in cattle. Not only does this raise animal welfare issues, it also causes significant economic loss from reduced milk yield and fertility. Despite that, prevalence of lameness in dairy farms is high because farmers are unable to dedicate time and labor to identify lame cattle. Many automatic lameness detection solutions have been proposed in literature. Machine vision solutions using cameras are especially attractive because cameras do not require much space and is relatively low cost. However, none of the machine vision solutions so far have been robust enough to be useful to dairy farmers. This thesis attempts to remedy that by applying deep learning methods to the pose estimation of cattle to analyze their gait and detect the presence of lameness. 313 videos of cattle walking were recorded at a dairy farm. Images were randomly extracted from those videos and 17 body parts were manually annotated on the images to fine-tune a deep neural network pretrained on ImageNet. The fine-tuned network is then used to automatically find the trajectories of the 17 body parts in all 313 videos. 84 gait features were extracted from each video based on these trajectories. Each video was also manually given a locomotion score between 1-5 by 2 experts. Due to the small number of locomotion score 5 cows, locomotion score 4 and 5 were merged into one group for analysis. Two experiments were conducted with these gait features and locomotion scores: a) Data analysis to test significant differences between locomotion score groups and b) Automatic locomotion score classification. Data analysis was done using ANOVA followed by Bonferroni correction. Stance time related features were the best at differentiating locomotion score groups, but were unable to differentiate between locomotion score pair 2<>3. Step length related features were also relatively good at differentiating different locomotion score groups, but have trouble differentiating between locomotion score pairs 1<>2 and 2<>3. For the automatic classification, the 84 gait features were first reduced to 3 features using LDA. Then, various classifiers were trained with these 3 features and locomotion score as labels. The linear discriminant classifier achieved the highest classification rate at 85.6%, but this number was heavily skewed by the high classification rate of locomotion score 1 group (95.3%), which also makes up the largest portion of the dataset. To correct this imbalance in the dataset, the prior probabilities were set equal for each locomotion score group and the classifiers were trained again. This resulted in much better classification rates with the linear discriminant classifier for locomotion score 2 and 3 (71% and 84% respectively) at the expense of lowering the classification rate of locomotion score 1 (83.4%).

Mechanical Engineering

Advisors/Committee Members: Harlaar, Jaap (mentor), Geelen, Jinne (graduation committee), Tax, David (graduation committee), Delft University of Technology (degree granting institution).

▼ Gait analysis has been an active area of research in computer vision for a long time. It is also important for rehabilitation science where clinicians explore innovative ways helping to analyze gait of different people. The traditional ways to study gait rely on 3D optical motion capture systems which involve the use of cumbersome active/passive markers to be placed on a subject's body. The attachment of markers to the segments hinder natural patterns of movement and may lead to altered gait information. Automated gait analysis has been proposed as a solution to this problem. The aim of automated gait analysis is to provide information about the gait parameters and gait determinants from video without using markers. Gait is a repetitive, highly constrained and periodic activity. Different gait determinants are active in different phases of the gait cycle to minimize the excursion of the body's center of gravity and help produce forward progression with the least expenditure of energy. The motion of limb segments encode information about different phases of gait cycle. However, estimating the motion of limbs from the videos is challenging since limbs are self occluding and only apparent motion can be observed using the images. To add to the issue, the quality of the recorded video (color contrast, cluttered background) and clothing worn by the subject can play a significant role in the computation of that apparent motion. Advisors/Committee Members: Durić, Zoran (advisor), Gerber, Naomi L (advisor).

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Gait event detection allows for insight into one’s gait pattern, an invaluable aid in rehabilitation. Current methods often rely on measured acceleration and rarely on position measurements [3]–[6]. In this paper we propose 4 novel gait detection methods based on the position of the Center of Mass (one approach being causal and thus suitable for real-time use) and compare them to 4 existing state-of-the-art acceleration- based methods. All algorithms are benchmarked on an existing data set (overground walking, 23 participants, 1772 steps), comparing the detection rate, false positive rate and the mean and (intra- and interparticipant) standard deviation of the timing error for Heel Strikes and Toe-Offs. We show that position-based algorithms give well-balanced results and are able to outperform the acceleration-based algorithms in all five metrics. Additionally, we propose and compare several methods for detecting left and right steps, thereby enabling quantification of the full gait cycle.

Biomechanical Design | BioRobotics

Advisors/Committee Members: Vallery, H. (mentor), Reijne, M.M. (graduation committee), Kok, M. (graduation committee), Delft University of Technology (degree granting institution).

▼ Knee osteoarthritis (OA) is a progressive disease and a leading cause of morbidity in older adults, resulting in severe mobility limitations. While the osteoligamentous and neuromuscular systems are altered in knee OA, little data is available to illustrate an association among these systems. The objective of this dissertation was to improve our understanding of how muscle activation patterns during gait are altered across the knee OA severity spectrum and to examine how factors related to the OA process are associated with these alterations. Three independent but related studies were conducted. Muscle activation of the medial and lateral orientations of the gastrocnemii, quadriceps and hamstrings were recorded during gait using surface electromyography for all three studies. Key activation features were identified using principal component analysis. First, participants selected from a large group (n=272) of individuals classified as asymptomatic, ii) moderate ii) severe knee OA were matched for walking velocity. Significant amplitude and temporal activation characteristics were found, supporting that differences among OA severities exist and were not the result of walking velocity only. Secondly, individuals with moderate OA were sub-grouped based on structural severity determined using Kellgren-Lawrence radiographic scores (II-IV) and were compared to a velocity-matched asymptomatic group. Medial gastrocnemius, lateral hamstring and quadriceps amplitudes and temporal patterns were significantly altered by structural severity where significant activation imbalances between the lateral:medial gastrocnemii and hamstrings were found with greater structural impairment (score>II). Thirdly, individuals with moderate OA were prospectively evaluated and divided into knee effusion and no effusion groups, based on a positive bulge test. A significantly higher knee flexion angle during mid-stance, higher quadriceps amplitudes and prolonged hamstrings amplitudes were found when effusion was found. These studies showed that muscle activation patterns during walking were related to i) OA presence and severity based on functional, symptoms and radiographic evidence, ii) structural severity and iii) knee joint effusion. These findings improve our understanding of the interrelationships between alterations in joint structure and function associated with knee OA and muscle activation patterns during gait. These data can contribute to the development of gait-based metrics that can facilitate knee OA diagnosis and monitor progression. Advisors/Committee Members: Dr. Walter Herzog (external-examiner), Dr. Sarah Wells (graduate-coordinator), Dr. John Kozey (thesis-reader), Dr. William Stanish (thesis-reader), Dr. Cheryl Hubley-Kozey (thesis-supervisor), Not Applicable (ethics-approval), Yes (manuscripts), Yes (copyright-release).

▼ Securing smartphones has increasingly become inevitable due to their massive popularity and significant storage and access to sensitive information. The gatekeeper of securing the device is authenticating the user. Amongst the many solutions proposed, gait recognition has been suggested to provide a reliable yet non-intrusive authentication approach - enabling both security and usability. While several studies exploring mobile-based gait recognition have taken place, studies have been mainly preliminary, with various methodological restrictions that have limited the number of participants, samples, and type of features; in addition, prior studies have depended on limited datasets, actual controlled experimental environments, and many activities. They suffered from the absence of real-world datasets, which lead to verify individuals incorrectly. This thesis has sought to overcome these weaknesses and provide, a comprehensive evaluation, including an analysis of smartphone-based motion sensors (accelerometer and gyroscope), understanding the variability of feature vectors during differing activities across a multi-day collection involving 60 participants. This framed into two experiments involving five types of activities: standard, fast, with a bag, downstairs, and upstairs walking. The first experiment explores the classification performance in order to understand whether a single classifier or multi-algorithmic approach would provide a better level of performance. The second experiment investigated the feature vector (comprising of a possible 304 unique features) to understand how its composition affects performance and for a comparison a more particular set of the minimal features are involved. The controlled dataset achieved performance exceeded the prior work using same and cross day methodologies (e.g., for the regular walk activity, the best results EER of 0.70% and EER of 6.30% for the same and cross day scenarios respectively). Moreover, multi-algorithmic approach achieved significant improvement over the single classifier approach and thus a more practical approach to managing the problem of feature vector variability. An Activity recognition model was applied to the real-life gait dataset containing a more significant number of gait samples employed from 44 users (7-10 days for each user). A human physical motion activity identification modelling was built to classify a given individual's activity signal into a predefined class belongs to. As such, the thesis implemented a novel real-world gait recognition system that recognises the subject utilising smartphone-based real-world dataset. It also investigates whether these authentication technologies can recognise the genuine user and rejecting an imposter. Real dataset experiment results are offered a promising level of security particularly when the majority voting techniques were applied. As well as, the proposed multi-algorithmic approach seems to be more reliable and tends to perform relatively well in practice on real live user data, an improved model…

▼ The study of human gait is innate to human interest and pervades many fields including biometrics,clinical analysis, computer animation, and robotics. From a surveillance perspective, gait recognitionis capable of identifying humans at a distance by inspecting their walking manners. It is an attractivemodality which can be performed surreptitiously in an unconstrained environment. Gait is one of thefew biometric features that can be measured remotely without physical contact and proximal sensing,which makes it useful in surveillance applications. However, in the real world, there are variousfactors significantly affecting human gait including clothes, shoes, carrying objects, walking surfaces,observed views, and walking speeds. Among these factors, changes of views and speeds have beenregarded as two of the most challenging problems for gait recognition. Particularly, view change willsignificantly impact on available visual features for matching, while speed change will alter walkingpatterns of each individual substantially. This thesis is mainly to develop novel methods forrecognizing gaits under changes of walking conditions focusing on views and speeds, without acooperative camera system. Five major methods are proposed from several various perspectives toaddress key aspects of these problems. Principally, a view-normalization of gaits is obtained througha new learning process by using mapping/projection relationships between correlated gait featuresacross different views, while a novel speed-invariant gait feature is developed by using a statisticalshape analysis based on a local-static gait information. Based on widely adopted gait databases, thecomprehensive experiments are carried out to verify the proposed methods. It is concluded that theproposed methods can achieve state-of-the-art performances for gait recognitions under view changeand/or speed change. In this thesis, the other relevant problems are also sorted out, including gaitperiod analysis, view classification, and walking speed estimation. Moreover, in order to enhance theperformance, multi-view gait information is utilised to achieve more stable and convincing outcomes. Advisors/Committee Members: Zhang, Jian, Advanced Analytics Institute, School of Software, University of Technology Sydney, Wu, Qiang, School of Computing & Communications, University of Technology Sydney, Li, Hongdong, Research School of Engineering, College of Engineering and Computer Science, Australian National University, Lin, Xuemin, Computer Science & Engineering, Faculty of Engineering, UNSW, Wang, Wei, Computer Science & Engineering, Faculty of Engineering, UNSW.

▼ Based on World Health Organization (WHO) report, between 250,000 and 500,000 people suffer from disabilities caused by spinal cord injuries each year. The result of this study is development of a medical device to restore walking in such patients using Functional Electrical Stimulation (FES). We selected dogs as our animal subject. This device uses FES to prevent an affected dog with limited walking abilities from falling during walking. The final version of the device includes a sensing core consisted of four Inertial Measurement Units (IMUs) attached to the hip, femur, tibia and metatarsus of our test subject. Using this sensory system, the device tracks and measures the hip, knee and hock joint angles in real time. We use a commercial microcontroller as our analytical core to provide suitable stimulation commands and provide appropriate voltage/current for delivery to target muscles. Data from IMUs are received by microcontroller using I2C bus communication. An advanced embedded C code is developed to program the microcontroller. We discuss a method to recognize the swing and stance phases of the dog gait during walking and propose several balancing strategies to be used for gait control during the stance and swing phase before falling occurs. We design and build a robodog to be compatible with the medical device. We use this robot to program and test the different cores of the device. We test our balancing strategies on our bionic test-bed before applying them on an actual animal subject. Results show the device can provide suitable sensing and stimulation control to balance the body of a dog that has limited ambulation abilities.

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Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

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Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

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Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

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Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ 

Bioengineering

Locomotion is essential to survival in most animals. Studies have shown that animals, including humans, choose a gait that minimizes the risk of injury and maximizes energetic efficiency. Individuals often encounter obstacles and perturbations during normal locomotion, from which they must recover. Despite the importance of understanding the mechanisms that enable recovery from perturbations, ethical and experimental challenges have prevented full exploration of these in legged systems. A powerful paradigm with which to tackle this difficulty would be the application of external and internal manipulation of the nervous system. These perturbations could target how gait is regulated and how the neural systems process sensory information to control locomotion during an unexpected perturbation. Here we present data on the response of female mice to rapid, precisely timed, and spatially confined mechanical perturbations applied by a treadmill system. Our data elucidate that after the mechanical perturbation, the mouse gait response is anisotropic, preferring deviations away from the trot towards bounding, over those towards other gaits, such as walk or pace. We quantified this shift by projecting the observed gait onto the line between trot and bound, in the space of quadrupedal gaits. We call this projection λ. For λ=0, the gait is the ideal trot; for λ=±π, it is the ideal bound. We found that the substrate perturbation caused a significant shift in λ towards bound during the stride in which the perturbation occurred and the following stride (linear mixed effects model: Δλ=0.26±0.07 and Δλ=0.21±0.07, respectively; random effect for animal, p<0.05 for both strides, n = 8 mice). We hypothesize that this is because the bounding gait is better suited to rapid acceleration or deceleration, and an exploratory analysis of jerk showed that it was significantly correlated with λ (p<0.05). To evaluate whether the same structure of gait controller exists when undergoing an entirely different class of manipulation, we applied an internal, neuromuscular perturbation. We directly stimulated the lateral gastrocnemius muscle of mice using implanted electrodes and a custom magnetic headstage. We found that the electrical muscle stimulation caused a significant shift in λ towards bound in trials where the stimulation occurred during the swing phase (linear mixed effects model: Δλ=0.23±0.06 and Δλ=0.28±0.06; for the stride during and after the stimulation, respectively; random effect for animal, p<0.05 for both, n = 7 mice). Understanding how gait is controlled under perturbations can give insight into the neuromechanical basis of locomotion, aid in diagnosing gait pathologies, and aid the design of more agile robots.

Temple University – Theses

Advisors/Committee Members: Spence, Andrew;, Lemay, Michel A., Smith, George, Hsieh, Tonia;.

▼ Coordination indicates the ability to assemble and maintain a series of proper relations between joints or segments during motions. In Dynamical Systems Theory (DST), movement patterns are results of a synergistic organization of the neuromuscular system based on the constraints of anatomical structures, environmental factors, and movement tasks. Human gait requires the high level of neuromuscular control to regulate the initiation, intensity and adaptability of movements. To better understand how the neuromuscular system organizes and coordinates movements during walking, examination of single joint kinematics and kinetics alone may not be sufficient. Studying inter-joint coordination will provide insights into the essential timing and sequencing of neuromuscular control over biomechanical degrees of freedom, and the variability of inter-joint coordination would reflect the adaptability of such control. Previous studies assessing inter-joint coordination were mainly focused on neurological deficiencies, such as stroke or cerebral palsy. However, information on how inter-joint coordination is modulated with different constraints, such as walking speeds, aging, brain injury or joint dysfunctions, are limited. This knowledge could help us in identifying the potential risks during walking and improve the performance of individuals with movement impairments. The purpose of the present study was to investigate the properties of inter-joint coordination pattern and variability during walking with different levels of neuromuscular system perturbations using a DST approach, including an overall neuromuscular systemic degeneration, a direct insult to the brain, and a joint disease. We found that aging seemed to reduce the pattern adaptability of neuromuscular control. Isolated brain injury and joint disease altered the coordination pattern and exaggerated the variability, indicating a poor neuromuscular control. To improve gait performances for different populations, clinical rehabilitation should be carefully designed as different levels of neuromuscular system constraints would lead to different needs for facilitating appropriate coordinative movement. This dissertation includes both previously published/unpublished and coauthored material. Advisors/Committee Members: Chou, Li-Shan (advisor).

▼ The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

▼ Microprocessor-controlled stance-control knee-ankle-foot orthoses (M-SCKAFO) can have multiple sensors at all lower-limb segments. This causes M-SCKAFO to be bulky and expensive, with complex control systems. Stance-control systems with sensors local to the knee-joint component would provide a modular orthosis component for easier orthotist-customization and personalization for users with knee-extensor weakness. A gait phase recognition model (GPR) is essential for a fast, accurate, and generalizable real-time orthosis-control. This thesis designed, developed, and evaluated a machine learning-based GPR model for intelligent M-SCKAFO control. The model used gait signals that mimicked thigh inertial sensor and knee angle. Machine learning was implemented to identify gait phases across multiple surface conditions and walking speeds. Thigh-segment angular velocity, thigh-segment acceleration, and knee angle were calculated from 30 able-bodied participants for level and up, down, right-cross, and left-cross slopes at 0.8, 0.6, 0.4 m/s, and self-paced speeds (1.33 m/s, SD = 0.04 m/s). A logistic model tree (LMT) was built with a set of 20 signal features extracted from 0.1s sliding windows. The GPR model determined the walking state and was fed through a “transition sequence verification and correction” (TSVC) algorithm to deal with continuous states. The GPR model was evaluated on a different data set from 12 able-bodied individuals that completed the same walking protocol (validation set). Gait phases were classified successfully regardless of surface-level, walking speed, and individual walking variability. The LMT had a tree size of 1643 nodes with 822 leaf nodes. The GPR model produced overall classification accuracy of 98.4% and increased to 98.7% when TSVC was applied. Results also demonstrated evidence of strong model-generalizability with GPR accuracy of 90.6% and increased to 98.6% when TSVC was applied, on the validation set. This research demonstrated that local sensor signals from thigh and knee, integrated with machine intelligence algorithms, provided viable GPR suitable for real-time orthosis-control. The logistic decision tree model and feature selection approach were computationally efficient for real-time GPR and gave reliable, robust, and generalizable results across multiple surfaces, walking speeds, and individual walking variability. GPR also benefitted from transition sequence verification and correction algorithms, providing enhanced gait phase classification performance.

▼ Alterations in gait patterns are commonly observed in individuals with transtibial amputation (TTA) who use a prosthesis. Current commercially available ankle-foot prostheses (AFP) offer very little range of motion (ROM) at the ankle joint. Previous researchers have hypothesized that lack of ankle ROM significantly contributes to alterations in TTA gait patterns. However, different patterns have been observed among TTA using the same AFP. Therefore it is unclear how restricted ankle ROM in current commercially available ankle-foot prostheses (AFP) contributes to observed changes in gait. Alterations in gait patterns have been shown to increase the incidence of low back pain and other musculoskeletal injuries. TTA have a greater incidence of low back pain and osteoarthritis of the knee and hip. Therefore it is important for researchers to understand the influence of different prosthetic components on gait in order to optimize gait patterns and minimize complications due to alterations in gait. The purpose of this study was to determine what compensatory alterations in gait patterns may occur as a result of imposed restricted ankle range of motion. Kinematic data was collected from 19 participants (9 men, 10 women) age 18-32 with no previous history of lower extremity injury or deformity in two conditions: level-ground walking with no restriction and level ground walking with the ankle restricted at 0 degrees plantarflexion by plaster casting. Results indicated that restricted ankle ROM contributes to decreased velocity and cadence and decrease in gait symmetry. A compensatory pattern was observed for pelvic obliquity, hip and knee flexion at toe-off and foot progression angle. Observed patterns did not resemble those observed in TTA. Results suggest that restricted ankle ROM contributes to some components of alterations in gait patterns observed in TTA. However a combination of other components, including loss of proprioception and power generation at the metatarsophalangeal (toe) joints may have a more significant contribution to TTA gait patterns than restricted ankle ROM alone. Advisors/Committee Members: Yun, Joonkoo (advisor), McCubbin, Jeff (committee member).