Intra-articular corticosteroid knee injections (ICIs) were used as a tool to determine the sensitivity of wearable inertial sensors and machine learning algorithms in identifying meaningful changes in gait patterns amidst day-to-day fluctuations in out-of-laboratory gait. Specifically, three overarching aims were proposed; I) Determine if three gait trials could define an everyday typical gait pattern, II) investigate if post-injection atypical strides are significantly different from pre-injection atypical strides and III) explore the relationship between changes in pain and atypical strides. Nine knee OA patients (7M/2F) were recruited from St. Joseph’s Healthcare Hamilton. Participants completed a total of four walking trials prior to the ICI and three following. Participants were fitted with two wearable sensors on each shank just below the knee, and one sensor on the lower back during every trial. Data from these sensors were processed to train and test a one-class support vector machine (OCSVM). Individual gait models were created based on three out of the four pre-injection trials. Each trained model was tested on a withheld pre-injection trial and three post-injection trials to determine the number of typical and atypical gait cycles. Self-reported pain was analyzed throughout the study and compared to the percent of atypical strides seen during each walk. It was found that three gait trials could not define a typical gait model and that post-injection atypical strides were not significantly different from with-held pre-injection atypical strides. Finally, large variations and fluctuations in self-reported pain were observed on a week-to-week basis, which were not significantly correlated to atypical strides observed. This study was the first to investigate the sensitivity of wearable inertial sensors and machine learning algorithms to detect changes in real-world gait patterns and provides foundational work for using wearable sensors to monitor and triage knee OA patients. / Thesis / Master of Science (MSc)

Gait biomechanics have been studied extensively. Many existing studies, though, have been performed in a controlled laboratory setting, and assumed that measures obtained are representative of gait in a naturalistic environment (e.g., outdoors). Several environmental and psychological factors may contribute to differences between these environments, and identifying any such differences is important for generalizing results outside the laboratory. The purpose of this study was to test the implicit assumption that gait inside a research laboratory does not differ from gait outdoors, when a participant is unaware of data collection in the latter. Means and interquartile ranges (IQR) of several spatio-temporal and kinematic gait characteristics were obtained from 19 young adults during several gait conditions both inside a laboratory environment and outdoors. Four comparisons were made between the two environments, including conditions involving: 1) self-selected speeds, 2) matching outdoors self-selected speeds, 3) matching outdoors self-selected speeds while carrying a crate, and 4) matching outdoors hurried speeds. Spatio-temporal variables differed between the two environments in that self-selected walking speed was 1.7% slower inside the lab and cadence was 1.4-2.6% lower for all four comparisons. At heel contact, the foot was 4.4-8.1% more dorsiflexed inside the lab for all comparisons except in matching hurried outdoors walking speed. Minimum toe clearance was 6.5-16.2% lower outdoors for all four comparisons. It is unclear if these differences impair the ability to generalize gait study results to outside the laboratory. Nevertheless, some specific differences exist in gait between environments, and that research should recognize. / Master of Science

Both the brain and the individual’s environment contribute to the intertwined trajectory of developmental domains across the lifespan. The primary aim of this dissertation was to examine developmental cascades in autism through this combined perspective. First, we systematically investigated how neuroimaging techniques have been used to identify neuroanatomical and neurophysiological differences in infants with an elevated likelihood of developing autism. We found that, in addition to these distinct structural and functional differences, neuroimaging provides a novel opportunity to examine how these differences relate to developmental trajectories across a variety of domains. Second, we focused on assessing gross motor performance in autistic children within their home environment, and relating this performance to standardized measures of movement, adaptive behavior, and communication skills. We found notable levels of variability in gross motor performance, and significant relationships between these measures and measures of adaptive behavior. Finally, through an embodied cognition, developmental systems approach, we assessed the feasibility of using both neuroimaging and lab-based tasks to assess the relationship between cognitive and motor performance in autistic children. We designed and executed a novel study using functional near-infrared spectroscopy during a dual-task walking paradigm and found that, while the paradigm itself produces promising preliminary data, several aspects of the study design may need to be reconsidered in order to ensure feasibility across all participants. Combined, these findings suggest that we can best understand the relationships between developmental domains in autism through a multifaceted lens. This research provides foundational groundwork for comprehensive autism research methods and paves the way for identifying and supporting autistic children’s individual differences, both across the spectrum and within intersecting developmental domains. / 2026-08-19T00:00:00Z

Thesis name: The evaluating of the change of walking quality in dancers in comparison with the normal population Thesis goal: This thesis deals with effect of long-term ballet dance on kinematic parameters of gait. The theoretical part includes basic characteristics of gait cycle and kinesiological and biomechanical findings of ballet movement and its compensatory mechanisms in musculoskeletal system. There is analyzed angular parameters of gait cycle in ballet dancers in performance of walking in the experimental part. The results will show if the many-years intensive training of dance affects the alignment of particular joints of the body during human walk. Method: Kinematic analysis by Qualisys system allowing automatic processing of record obtained with infrared cameras. Qualisys uses its own high-frequency cameras for precise movement tracking of the measured object using active or passive markers. Gathered data from device were processed and statictically evaluated with Microsoft Office Excel. Keywords: gait, bipedal locomotion, dance, gait analysis, gait of dancers, ballet, kinematics analysis

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.

Analyzing a person's gait is important in determining their physical and neurological health. However, typical motion analysis laboratories are only in urban specialty care facilities and can be expensive due to the specialized personnel and technology needed for these examinations. Many patients, especially those who reside in underdeveloped or isolated locations, find it impractical to go to such facilities. With the help of recent developments in high-performance computing and artificial intelligence models, it is now feasible to evaluate human movement using digital video. Over the past 20 years, various visual gait analysis tools and scales have been developed. A study of the literature and discussions with physicians who are domain experts revealed that the Edinburgh Visual Gait Score (EVGS) is one of the most effective scales currently available. Clinical implementations of EVGS currently rely on human scoring of videos. In this thesis, an algorithmic implementation of EVGS scoring based on hand-held smart phone video was implemented. Walking gait was recorded using a handheld smartphone at 60Hz as participants walked along a hallway. Body keypoints representing joints and limb segments were then identified using the OpenPose - Body 25 pose estimation model. A new algorithm was developed to identify foot events and strides from the keypoints and determine EVGS parameters at relevant strides. The stride identification results were compared with ground truth foot events that were manually labeled through direct observation, and the EVGS results were compared with evaluations by human scorers. Stride detection was accurate within 2 to 5 frames. The level of agreement between the scorers and the algorithmic EVGS score was strong for 14 of 17 parameters. The algorithm EVGS results were highly correlated to scorers' scores (r>0.80) for eight of the 17 factors. Smartphone-based remote motion analysis with automated implementation of the EVGS may be employed in a patient's neighborhood, eliminating the need to travel. These results demonstrated the viability of automated EVGS for remote human motion analysis.

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La marche est le mode de locomotion naturel de l’homme. Malgré les très nombreuses études s’y étant intéressé, cela reste un mouvement complexe. Ceci est d’autant plus vrai lorsqu’une pathologie vient le perturber. Dans le cadre clinique, le recueil de données réalisé est appelé l’Analyse Quantifiée de la Marche (AQM). Elle s’adresse, notamment, à des patients souffrant de troubles de la marche issus de pathologies affectant le système nerveux central. La quantité dedonnées pouvant être extraite d’une AQM étant très importante, des index ont été définis et validés. Le Gillette Gait Index (GGI), le Gait Deviation Index (GDI) et l’Edinburgh Visual Gait Score (EVGS) sont parmi les plus utilisés. Leurs limites principales sont qu’ils ont été définis que pour la prise en charge des enfants paralysés cérébraux et qu’ils sont basés presque exclusivement sur la cinématique. Les modalités de calcul de ces index n’étant pas spécifiques de la pédiatrie, dans un premier temps, nous avons voulu voir comment ceux-ci se comportaient chez l’adulte. Nous avons ainsi, par la démonstration de l’applicabilité du GGI, du GDI et de l’EVGS, validé le principe de l’AQM chez l’adulte. Cependant, de façon courante, l’AQM comprend dans son protocole un enregistrement électromyographique (EMG) qui ne fait que très rarement l’objet d’une réelle quantification à la marche. Nous avons donc, dans un second temps, défini un nouvel index dequantification de la marche pathologique basé sur l’EMG : le Ker-EGI. Cet index reprend la philosophie et le modèle mathématique du GDI. Nous avons validé le Ker-EGI chez l’adulte en le corrélant avec le GGI, le GDI et l’EVGS. Ce nouvel index va permettre de réaliser, à moindre coût, un meilleur suivi au quotidien des patients. Il est plus accessible en routine clinique et pourra être associé à l’EVGS pour donner un tableau clinique complet du patient (regards neuromoteur etcinématique) / Walking is the natural way of locomotion for human. Nevertheless, despite numerous studies, it remains a complex movement. This is all the more real when a pathology disturbs it. Data collection made on patients is called Clinical Gait Analysis (CGA). This is dedicated, in particular, to patients with a central nervous system disorder. As data outcoming from the CGA could be very heavy, indices have been defined and validated. Among the most used are the Gillette Gait Index(GGI), the Gait Deviation Index (GDI) and the Edinburgh Visual Gait Score (EVGS). Their main limitations are that they have only been defined for children with cerebral palsy and they are based quite solely one kinematics. As the methods to compute these indices are not child-specific, we have first evaluated how they could also be used in adults. So, demonstrating the applicability of the GGI, the GDI and the EVGS, we have validated the principle of CGA in adults. Usually, the CGA’s protocol includes electromyographic measures (EMG), but rarely these data are really quantified. That is why, secondly, we have defined a new index of gait quantification based on EMG: the Ker-EGI. This index uses the philosophy and the mathematical model of the GDI. We have validated the Ker-EGI in adults correlating it with the GGI, the GDI and the EVGS. This new index more accessible in clinical routine allows to perform, for a lower cost, a better patient’s care in everyday life. Furthermore, if the Ker-EGI is associated with the EVGS, we have a more complete clinical picture with a neuro-motor and kinematic view of the patient

Motion analysis deals with determining what and how activities are being performed by a subject, through the use of sensors. The process of answering the what question is commonly known as classification, and answering the how question is here referred to as characterization. Frequently, combinations of inertial sensor such as accelerometers and gyroscopes are used for motion analysis. These sensors are cheap, small, and can easily be incorporated into wearable systems. The overall goal of this thesis was to improve the processing of inertial sensor data for the characterization of movements. This thesis presents a framework for the development of motion analysis systems that targets movement characterization, and describes an implementation of the framework for gait analysis. One substantial aspect of the framework is symbolization, which transforms the sensor data into strings of symbols. Another aspect of the framework is the inclusion of human expert knowledge, which facilitates the connection between data and human concepts, and clarifies the analysis process to a human expert. The proposed implementation was compared to state of practice gait analysis systems, and evaluated in a clinical environment. Results showed that expert knowledge can be successfully used to parse symbolic data and identify the different phases of gait. In addition, the symbolic representation enabled the creation of new gait symmetry and gait normality indices. The proposed symmetry index was superior to many others in detecting movement asymmetry in early-to-mid-stage Parkinson's Disease patients. Furthermore, the normality index showed potential in the assessment of patient recovery after hip-replacement surgery. In conclusion, this implementation of the gait analysis system illustrated that the framework can be used as a road map for the development of movement analysis systems.

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