PREDICTIVE SIMULATION OF HUMAN MOVEMENT AND APPLICATIONS TO ASSISTIVE DEVICE DESIGN AND CONTROL

Predictive simulation based on dynamic optimization using musculoskeletal models is a powerful approach for studying biomechanics of human gait. Predictive simulation can be used for a variety of applications from designing assistive devices to testing theories of motor controls. However, one of the challenges in formulating the predictive dynamic optimization problem is that the cost function, which represents the underlying goal of the walking task (e.g., minimal energy consumption) is generally unknown and is assumed a priori. While different studies used different cost functions, the qualities of the gaits with those cost functions were often not provided. Therefore, this dissertation evaluates and examines different cost function forms for dynamic simulation of human walking. The problem of the walking cost function determination was cast as a bilevel optimization, which was solved using a nested evolutionary approach. The results showed cost functions based on a weighted combination of muscle-based performance criteria (e.g., metabolic cost, muscle fatigue), gait smoothness, and gait stability led to better walking solutions compared to any cost functions only based on muscle performance criteria. Further evaluations of the walking cost functions were done in the simulation cases of human walking augmented with assistive devices such as prosthesis and exoskeleton. The simulations of augmented walking were comparable to the experimental results, which suggests the potential of using the simulation approach to address problems of finding assistive device design and control

Developments in smart multi-function gait assistive devices for the prevention and treatment of knee osteoarthritis—a literature review

Knee osteoarthritis (OA) is a degenerative condition that critically affects locomotor ability and quality of life and, the condition is particularly prevalent in the senior population. The current review presents a gait biomechanics conceptual framework for designing active knee orthoses to prevent and remediate knee OA. Constant excessive loading diminishes knee joint articular cartilage and, therefore, measures to reduce kinetic stresses due to the fact of adduction moments and joint compression are an essential target for OA prevention. A powered orthosis enables torque generation to support knee joint motions and machine-learning-driven “smart systems” can optimise the magnitude and timing of joint actuator forces. Although further research is required, recent findings raise the possibility of exoskeleton-supported, non-surgical OA interventions, increasing the treatment options for this prevalent, painful and seriously debilitating disease. Combined with advances in regenerative medicine, such as stem cell implantation and manipulation of messenger ribonucleic acid (m-RNA) transcription, active knee orthoses can be designed to incorporate electromagnetic stimulators to promote articular cartilage resynthesis

Robotic Home-Based Rehabilitation Systems Design: From a Literature Review to a Conceptual Framework for Community-Based Remote Therapy During COVID-19 Pandemic

During the COVID-19 pandemic, the higher susceptibility of post-stroke patients to infection calls for extra safety precautions. Despite the imposed restrictions, early neurorehabilitation cannot be postponed due to its paramount importance for improving motor and functional recovery chances. Utilizing accessible state-of-the-art technologies, home-based rehabilitation devices are proposed as a sustainable solution in the current crisis. In this paper, a comprehensive review on developed home-based rehabilitation technologies of the last 10 years (2011–2020), categorizing them into upper and lower limb devices and considering both commercialized and state-of-the-art realms. Mechatronic, control, and software aspects of the system are discussed to provide a classified roadmap for home-based systems development. Subsequently, a conceptual framework on the development of smart and intelligent community-based home rehabilitation systems based on novel mechatronic technologies is proposed. In this framework, each rehabilitation device acts as an agent in the network, using the internet of things (IoT) technologies, which facilitates learning from the recorded data of the other agents, as well as the tele-supervision of the treatment by an expert. The presented design paradigm based on the above-mentioned leading technologies could lead to the development of promising home rehabilitation systems, which encourage stroke survivors to engage in under-supervised or unsupervised therapeutic activities

Wearable exoskeletons to support ambulation in people with neuromuscular diseases, design rules and control

Neuromuscular diseases are degenerative and, thus far, incurable disorders that lead to large muscle wasting. They result in constant deterioration of activities of daily living and in particular of ambulation. Some common types include Duchenne muscular dystrophy, Charcot-Marie-Tooth disease, polymyositis and amyotrophic lateral sclerosis. While these diseases individually have a low rate of occurrence and are mostly unknown to most people, collectively they affect a significant part of the population. About 1 person in 2000 suffer from neuromuscular diseases, which means an approximate total of 370â000 people over the European continent. Recent technology breakthroughs have made possible the realization of advanced powered orthotics, which are commonly called exoskeletons. The most advanced devices have successfully been able to support patients in walking despite a debilitating condition such as complete spinal cord injury. Such technology could be ideal for people with mid-stage neuromuscular diseases as it provides more mobility and independence. This work investigates the definitions and requirements that would need to be fulfilled for any proposed orthotic device to assist people living with neuromuscular diseases. To define the needs of patients with neuromuscular disease, a large literature review is conducted on gait compensation patterns. The research also includes the data collection of experimental gait measurements from fourteen people with heterogeneous neuromuscular diseases. Conclusions show that orthotics for people with neuromuscular diseases require tunable assistance at each joint and a collaborative control strategy in order to let the user control motion. Eventually, most people may not be able to use crutches. A full lower limb exoskeleton, AUTONOMYO, is designed, realized and evaluated. A particular attention is put on the optimization of the actuator and transmission units. In order to reduce the effects of inertia and weight of those units, a design is explored with actuation remotely located from the joints. The transmission is realized by custom cable wire and pulley systems, combined with standard planetary gears. The dynamics of different coupling between the hip and the knee flexion/extension joints are explored, and their benefits and tradeoffs analyzed. A novel control strategy based on a finite-state active impedance model is designed and implemented on the AUTONOMYO device. The controller consists of three states of different active impedances mimicking a visco-elastic behavior. The switching condition between states is uniquely based on the hip flexion velocity to detect the user intent. The performance of the strategy regarding the detection of intention and the modulation of the assistance is evaluated on a test bench and in real conditions with healthy pilots and with a person with limb girdle muscular dystrophy. The preliminary results are promising since all pilots (including the one with muscular dystrophy) are able to initiate and terminate assisted walking on demand. They are all able both to walk with a good stride rate and to reach moderate velocities. Healthy pilots are able to ambulate alone with the exoskeleton, while the pilot with muscular dystrophy requires human assistance for the management of balance

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

A Study on Active/Passive Pneumatic Actuators for Assistive Systems

The need for intelligent assistive devices is growing. Due to advances in medicine, people are living longer and able to recover from severe neurological incidents, resulting in an increased population with neuromuscular weakness. In workplaces such as assembly lines, there is a high possibility of work-related fatigue or injury, such as when workers squat down or lift their arms during their work tasks. Assistive devices could help remedy loss of strength on their extremities as well as keep the work environment safe and productive, allowing these growing segments of the population in need of the devices to live more self-sufficient, productive, and higher-quality lives.In the design of assistive systems, an important design goal is prolonged operational time, which requires the minimum usage of energy. Energy consumption can be reduced by modifying the mechanical characteristics of assistive systems according to the dynamic characteristics of the human body, which vary considerably between tasks. This dissertation investigates 1) the design of actuators with adjustable mechanical impedance, 2) control strategies to search for, and adjust to, a suitable mechanical impedance for assistance and 3) sensing technologies for classifying the tasks in which the human engages.The first part of this dissertation characterizes a pneumatic variable stiffness actuator named an Active/Passive Pneumatic Actuator (AP2A). The actuator consists of an air cylinder and an array of solenoid valves. These valves and the corresponding switching algorithms tune the chamber pressures and make the AP2A function as a mechanical spring with desired stiffness. The actuator has a low mechanical impedance compared to geared motors, which enables it to achieve efficient interaction. Control strategies of an assistive system with the AP2A are discussed in the second part. This control framework utilizes the characteristics of the AP2A to provide assistance when necessary and to operate transparently (i.e., neither to assist nor to disturb the users) otherwise. Energy consumed by the AP2A and the assisted system is minimized by solving an optimal control problem. Finally, an estimator is introduced to detect assistive timing for the assistive system with the AP2A. This estimator utilizes physiological signals such as surface electromyogram and prior knowledge of a muscular model, classifying if the user is under the specified condition to be assisted by the AP2A. It demonstrates that the user's effort can be saved, also reducing the number of procedures to collect training data for the estimator before using assistive systems. The performance of the actuator, the controller, and the estimator proposed in this dissertation are verified through experiments.From the above, this dissertation contributes to developing the AP2A that provides assistance and saves energy usage of assistive systems by working as a mechanical spring with stiffness optimized for achieving effective interaction under specific conditions. This actuator supports assistive devices that can be deployed in the real world, properly assisting the users when needed

Principles of energy optimization underlying human walking gait adaptations

Learning to move in novel situations is a complex process. We need to continually learn the changing situations and determine the best way to move. Optimization is a widely accepted framework for this process. However, little is known about algorithms used by the nervous system to perform this optimization. Our lab recently found evidence that people can continuously optimize energy during walking. My goal in this thesis is to identify principles of optimization, particularly energy optimization in walking, that govern our choice of movement in novel situations. I used two novel walking tasks for this purpose. For the first task, I designed, built, and tested a mechatronic system that can quickly, accurately, and precisely apply forces to a user’s torso. It changes the relationship between a walking gait and its associated energetic cost—cost landscape—to shift the energy optimal walking gait. Participants shift their gait towards the new optimum in these landscapes. In my second project, I aimed to understand how the nervous system identifies when to initiate optimization. I used my system to create cost landscapes of three different cost gradients. I found that experiencing a steeper cost gradient through natural variability is not sufficient to cue the nervous system to initiate optimization. For my third and fourth projects, I used the task of split-belt walking. I collaborated with another research group to analyse the mechanics and energetics of walking with different step lengths on a split-belt treadmill. I found that people can harness energy from a split-belt treadmill by placing their leading leg further forward on the fast belt, and that there may be an energy optimal gait. In my fourth project, I used computer modelling to identify that there may exist an energy optimal gait due to the trade-off between the cost of swinging the leg and the cost of redirecting the body center of mass when transitioning from step to step. Together, these projects develop a new system and a new approach to understand energy optimization in walking. They uncover principles governing the initiation of this process and our ability to benefit from it