Development of a Wearable Mechatronic Elbow Brace for Postoperative Motion Rehabilitation

This thesis describes the development of a wearable mechatronic brace for upper limb rehabilitation that can be used at any stage of motion training after surgical reconstruction of brachial plexus nerves. The results of the mechanical design and the work completed towards finding the best torque transmission system are presented herein. As part of this mechatronic system, a customized control system was designed, tested and modified. The control strategy was improved by replacing a PID controller with a cascade controller. Although the experiments have shown that the proposed device can be successfully used for muscle training, further assessment of the device, with the help of data from the patients with brachial plexus injury (BPI), is required to improve the control strategy. Unique features of this device include the combination of adjustability and modularity, as well as the passive adjustment required to compensate for the carrying angle

Development of Digital Control Systems for Wearable Mechatronic Devices: Applications in Musculoskeletal Rehabilitation of the Upper Limb

The potential for wearable mechatronic systems to assist with musculoskeletal rehabilitation of the upper limb has grown with the technology. One limiting factor to realizing the benefits of these devices as motion therapy tools is within the development of digital control solutions. Despite many device prototypes and research efforts in the surrounding fields, there are a lack of requirements, details, assessments, and comparisons of control system characteristics, components, and architectures in the literature. Pairing this with the complexity of humans, the devices, and their interactions makes it a difficult task for control system developers to determine the best solution for their desired applications. The objective of this thesis is to develop, evaluate, and compare control system solutions that are capable of tracking motion through the control of wearable mechatronic devices. Due to the immaturity of these devices, the design, implementation, and testing processes for the control systems is not well established. In order to improve the efficiency and effectiveness of these processes, control system development and evaluation tools have been proposed. The Wearable Mechatronics-Enabled Control Software framework was developed to enable the implementation and comparison of different control software solutions presented in the literature. This framework reduces the amount of restructuring and modification required to complete these development tasks. An integration testing protocol was developed to isolate different aspects of the control systems during testing. A metric suite is proposed that expands on the existing literature and allows for the measurement of more control characteristics. Together, these tools were used ii ABSTRACT iii to developed, evaluate, and compare control system solutions. Using the developed control systems, a series of experiments were performed that involved tracking elbow motion using wearable mechatronic elbow devices. The accuracy and repeatability of the motion tracking performances, the adaptability of the control models, and the resource utilization of the digital systems were measured during these experiments. Statistical analysis was performed on these metrics to compare between experimental factors. The results of the tracking performances show some of the highest accuracies for elbow motion tracking with these devices. The statistical analysis revealed many factors that significantly impact the tracking performance, such as visual feedback, motion training, constrained motion, motion models, motion inputs, actuation components, and control outputs. Furthermore, the completion of the experiments resulted in three first-time studies, such as the comparison of muscle activation models and the quantification of control system task timing and data storage needs. The successes of these experiments highlight that accurate motion tracking, using biological signals of the user, is possible, but that many more efforts are needed to obtain control solutions that are robust to variations in the motion and characteristics of the user. To guide the future development of these control systems, a national survey was conducted of therapists regarding their patient data collection and analysis methods. From the results of this survey, a series of requirements for software systems, that allow therapists to interact with the control systems of these devices, were collected. Increasing the participation of therapists in the development processes of wearable assistive devices will help to produce better requirements for developers. This will allow the customization of control systems for specific therapies and patient characteristics, which will increase the benefit and adoption rate of these devices within musculoskeletal rehabilitation programs

Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

The Research on Soft Pneumatic Actuators in Italy: Design Solutions and Applications

Interest in soft actuators has increased enormously in the last 10 years. Thanks to their compliance and flexibility, they are suitable to be employed to actuate devices that must safely interact with humans or delicate objects or to actuate bio-inspired robots able to move in hostile environments. This paper reviews the research on soft pneumatic actuators conducted in Italy, focusing on mechanical design, analytical modeling, and possible application. A classification based on the geometry is proposed, since a wide set of architectures and manufacturing solutions are available. This aspect is confirmed by the extent of scenarios in which researchers take advantage of such systems’ improved flexibility and functionality. Several applications regarding bio-robotics, bioengineering, wearable devices, and more are presented and discussed

Towards the Development of a Wearable Tremor Suppression Glove

Patients diagnosed with Parkinson’s disease (PD) often associate with tremor. Among other symptoms of PD, tremor is the most aggressive symptom and it is difficult to control with traditional treatments. This thesis presents the assessment of Parkinsonian hand tremor in both the time domain and the frequency domain, the performance of a tremor estimator using different tremor models, and the development of a novel mechatronic transmission system for a wearable tremor suppression device. This transmission system functions as a mechatronic splitter that allows a single power source to support multiple independent applications. Unique features of this transmission system include low power consumption and adjustability in size and weight. Tremor assessment results showed that the hand tremor signal often presents a multi-harmonics pattern. The use of a multi-harmonics tremor model produced a better estimation result than using a monoharmonic tremor model

Motion Intention Estimation using sEMG-ACC Sensor Fusion

Musculoskeletal injuries can severely impact the ability to produce and control body motion. In order to regain function, rehabilitation is often required. Wearable smart devices are currently under development to provide therapy and assistance for people with impaired arm function. Electromyography (EMG) signals are used as an input to pattern recognition systems to determine intended movements. However, there is a gap between the accuracy of pattern recognition systems in constrained laboratory settings, and usability when used for detecting dynamic unconstrained movements. Motion factors such as limb position, interaction force, and velocity, are known to have a negative impact on the pattern recognition. A possible solution lies in the use of data from other sensors along with the EMG signals, such as signals from accelerometers (ACC), in the training and use of classifiers in order to improve classification accuracy. The objectives of this study were to quantify the impact of motion factors on ACC signals, and to use these ACC signals along with EMG signals for classifying categories of motion factors. To address these objectives, a dataset containing EMG and ACC signals while individuals performed unconstrained arm motions was studied. Analyses of the EMG and accelerometer signals and their use in training classification models to predict characteristics of intended motion were completed. The results quantify how accelerometer features change with variations in arm position, interaction forces, and motion velocities. The results also show that the combination of EMG and ACC data have relatively increased the accuracy of motion intention detection. Velocity could be distinguished between stationary and moving with less than 10% error using a Decision Tree ensemble classifier. Future work should expand on motion factors and EMG-ACC sensor fusion to identify interactions between a person and the environment, in order to guide tuning of control models working towards controlling wearable mechatronic devices during dynamic movements

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

Mobile Mechatronic/Robotic Orthotic Devices to Assist–Rehabilitate Neuromotor Impairments in the Upper Limb: A Systematic and Synthetic Review

This paper overviews the state-of-the-art in upper limb robot-supported approaches, focusing on advancements in the related mechatronic devices for the patients' rehabilitation and/or assistance. Dedicated to the technical, comprehensively methodological and global effectiveness and improvement in this inter-disciplinary field of research, it includes information beyond the therapy administrated in clinical settings-but with no diminished safety requirements. Our systematic review, based on PRISMA guidelines, searched articles published between January 2001 and November 2017 from the following databases: Cochrane, Medline/PubMed, PMC, Elsevier, PEDro, and ISI Web of Knowledge/Science. Then we have applied a new innovative PEDro-inspired technique to classify the relevant articles. The article focuses on the main indications, current technologies, categories of intervention and outcome assessment modalities. It includes also, in tabular form, the main characteristics of the most relevant mobile (wearable and/or portable) mechatronic/robotic orthoses/exoskeletons prototype devices used to assist-rehabilitate neuromotor impairments in the upper limb

Multiobjective optimization of a cable-driven robot for wrist rehabilitation using a genetic algorithm

This paper presents a multi-objective optimization using a genetic algorithm to reduce the size of the current configuration of a cable-driven end-effector robot for wrist rehabilitation. The objective of this work is to obtain a smaller robot with good performance by employing a constrained genetic algorithm (GA) so the device can be light and wearable. The optimization was performed to study the effect of the robot dimensions and in the end, a new solution that can reduce the robot size by 35% and increase the performance by 1.8% was found

Design and Motion Control of a Lower Limb Robotic Exoskeleton

This chapter presents the results of research work on design, actuator selection and motion control of a lower extremity exoskeleton developed to provide legged mobility to spinal cord injured (SCI) individuals. The exoskeleton has two degrees of freedom per leg. Hip and knee joints are actuated in the sagittal plane by using DC servomotors. Additional effort supplied by user’s arms through crutches is defined as user support rate (USR). Experimentally determined USR values are considered in actuator torque computations for achieving a realistic actuator selection. A custom-embedded system is used to control exoskeleton. Reference joint trajectories are determined by using clinical gait analysis (CGA). Three-loop cascade controllers with current, velocity and position feedback are designed for controlling the joint motions of the exoskeleton. A non-linear ARX model is used to determine controller parameters. Overall performance and an assistive effect of WSE-2 are experimentally investigated by conducting tests with a paraplegic patient with T10 complete injury