Sensor-Based Adaptive Control and Optimization of Lower-Limb Prosthesis.

Recent developments in prosthetics have enabled the development of powered prosthetic ankles (PPA). The advent of such technologies drastically improved impaired gait by increasing balance and reducing metabolic energy consumption by providing net positive power. However, control challenges limit performance and feasibility of today’s devices. With addition of sensors and motors, PPA systems should continuously make control decisions and adapt the system by manipulating control parameters of the prostheses. There are multiple challenges in optimization and control of PPAs. A prominent challenge is the objective setup of the system and calibration parameters to fit each subject. Another is whether it is possible to detect changes in intention and terrain before prosthetic use and how the system should react and adapt to it. In the first part of this study, a model for energy expenditure was proposed using electromyogram (EMG) signals from the residual lower-limbs PPA users. The proposed model was optimized to minimize energy expenditure. Optimization was performed using a modified Nelder-Mead approach with a Latin Hypercube sampling. Results of the proposed method were compared to expert values and it was shown to be a feasible alternative for tuning in a shorter time. In the second part of the study, the control challenges regarding lack of adaptivity for PPAs was investigated. The current PPA system used is enhanced with impedance-controlled parameters that allow the system to provide different assistance. However, current systems are set to a fixed value and fail to acknowledge various terrain and intentions throughout the day. In this study, a pseudo-real-time adaptive control system was proposed to predict the changes in the gait and provide a smoother gait. The proposed control system used physiological, kinetic, and kinematic data and fused them to predict the change. The prediction was done using machine learning-based methods. Results of the study showed an accuracy of up to 89.7 percent for prediction of change for four different cases

Improving Intelligence of Robotic Lower-Limb Prostheses to Enhance Mobility for Individuals with Limb Loss

The field of wearable robotics is an emerging field that seeks to create smarter and intuitive devices that can assist users improve their overall quality of life. Specifically, individuals with lower limb amputation tend to have significantly impaired mobility and asymmetric gait patterns that result in increased energy expenditure than able-bodied individuals over a variety of tasks. Unfortunately, most of the commercial devices are passive and lack the ability to easily adapt to changing environmental contexts. Powered prostheses have shown promise to help restore the necessary power needed to walk in common ambulatory tasks. However, there is a need to infer/detect the user's movement to appropriately provide seamless and natural assistance. To achieve this behavior, a better understanding is required of adding intelligence to powered prostheses. This dissertation focuses on three key research objectives: 1) developing and enhancing offline intent recognition systems for both classification and regression tasks using embedded prosthetic mechanical sensors and machine learning, 2) deploying intelligent controllers in real-time to directly modulate assistive torque in a knee and ankle prosthetic device, and 3) quantifying the biomechanical and clinical effects of a powered prosthesis compared to a passive device. The findings conducted show improvement in developing powered prostheses to better enhance mobility for individuals with transfemoral amputation and show a step forward towards clinical acceptance.Ph.D

Simulation And Control At the Boundaries Between Humans And Assistive Robots

Human-machine interaction has become an important area of research as progress is made in the fields of rehabilitation robotics, powered prostheses, and advanced exercise machines. Adding to the advances in this area, a novel controller for a powered transfemoral prosthesis is introduced that requires limited tuning and explicitly considers energy regeneration. Results from a trial conducted with an individual with an amputation show self-powering operation for the prosthesis while concurrently attaining basic gait fidelity across varied walking speeds. Experience in prosthesis development revealed that, though every effort is made to ensure the safety of the human subject, limited testing of such devices prior to human trials can be completed in the current research environment. Two complementary alternatives are developed to fill that gap. First, the feasibility of implementing impulse-momentum sliding mode control on a robot that can physically replace a human with a transfemoral amputation to emulate weight-bearing for initial prototype walking tests is established. Second, a more general human simulation approach is proposed that can be used in any of the aforementioned human-machine interaction fields. Seeking this general human simulation method, a unique pair of solutions for simulating a Hill muscle-actuated linkage system is formulated. These include using the Lyapunov-based backstepping control method to generate a closed-loop tracking simulation and, motivated by limitations observed in backstepping, an optimal control solver based on differential flatness and sum of squares polynomials in support of receding horizon controlled (e.g. model predictive control) or open-loop simulations. v The backstepping framework provides insight into muscle redundancy resolution. The optimal control framework uses this insight to produce a computationally efficient approach to musculoskeletal system modeling. A simulation of a human arm is evaluated in both structures. Strong tracking performance is achieved in the backstepping case. An exercise optimization application using the optimal control solver showcases the computational benefits of the solver and reveals the feasibility of finding trajectories for human-exercise machine interaction that can isolate a muscle of interest for strengthening

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Exploring thermal discomfort amongst lower-limb prosthesis wearers

Amongst lower-limb prosthesis wearers, thermal discomfort is a common problem with an estimated prevalence of more than 50%. Overheating does not just create discomfort to the user, but it has been linked to excessive sweating, skin damage caused by a moist environment and friction. Due to impermeable prosthetic components and a warm moist environment, minor skin damage can result in skin infections that can lead to prosthesis cessation, increased social anxiety, isolation and depression. Despite the seriousness of thermal discomfort, few studies explore the issue, with research predominantly constrained to controlled laboratory scenarios, with only one out of laboratory study. In this thesis, studies investigate how thermal discomfort arises and what are the consequences of thermal discomfort for lower-limb prosthesis wearers. Research studies are designed around the principles of presenting lived experiences of the phenomenon and conducting research in the context of participants' real-life activities. A design exploration chapter investigates modifying liner materials and design to create a passive solution to thermal discomfort. However, this approach was found to be ineffective and unfeasible. Study 1 presents a qualitative study which investigates the user experience of a prosthesis, thermal discomfort and related consequences. Study 2 explores limb temperature of male amputees inside and outside the laboratory, with the latter also collecting perceived thermal comfort (PTC) data. Finally, Study 3 investigates thermal discomfort in the real-world and tracks limb temperature, ambient conditions, activities, and experience sampling of PTC. While there were no apparent relationships presented in sensor data, qualitative data revealed that in situations where prosthesis wearers perceived a lack of control, thermal discomfort seemed to be worse. When combined, the studies create two knowledge contributions. Firstly, the research provides a methodological contribution showing how to conduct mixed-methods research to obtain rich insights into complex prosthesis phenomena. Secondly, the research highlights the need to appreciate psychological and contextual factors when researching prosthesis wearer thermal comfort. The research contributions are also converted into an implication for prosthesis design. The concept of 'regaining control' to psychologically mitigate thermal discomfort could be incorporated into technologies by using 'on-demand' thermal discomfort relief, rather than 'always-on' solutions, as have been created in the past

Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the “ExoNet” database was developed – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called “NetScore”, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance