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To compare the one-year postoperative outcomes of anti-gravity treadmill rehabilitation with those of standard rehabilitation in patients with ankle or tibial plateau fractures.

Yes / Introduction: Clinicians typically use findings from cohort studies to objectively inform judgements regarding the potential (dis)advantages of prescribing a new prosthetic device. However, before finalising prescription a clinician will typically ask a patient to ‘try out’ a change of prosthetic device while the patient is at the clinic. Observed differences in gait when using the new device should be the result of the device’s mechanical function, but could also conceivably be due to patient related factors which can change from day-to-day and can thus make device comparisons unreliable. To determine whether a device’s mechanical function consistently has a more meaningful impact on gait than patient-related factors, the present study undertook quantitative gait analyses of a trans-tibial amputee walking using two different foot-ankle devices on two occasions over a year apart. If the observed differences present between devices, established using quantitative gait analysis, were in the same direction and of similar magnitude on each of the two occasions, this would indicate that device-related factors were more important than patient-related factors. Methods: One adult male with a unilateral trans-tibial amputation completed repeated walking trials using two different prosthetic foot devices on two separate occasions, 14 months apart. Walking speed and sagittal plane joint kinematics and kinetics for both limbs were assessed on each occasion. Clinically meaningful differences in these biomechanical outcome variables were defined as those with an effect size difference (d) between prosthetic conditions of at least 0.4 (i.e. ‘medium’ effect size). Results: Eight variables namely, walking speed, prosthetic ‘ankle’ peak plantar- and dorsi- flexion and peak positive power, and residual knee loading response flexion, peak stance-phase extension and flexion moments and peak negative power, displayed clinically meaningful differences (d > 0.4) between foot devices during the first session. All eight of these showed similar effect size differences during the second session despite the participant being heavier and older. Conclusions: Findings suggest that a prosthetic device’s mechanical function consistently has a more meaningful impact on gait than patient-related factors. These findings support the current clinical practice of making decisions regarding prosthetic prescription for an individual, based on a single session evaluation of their gait using two different devices. However, to confirm this conclusion, a case series using the same approach as the present study could be undertaken.

The ankle joint complex is a common source of injury for various demographics and is often observed during gait analysis. I investigate using soft robotic sensors as a means for collecting kinematic data at the ankle joint complex. I validate the linearity of these sensors by measuring stretch against extension and against stretch from frontal and sagittal planar foot movements using a wooden ankle mockup. I then conduct a study involving ten participants who perform repetitive trials of four foot movements (plantarflexion, dorsiflexion, inversion and eversion) using ten different locations. Four optimal locations were identified for these movements based on linearity, accuracy, robustness, and consistency. Lastly, I validated soft robotic sensors against the human gait cycle. Twenty participants were recruited and performed twelve trials, walking across a flat surface and a cross-sloped surface while motion capture data and soft robotic sensor data was collected.

The loss of mobility among the elderly has become a significant health and socio-economic concern worldwide. Poor mobility due to gradual deterioration of the musculoskeletal system causes older adults to be more vulnerable to serious health risks such as joint injuries, bone fractures and traumatic brain injury. The costs associated with the treatment and management of these injuries are a huge financial/social burden on the government, society and family. Knee is one of the key joints that bear most of the body weight, so its proper function is essential for good mobility. Further, Continuous monitoring of the knee joint can potentially provide important quantitative information related to knee health and mobility that can be utilized for health assessment and early diagnoses of mobility-related problems. In this research work, we developed an easy-to-use, low-cost, multi-sensor-based wearable device to monitor and assess the knee joint and proposed an analysis system to characterize and classify an individual’s knee joint features with respect to the baseline characteristics of his/her peer group. The system is composed of a set of different miniaturized sensors (inertial motion, temperature, pressure and galvanic skin response) to obtain linear acceleration, angular velocity, skin temperature, muscle pressure and sweat rate of a knee joint during different daily activities. A database is constructed from 70 healthy adults in the age range from 18 to 86 years using the combination of all signals from our knee joint monitoring system. In order to extract relevant features from the datasets, we employed computationally efficient methods such as complementary filter and wavelet packet decomposition. Minimum redundancy maximum relevance algorithm and principal component analysis were used to select key features and reduce the dimension of the feature vectors. The obtained features were classified using the support vector machine, forming two distinct clusters in the baseline knee joint characteristics corresponding to gender, age, body mass index and knee/leg health condition. Thus, this simple, easy‐to‐use, cost-effective, non-invasive and unobtrusive knee monitoring system can be used for real-time evaluation and early diagnoses of joint disorders, fall detection, mobility monitoring and rehabilitation. / Thesis / Master of Applied Science (MASc)

Background: The yearly incidence of people with Spinal Cord Injury (SCI) is between250,000 and 500,000, according to the World Health Organization (WHO). The injury often reduces the ability to walk. Various consequences affect the nervous system and, thus, the entire body. Therefore, the patient population with SCI is highly heterogeneous also in their gait patterns. Multiple tools are used to classify and understand the walking impairments caused by the injury. Objective: To underline the added value brought by the integration of 3D gait analysis to more standard methods (GDI, GPS, GVS, spatiotemporal parameters, ASIAgrade, muscle strength, and spasticity) in the evaluation and interpretation of gait patterns of subjects with SCI. Methods: 3D gait analysis with a passive optical motion capture system (Vicon)and four force plates was performed in 7 control subjects and 3 with SCI. The model used for marker placement and pre-processing was CGM 2.3. Matlab was used to analyze and plot the kinematic and kinetic joints’ data and calculate the GDI, GPS, and GVS indexes and spatiotemporal parameters for subjects with SCI and the control group. A specialized physiotherapist conducted the clinical assessment of the patients with SCI in a rehabilitation center. This included: ASIA grade and review, muscle strength, and spasticity with Daniels Whorthingham scale and Modified Ashworth scale, respectively. The evaluation of the result was qualitative. Results: The integration of 3D gait analysis show further understanding in the assessment of walking impairments. The indexes resumed the impairments and classified the subjects but lacked temporal and functional perspective. Gait phases and spatiotemporal parameters suggested difficulties in stability and balance but could not identify the problem’s origin. Lastly, clinical assessment enlightened the singular motor and sensory function impairments. 3D gait analysis contextualized these results identifying gait patterns and functional implications. Conclusion: Integrating 3D gait analysis might give a deeper understanding of subjects with SCI’s gait strategies and impairments. Indeed this complex technique links the other methods’ results, contextualizing them and gaining information.

Gait analysis is the systematic study of human walking. Clinical gait analysis is the process by which quantitative information is collected for the assessment and decision-making of any gait disorder. Although observational gait analysis is the therapist's primary clinical tool for describing the quality of a patient's walking pattern, it can be very unreliable. Modern gait analysis is facilitated through the use of specialized equipment. Currently, accurate gait analysis requires dedicated laboratories with complex settings and highly skilled operators. Wearable locomotion tracking systems are available, but they are not sufficiently accurate for clinical gait analysis. At the same time, wireless healthcare is evolving. Particularly, ultra wideband (UWB) is a promising technology that has the potential for accurate ranging and positioning in dense multi-path environments. Moreover, impulse-radio UWB (IR-UWB) is suitable for low-power and low-cost implementation, which makes it an attractive candidate for wearable, low-cost, and battery-powered health monitoring systems. The goal of this research is to propose and investigate a full-body wireless wearable human locomotion tracking system using UWB radios. Ultimately, the proposed system should be capable of distinguishing between normal and abnormal gait, making it suitable for accurate clinical gait analysis. / Ph. D.

As a valuable asset in human augmentation and medical rehabilitation, exoskeletons have become a major area for research and development. They have shown themselves to be effective tools for training and rehabilitation of individuals suffering from limited mobility. However, most exoskeletons are not capable of balancing without the assistance of crutches from the user. Leveraging technology and techniques developed for force controlled humanoid robots, a project was undertaken to develop a fully self-balancing, compliant lower-body robotic exoskeleton. Due to their many beneficial features, series elastic actuators were utilized to power the joints on the exoskeleton. This thesis details the development of four linear series elastic actuators (LSEA) as part of this project. All 12-degrees of freedom will be powered by one of these four LSEA's. Actuator requirements were developed by examining human gait data and three robot-walking simulations. These four walking scenarios were synthesized into one set of power requirements for actuator development. Using these requirements, analytical models were developed to perform component trade studies and predict the performance of the actuator. These actuators utilize high-efficacy components, parallel electric motors, and liquid cooling to attain high power-to-weight ratios, while maintaining a small lightweight design. These analyses and trade studies have resulted in the design of a dual-motor liquid-cooled actuator capable of producing a peak force 8500N with a maximum travel speed of 0.267m/s, and three different single-motor actuators capable of producing forces up to 2450N continuously, with a maximum travel speeds up to 0.767m/s. / Master of Science

Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were developed that included Gaussian continuous wavelet transform (CWT) to determine spatiotemporal characteristics, detrended fluctuation analysis (DFA) to examine gait complexity and autocorrelation analyses (ACF) to assess gait regularity. Reliability was examined using repeated running efforts and intraclass correlation. Proof of concept was determined by examining differences in each variable between various running speeds. Applicability was established by examining gait before and after fatiguing activity. The results showed most variables had excellent reliability. Test-retest R2 values for these variables ranged from 0.8 to 1.0. Low reliability was seen in bilateral comparisons of gait symmetry. Increases in running speed resulted in expected changes in spatiotemporal and acceleration variables. Fatiguing exercise had minimal effects on spatiotemporal variables but resulted in noticeable declines in complexity. This investigation shows that GPS-embedded trunk-mounted accelerometers can be effectively used to assess running gait. CWT and DFA yield reliable measures of spatiotemporal characteristics of gait and gait complexity. The effects of running speed and fatigue on these variables provides proof of concepts and applicability for this analytical approach. / Master of Science / Fitness trackers have become widely accessible and easy to use. So much so that athletic teams have been using them to track activity throughout the season. Researchers are able to manipulate data generated from the fitness monitors to assess many different variables including gait. Monitoring gait may generate important information about the condition of the individual. As a person fatigues, running form is theorized to breakdown, which increases injury risk. Therefore the ability to monitor gait may be advantageous in preventing injury. The purpose of this study is to show that the methods in this study are reproducible, respond reasonably to changes in speed, and to observe the changes of gait in the presence of fatigue or on tired legs. Three analyses are used in this study. The first method called autocorrelation, overlays acceleration signals of consecutive foot strikes, and determines the similarity between them. The second method utilizes a wave transformation technique that is able to determine foot contact times. The final method attempts to determine any pattern in the running stride. This method looks for changes in the structure of the pattern. Less structure would indicate a stride that is fatigued. The results showed that the methods of gait analysis used in this study were reproducible and responded appropriately with changes in speed. Small changes in gait were observed due to the presence of fatigue. Further investigation into the use of these methods to determine changes in gait due to the presence of fatigue are warranted.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) and DBS of the pedunculopontine nucleus (PPN) have been shown to be effective surgical therapies for Parkinson’s disease (PD). To better understand the PPN and STN as DBS targets for PD, this research compares the anatomy, electrophysiology, and motor control roles of these nuclei. PPN and STN connections were examined in vivo in human subjects and in the non-human primate using probabilistic diffusion tractography. Both the PPN and STN were connected with each other and with the motor cortex (M1) and basal ganglia. After studying these anatomical connections in primates, their functional significance was further explored in an anesthetized rat model of PD. Examination of the electrophysiological relationship between the PPN and basal ganglia in the presence of slow cortical oscillatory activity suggested that excitatory input from the STN may normally modulate PPN spike timing but that inhibitory oscillatory input from the basal ganglia output nuclei has a greater effect on PPN spike timing in the parkinsonian brain. To examine transmission and modulation of oscillatory activity between these structures at higher frequencies, LFP activity was recorded from the PPN and STN in PD patients performing simple voluntary movements. Movement-related modulation of oscillatory activity predominantly occurred in the α (8-12 Hz) and low β (12-20 Hz) frequencies in the STN but in the high β (20-35 Hz) frequencies in the PPN, supporting observations from rodent studies suggesting that oscillatory activity is not directly transmitted from the STN to the PPN in PD. Finally, to better understand the roles of the STN and PPN in large-scale movement, the effects of STN and PPN DBS on gait abnormalities in PD patients were studied. DBS of the STN appeared to improve gait by optimising executive gait control while DBS of the PPN appeared to restore autonomic gait control. These results have several implications for DBS patient selection, surgical targeting, and for understanding the mechanisms underlying DBS efficacy.