Design, Control, and Implementation of a Robotic Gait Rehabilitation System for Overground Gait Training
Bibliographic record
Abstract
Robotic devices for gait rehabilitation have the potential to improve patient and caregiver safety, reduce therapy costs, and allow a larger number of patients to get access to physical therapy.Additionally, data collected from the robot's sensors may be used to assess impairment severity and track patient progress.However these devices also suffer from many drawbacks such as high cost, complexity, limited training capabilities, and constrained joint motions and postural responses.With these limitations in mind, this thesis introduces GaitEnable, a simply designed robotic gait trainer that combines an intelligent reactive controller, an actuated omnidirectional mobile base and a passive body weight support system.In addition to describing the device and its control system, this thesis also presents results from a series of validation experiments performed to characterize the performance of the device.The results demonstrate that GaitEnable's control system ensures stable humanrobot interactions, and that GaitEnable can assist and perturb a user's gait in a systematic manner.The experiments also confirm that GaitEnable's actuated omni-directional mobile allows users to walk more naturally as it reduces the motion constraints that the device imposes.In addition to describing the GaitEnable system, this thesis also focuses on the more general problem of interaction stability in coupled human-robot systems.Two novel trajectory manipulations for ensuring stable, oscillation-free interactions in admittance-controlled haptic devices are proposed.A Lyapunov stability analysis is used to show that the proposed manipulations are stable in the sense of uniform ultimate boundedness.Also, an extensive set of experiments confirm that the impedance manipulations allow the display of large apparent inertia reductions, ensure stable interactions, and are robust to actuator saturation and model uncertainties.These features make them ideal for use with robotic systems that are attached to humans (e.g., the GaitEnable system).Use of these manipulations is also extended to typical position control problems, and additional experimental results confirm the manipulations help eliminate chatter and provide a better transient response and steady-state tracking error.I would like to thank my supervisor Dr. Mojtaba Ahmadi for his mentorship, guidance, and friendship over the many years I spent at Carleton.Whenever I was stuck, I always knew that I could knock on his door and immediately be invited in.More than that, I will be forever grateful to him for affording me the freedom to pursue what I was most passionate about.Opportunities like that are rare, and I'm thankful that he had enough faith in me to let me explore without boundaries.I also want to thank the staff in the MAE office and at the machine shop for their assistance with the multitude of requests I inevitably came to them with over the years.Finally, I also want to thank the numerous lab mates who I've had the pleasure of spending time with, and being friends with over the last five years.It was a long road to get here, and your suggestions, help and company made it possible.v improves patient safety and reduces therapist workload. MotivationImmobility can lead to accelerated bone and muscle loss, sensory deprivation, isolation, delerium and incontinence [5].Short bouts of immobility, e.g., even 5-10 days of bed rest during a hospitalization, can contribute to an increased risk of mortality and significant impairments in an individual's long-term ability for self-care and locomotion [6].Many of these hazards can be avoided by timely and sufficient gait rehabilitation therapy [6][7][8][9][10].Fundamentally, the key to faster recovery is to walk early, typically within 24-48 hours of a major surgery or acute illness.Many studies show that the simple act of walking 15-25 minutes a day within 48 hours of an acute illness or surgery can improve outcomes and reduce hospitalization stays by 2-5 days [6][7][8][9][10].Therefore, tools and practices that encourage users to walk earlier during their hospital stay are critical.The impact of early gait rehabilitation is substantial since over 5,000,000 orthopaedic, post-surgical and acute cardiovascular, respiratory, and neurological inpatients can benefit from this therapy [11].While the benefits are clear, two key barriers limit the widespread practice of early mobilization: i) patient and caregiver safety concerns; and ii), staffing limitations.Physical and cognitive impairments caused by an illness or medication/sedation can leave many patients at a heightened risk for falls.These falls are a serious problem since nearly 23% of falls result in serious injuries such as hip fractures [12].Additionally, serious falls can increase patient care costs by nearly $35,000 and result in millions in litigation costs [12].Accordingly, tools that facilitate early mobility must be designed to prevent falls and reduce a patient's risk of injury during their training.Caregiver injuries also pose an impediment to the delivery of gait rehabilitation therapy.Support staff such as nurses, nurses' aides, and attendants have the highest Chapter 5 -Experiments with GaitEnableThis chapter investigates whether GaitEnable's powered omnidirectional mobile base can reduce motion constraints and generate force cues for influencing a user's gait.Results from experiments performed by seven healthy subjects confirm that GaitEnable's powered mobile base is capable of masking the device's inertial properties, and that the control system is capable of transmitting force cues for assisting or perturbing a user's gait.Additional results also show the value of using a powered mobile base in comparison to using castors, and GaitEnable's ability to minimize motion constraints during free walking. Chapter 6 -Conclusions and RecommendationThis chapter summarizes the research and discusses possible improvements and directions for future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".