A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion&-extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm&-exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient's intention.The research was funded by RoboHealth (DPI2013-47944-C4-3-R) and the EDAM (DPI2016-75346-R) Spanish research projects

Shape Memory Alloy (SMA)-Based Exoskeletons for Upper Limb Rehabilitation

This contribution presents the advances in the use of flexible Shape Memory Alloy (SMA)-based actuators for the development of upper limb rehabilitation exoskeletons that have been carried out by our research group. The actuator features developed by our research group maintain the SMA wire characteristics (low-weight, low-cost, noiseless operation, compact, and simplicity) and additionally presents the flexibility and its increase the work frequency. These characteristics make that its integration in rehabilitation exoskeletons provides the user more comfort, easy to use, and freedom of movement. The chapter describes some different rigid and soft rehabilitation exoskeletons for different joints such as the elbow, wrist, and hand in which this type of actuator has been successfully integrated. This gives the possibilities to expand the research line with the actuated soft exosuits systems, in a future development perspective

Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed,3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are usedcoherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus,the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional liftto provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper

Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed,3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are usedcoherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus,the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional liftto provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper

Assistance Robotics and Biosensors

This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including: Affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control and advances in brain–machine interfacing

Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms

BACKGROUND AND OBJECTIVE: Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy. METHODS: A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained. RESULTS: Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models. CONCLUSION: The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer

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

XXII International Conference on Mechanics in Medicine and Biology - Abstracts Book

This book contain the abstracts presented the XXII ICMMB, held in Bologna in September 2022. The abstracts are divided following the sessions scheduled during the conference

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