Submitted by WASHINGTON TAKASHI KANO null (washingtontakashikano@hotmail.com) on 2018-01-31T21:20:02Z No. of bitstreams: 1 VERSÃO FINAL - TESE - WASHINGTON.pdf: 1112686 bytes, checksum: 1982a0d4d87127d62cf86b5bff0aed6e (MD5) / Approved for entry into archive by Maria Lucia Martins Frederico null (mlucia@fca.unesp.br) on 2018-02-01T10:59:18Z (GMT) No. of bitstreams: 1 kano_wt_dr_bot.pdf: 1112686 bytes, checksum: 1982a0d4d87127d62cf86b5bff0aed6e (MD5) / Made available in DSpace on 2018-02-01T10:59:18Z (GMT). No. of bitstreams: 1 kano_wt_dr_bot.pdf: 1112686 bytes, checksum: 1982a0d4d87127d62cf86b5bff0aed6e (MD5) Previous issue date: 2018-01-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / RESUMO O objetivo do presente estudo foi avaliar os sinais eletromiográficos do músculo bíceps femoral de três raças de cães hígidos, ao caminhar e ao trote. Foram utilizados 31 animais, distribuídos em três grupos: Grupo 1 – 10 cães da raça Beagle; Grupo 2 – 10 cães da raça Boiadeiro Australiano; Grupo 3 – 11 cães da raça Labrador. Foram determinados os parâmetros têmporo-espaciais e porcentagem de pressão para ambos os membros torácicos e pélvicos, no caminhar e ao trote, ao mesmo tempo em que foi analisado o músculo bíceps femoral, direito e esquerdo, por meio do sistema eletromiográfico de superfície. Pela análise estatística não foram observadas diferenças entre os membros, direito e esquerdo, tanto para os valores têmporo-espaciais como porcentagem de pressão. O padrão gráfico de atividade muscular mostrou que nos Grupos 2 e 3 na locomoção ao caminhar, o músculo bíceps femoral apresentou 1 pico máximo na fase de apoio, e 1 pico mínimo e 1 pico máximo de menor intensidade na fase de balanço. No Grupo 1 foi detectada a presença de três picos. Na locomoção ao trote o padrão gráfico de atividade muscular foi similar para os três grupos, com 1 pico máximo na fase de apoio, e 1 pico mínimo e 1 pico máximo na fase de balanço, ambos de menor intensidade. Os Grupo 2 e 3 não apresentaram diferença estatística na porcentagem de atividade muscular média ao caminhar e ao trote. Foi possível concluir que no trote o padrão de atividade eletromiográfico do músculo bíceps femoral mostrou semelhança entre os grupos. / SUMMARY The aim of this study was to evaluate the electromyography (EMG) signals of the biceps femoris muscle of three healthy dog breeds at the walk and trot. Thirty-one dogs were used, distributed in three groups: Group 1 - 10 Beagles, Group 2 - 10 Blue heelers, Group 3 - 11 Labradors. The temporospatial parameters and percentage of pressure in both thoracic and pelvic limbs were determined during walking and trotting, at the same time that the right and left biceps femoris muscles were analyzed by use of surface EMG. There were not any statistically significant differences between right and left limbs for both temporospatial values and percentage of pressure. The graphical pattern of muscle activity showed in Groups 2 and 3 at walk that the biceps femoris muscle had one maximal peak in the stance phase, and one minimal peak and one maximal peak of lower intensity in the swing phase. A three-peak activity pattern was observed in Group 1. The graphs of muscular activity patterns was similar among groups at trot showing one maximal peak in the stance phase, and one minimal peak and one maximal peak in the swing phase, both of lower intensity. The Groups 2 and 3 did not present statistical differences in the percentage of average muscle activity at walk and at trot. In conclusion, the electromyographic activity pattern of the biceps femoris muscle was similar among the groups at the trot. / 1457459

Our goal is to introduce a novel method for representing, generalising, and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiment may be recovered. Our approach is built upon (i) work in the domain of null-space policy recovery and (ii) previous work in generalisation for point-to-point movements. The problem is formalised using a walking phase model, and the null-space learning method is used to generalise a consistent policy from multiple observations with rich variations. Once recovered, the underlying policies (mapped to different gait phases) can serve as reference guideline to quantify and identify pathological gaits while being robust against interpersonal and task variations. To validate our methods, we have demonstrated robustness of our method with simulated sagittal 2-link gait data with multiple ground truth constraints and policies. Pathological gait identification was then tested on real-world human gait data with induced gait abnormality, with the proposed method showing significant robustness to variations in speed and embodiment compared to template based methods. Future work will extend this to kinetic features and higher degree-of-freedom.

Progress in the fields of wearable sensor technologies together with specialized analysis algorithms has enabled systems for gait analysis outside labs. An example of a wearable sensor is the accelerometer embedded in a typical smartphone. The goal was to propose a system design capable of hosting existing gait analysis algorithms in a cloud environment, and tailor the design as to deliver fast results with the ambition of reaching near real-time.    The project identified a set of enabling technologies by examining existing systems for gait analysis; the technologies included cloud computing and WebSockets. The final system design is a hierarchical composition starting with a Linux VM running Node.js, which in turn connects to a database and hosts instances of the MatLab runtime. The results show the feasibility of mobile cloud based free-living gait analysis. The architectural design provides a solution to the critical problem of enabling existing algorithms to run in a cloud environment; and shows how  the graphical output of the native algorithm could be accurately reproduced in a web browser. The system can process a chunk of 1300 data points under 3 seconds for a client streaming at 128 Hz, while simultaneously streaming the real time signal.

abstract: The human ankle is a critical joint required for mobility and stability of the body during static and dynamic activity. The absence of necessary torque output by the ankle due to neurological disorder or near-fatal injury can severely restrict locomotion and cause an inability to perform daily tasks. Physical Human-Robot Interaction (pHRI) has explored the potential of controlled actuators to positively impact human joints and partly restoring the required torque and stability at the joint to perform a task. However, a trade-off between agility and stability of the control technique of these devices can reduce the complete utilization of the performance to create a desirable impact on human joints. This research focuses on two control techniques of an Active Ankle Foot Orthosis (AFO) namely, Variable Stiffness (VS) and Variable Damping (VD) controllers to modulate ankle during walking. The VS controller is active during the stance phase and is used to restore the ankle trajectory of healthy participants that has been altered by adding a dead-weight of 2 Kgs. The VD controller is active during the terminal stance and early-swing phase and provides augmentative force during push-off that results in increased propulsion and stabilizes the ankle based on user-intuitions. Both controllers have a positive impact on Medial Gastrocnemius (GAS) muscle and Soleus (SOL) muscle which are powerful plantar - flexors critical to propulsion and kinematic properties during walking. The VS controller has recorded an 8.18% decrease in GAS and an 9.63 % decrease in SOL muscle activity during the stance phase amongst participants while decreasing mean ankle position error by 22.28 % and peak ankle position error by 17.43%. The VD controller demonstrated a 7.59 % decrease in GAS muscle and a 10.15 % decrease in SOL muscle activity during push-off amongst the participants while increasing the range-of-motion (ROM) by 7.84 %. Comprehensively, the study has shown a positive impact on ankle trajectory and the corresponding muscle effort at respective stages of the controller activity. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2020

Human knee kinematics, especially during gait, are an important analysis tool. The current "gold standard" for kinematics measurement is a multi-camera, marker-based motion capture system with 3D position tracking. These systems are accurate but expensive and their use is limited to a confined laboratory environment. High deflection strain gauges (HDSG) are a novel class of sensors that have the potential to measure kinematics and can be inexpensive, low profile, and are not limited to measurements within a calibrated volume. However, many HDSG sensors can have a non-linear and non-monotonic response. This thesis explores using a nanocomposite HDSG sensor system for measuring knee kinematics in walking gait and overcoming the non-monotonic sensor response found in HDSGs through advanced modeling techniques. Nanocomposite HDSG sensors were placed across the knee joint in nine subjects during walking gait at three speeds and three inclines. The piezoresistive response of the sensors was obtained by including the sensors in a simple electrical circuit and recorded using a low-cost microcontroller. The voltage response from the system was used in four models. The first two models included a physics-based log-normal model and statistical functional data analysis model that estimated continuous knee angles. The third model was a discrete linear regression model that estimated the inflection points on the knee flexion/extension cycle. Finally, a machine learning approach helped to predict subject speed and incline of the walking surface. The models showed the sensor has the capability to provide knee kinematic data to a degree of accuracy comparable to similar kinematic sensors. The log-normal model had a 0.45 r-squared and was unsuitable as a stand-alone continuous angle predictor. After running a 10-fold cross validation the functional data analysis (FDA) model had an overall RMSE of 3.4° and could be used to predict the entire knee flexion/extension angle cycle. The discrete linear regression model predicted the inflection points on the knee kinematics graph during each gait cycle with an average RMSE of 1.92° for angle measures and 0.0332 seconds for time measures. In every estimate, the discrete linear regression model performed better than the FDA model at those points. The 10-fold cross validation of the machine learning approach using the discrete voltages could predict the categorical incline 90% of the time and the RMSE for the speed model was 0.23 MPH. The use of a HDSG as a knee kinematics sensor was shown as a viable alternative to existing motion capture technology. In future work, it is recommended that a calibration method be developed that would allow this sensor to be used independent of a motion capture system. With these advancements, this inexpensive and low profile HDSG will advance understanding of human gait and kinematics in a more affordable and scope enhancing way.

Human gait analysis is an essential indicator for physical and neuroglial health of an individual. Recent developments in deep-learning approaches to computer vision make possible new techniques for body segment and joint detection from photos and video frames. In this thesis, we propose a deep learning approach for non-invasive video-based gait analysis using two RGB cameras that would be suitable for routine gait monitoring in senior care and rehabilitation centers. Due to modularity and the low cost of implementation, it is considered an affordable solution for such centers. Furthermore, since the solution does not require any markers or sensors to be worn, it is a pervasive and easy method for daily usage. Our proposed deep-learning approach starts by calibrating both the intrinsic and extrinsic parameters of the cameras. Next, video streams captured from two RGB cameras are used as input, and OpenPose and HyperPose deep-learning frameworks are used to localize the main body key points, including the joints and skeleton based on Body 25 and COCO models, respectively. The 2D parameter outputs from the frameworks are triangulated into 3D vector spaces for further analysis. In order to reduce the noises in our data, we applied median and dual pass butter worth filters to the data. Finally gait parameters has been extracted measured and compared to the manually evaluated ground truth data which has been capture via manual measurement of a domain expert. The approach was evaluated in a laboratory setting similar to an institutional hallway in five types of trials: walking back and forth in a straight line while turning out of frame, walking back and forth in a straight line while turning in frame, circular walking, walking with a cane and a walker. The method brings promising results compared to more expensive and restrictive approaches that use up to 16 cameras and require markers or sensors.

Clinical decisions based on gait data obtained by optoelectronic motion capturing require profound knowledge about the repeatability of the used measurement systems and methods. The purpose of this study was to evaluate the effects of inconsistent anthropometric measurements on joint kinematics calculated with the Plug-in Gait model. Therefore, a sensitivity study was conducted to ascertain how joint kinematics output is affected to different anthropometric data input. One previously examined gait session of a healthy male subject and his anthropometric data that were assessed by two experienced examiners served as a basis for this analytical evaluation. This sensitivity study yielded a maximum difference in joint kinematics by the two sets of anthropometrics of up to 1.2°. In conclusion, this study has shown that the reliability of subjects’ anthropometrics assessed by experienced examiners has no considerable effects on joint kinematics.

京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第18199号 / 人健博第16号 / 新制||人健||2(附属図書館) / 31057 / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 黒木 裕士, 教授 足立 壯一, 教授 三谷 章 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

Bibliographical record DVOŘÁK, J. Physiological movements of the spine during locomotion. Prague: Charles University, 2nd Faculty of Medicine, Department of Rehabilitation and Sports Medicine 2021. 75 p. Thesis supervisor PhDr. Marcela Šafářová, Ph.D. Abstract The diploma thesis Physiological movements of the spine during locomotion deals with the relationship between locomotion, spatiotemporal properties of gait and spinal movements. The theoretical part of the work summarizes the knowledge about the phylogenetic and ontogenetic development of the spine. The paper discusses the influences that affect the motility of the spine from intrauterine development to old age. The main part of the theoretical part is devoted to an overview of studies examining the movements of the spine during human locomotion. The experimental part was performed by testing a group of younger (24.6 ± 3.6 years) and older adults (43.5 ± 4.6 years). Both groups consisted of 8 women and 8 men. A total of 32 volunteers were tested. Otto's spine distance, thoracic spine rotation, 95% COP standing, and spatiotemporal gait data were measured using a Zebris Rehawalk FDM-T. Thereafter, therapy was applied to the chest to affect the dynamics of movement. Finally, control measurements of all olunteers were performed. Statistical data...

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