Robotics rehabilitation of the elbow based on surface electromyography signals

Physical rehabilitation based on robotic systems has the potential to cover the patient’s need of improvement of upper extremity functionalities. In this article, the state of the art of resistant and assistive upper limb exoskeleton robots and their control are thoroughly investigated. Afterward, a single-degree-of-freedom exoskeleton matching the elbow–forearm has been advanced to grant a valid rehabilitation therapy for persons with physical disability of upper limb motion. The authors have focused on the control system based on the use of electromyography signals as an input to drive the joint movement and manage the robotics arm. The correlation analysis between surface electromyography signal and the force exerted by the subject was studied in objects’ grasping tests with the purpose of validating the methodology. The authors developed an innovative surface electromyography force–based active control that adjusts the force exerted by the device during rehabilitation. The control was validated by an experimental campaign on healthy subjects simulating disease on an arm, with positive results that confirm the proposed solution and that open the way to future researches

Feature Analysis for Classification of Physical Actions using surface EMG Data

Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this scenario, a successful methodology is to use the EMG signal based information to control the support robotics. For this mechanism to function properly, the EMG signal from the muscles has to be sensed and then the biological motor intention has to be decoded and finally the resulting information has to be communicated to the controller of the robot. An accurate detection of the motor intention requires a pattern recognition based categorical identification. Hence in this paper, we propose an improved classification framework by identification of the relevant features that drive the pattern recognition algorithm. Major contributions include a set of modified spectral moment based features and another relevant inter-channel correlation feature that contribute to an improved classification performance. Next, we conducted a sensitivity analysis of the classification algorithm to different EMG channels. Finally, the classifier performance is compared to that of the other state-of the art algorithm

Coordination Control of a Dual-Arm Exoskeleton Robot Using Human Impedance Transfer Skills

This paper has developed a coordination control method for a dual-arm exoskeleton robot based on human impedance transfer skills, where the left (master) robot arm extracts the human limb impedance stiffness and position profiles, and then transfers the information to the right (slave) arm of the exoskeleton. A computationally efficient model of the arm endpoint stiffness behavior is developed and a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. A reference command consisting of the stiffness and position profiles of the operator is computed and realized by one robot in real-time. Considering the dynamics uncertainties of the robotic exoskeleton, an adaptive-robust impedance controller in task space is proposed to drive the slave arm tracking the desired trajectories with convergent errors. To verify the robustness of the developed approach, a study of combining adaptive control and human impedance transfer control under the presence of unknown interactive forces is conducted. The experimental results of this paper suggest that the proposed control method enables the subjects to execute a coordination control task on a dual-arm exoskeleton robot by transferring the stiffness from the human arm to the slave robot arm, which turns out to be effective

Detection of intention level in response to task difficulty from EEG signals

We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI). In particular, we use linear discriminant analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights. We observe that it is possible to classify tasks of varying difficulty based on EEG signals. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. Our experimental results suggest that it is possible to extract the intention level information from EEG signals in response to task difficulty and indicate some level of correlation between EEG and EMG. With a view towards detecting patients' intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

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

Development of Arm Exo-skeleton for Bicep Brachii Muscle

This report presents the study on design of arm exoskeleton for stroke rehabilitation purpose. The mechanical design of the exoskeleton focuses on few aspects of the arm exoskeleton which are length and the design of the exoskeleton and motor specification. Besides, the experiment of obtaining surface electromyography (sEMG) signal for repetition training for physiotherapy patient purpose is carried out to observe the difference in amplitude and muscle signal of different subjects (four males and four females) due to the amount of training and the angle of the training. The signals are filtered and the average of the root mean square of the data is compared

Adaptive Compliance Shaping with Human Impedance Estimation

Human impedance parameters play an integral role in the dynamics of strength amplification exoskeletons. Many methods are used to estimate the stiffness of human muscles, but few are used to improve the performance of strength amplification controllers for these devices. We propose a compliance shaping amplification controller incorporating an accurate online human stiffness estimation from surface electromyography (sEMG) sensors and stretch sensors connected to the forearm and upper arm of the human. These sensor values along with exoskeleton position and velocity are used to train a random forest regression model that accurately predicts a person's stiffness despite varying movement, relaxation, and muscle co-contraction. Our model's accuracy is verified using experimental test data and the model is implemented into the compliance shaping controller. Ultimately we show that the online estimation of stiffness can improve the bandwidth and amplification of the controller while remaining robustly stable.Comment: 8 pages, 9 figures, Accepted for publication at the 2020 American Control Conference. Copyright IEEE 202

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