Interaction with a hand rehabilitation exoskeleton in EMG-driven bilateral therapy: Influence of visual biofeedback on the users’ performance

Producción CientíficaThe effectiveness of EMG biofeedback with neurorehabilitation robotic platforms has not been previously addressed. The present work evaluates the influence of an EMG-based visual biofeedback on the user performance when performing EMG-driven bilateral exercises with a robotic hand exoskeleton. Eighteen healthy subjects were asked to perform 1-min randomly generated sequences of hand gestures (rest, open and close) in four different conditions resulting from the combination of using or not (1) EMG-based visual biofeedback and (2) kinesthetic feedback from the exoskeleton movement. The user performance in each test was measured by computing similarity between the target gestures and the recognized user gestures using the L2 distance. Statistically significant differences in the subject performance were found in the type of provided feedback (p-value 0.0124). Pairwise comparisons showed that the L2 distance was statistically significantly lower when only EMG-based visual feedback was present (2.89 ± 0.71) than with the presence of the kinesthetic feedback alone (3.43 ± 0.75, p-value = 0.0412) or the combination of both (3.39 ± 0.70, p-value = 0.0497). Hence, EMG-based visual feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real time. This type of feedback could benefit patients in learning more quickly how to activate robot functions, increasing their motivation towards rehabilitation.Ministerio de Ciencia e Innovación - (project RTC2019-007350-1)Consejería de Educación, Fondo Social Europeo, Gobierno Vasco - (BERC 2022-2025) y (project 3KIA (KK-2020/00049)Ministerio de Ciencia, Innovación y Universidades - (BCAM Severo Ochoa: SEV-2017-0718

Neurorehabilitation of the hand using the cybergrasp[TM] and mirror image

In recent years, researchers have explored the use of a mirror image as a means of rehabilitation for individuals suffering from hemiparesis. Through neuroimaging and functional testing, neurological improvement has been demonstrated in those that engage in mirror therapy. Bilateral training, or simultaneous movement of both sides of the body, has also been studied as a treatment method to improve function after cerebral vascular accident. The development of robotic systems to assist movement of the human body has played a major role in the fabrication of bilateral training devices. In this experiment, the CyberGrasp™ robotic exoskeleton was used to assist the paretic hand in simultaneous bilateral movement in three subjects more than 1 year post stroke. While the bilateral motion took place, the subject viewed a mirror image of their unaffected hand superimposed on their impaired hand. Results at the end of 2 weeks showed no major change in active digit extension, but a noted decrease in the stretch reflex and clinically significant improvements on the Jebsen Test of Hand Function. The system resulted in no major side effects. In conclusion, robot-assisted bilateral training in conjunction with mirror therapy may be a helpful treatment in patients suffering from hemiparesis due to neurological impairment. The experiment conducted demonstrated the feasibility of the system to be used in further research

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

Robotic Rehabilitation System In Malaysia

The goal of this project entitled Robotic Rehabititation System in Malaysia is to examine the purpose of robotics to therapeutic procedures for achieving the finest possible motor and functional recovery for persons with impairments following various diseases such as amputations, life-threatening wounds, brain injury, pain management issues, orthopaedics, pulmonary, spinal cord injuries and strokes. Feasibility study and research concerning robotic rehabilitation system iue prepared for the development of robotic based rehabilitation system in Malaysia to be fulfilled. However, there are significant research challenges in developing and testing rehabilitation robots so that they meet the requirements of the patients. The technology must be capable of improving person's impaired limbs or part of the body. In addition, robots must be able to understand the complexity of human type of movements. Thus, non-robotic rehabilitation centre can be transformed to a robotic based rehabilitation centre by analysing the possibility of transforming the current practice of rehabilitation programs conducted via physiotherapist to an automated rehabilitation activity by means of robot follows with good evidence on how robots might enhance the delivery of robotic rehabilitation to people of all ages

Hand Gestures Replicating Robot Arm based on MediaPipe

A robotic arm is any variety of programmable mechanical devices designed to operate items like a human arm and is one of the most beneficial innovations of the 20th century, quickly becoming a cornerstone of many industries. It can perform a variety of tasks and duties that may be time-consuming, difficult, or dangerous to humans. The gesture-based control interface offers many opportunities for more natural, configurable, and easy human-machine interaction. It can expand the capabilities of the GUI and command line interfaces that we use today with the mouse and keyboard. This work proposed changing the concept of remote controls for operating a hand-operated robotic arm to get rid of buttons and joysticks by replacing them with a more intuitive approach to controlling a robotic arm via the hand gestures of the user. The proposed system performs vision-based hand gesture recognition and a robot arm that can replicate the user's hand gestures using image processing. The system detects and recognizes hand gestures using Python and sends a command to the microcontroller which is the Arduino board connected to the robot arm to replicate the recognized gesture. Five servo motors are connected to the Arduino Nano to control the fingers of the robot arm; These servos are related to the robot arm prototype. It is worth noting that this system was able to repeat the user's hand gestures with an accuracy of up to 96%

Review of control strategies for robotic movement training after neurologic injury

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well

EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation

Background: For an individualized support of patients during rehabilitation, learning of individual machine learning models from the human electroencephalogram (EEG) is required. Our approach allows labeled training data to be recorded without the need for a specific training session. For this, the planned exoskeleton-assisted rehabilitation enables bilateral mirror therapy, in which movement intentions can be inferred from the activity of the unaffected arm. During this therapy, labeled EEG data can be collected to enable movement predictions of only the affected arm of a patient. Methods: A study was conducted with 8 healthy subjects and the performance of the classifier transfer approach was evaluated. Each subject performed 3 runs of 40 self-intended unilateral and bilateral reaching movements toward a target while EEG data was recorded from 64 channels. A support vector machine (SVM) classifier was trained under both movement conditions to make predictions for the same type of movement. Furthermore, the classifier was evaluated to predict unilateral movements by only beeing trained on the data of the bilateral movement condition. Results: The results show that the performance of the classifier trained on selected EEG channels evoked by bilateral movement intentions is not significantly reduced compared to a classifier trained directly on EEG data including unilateral movement intentions. Moreover, the results show that our approach also works with only 8 or even 4 channels. Conclusion: It was shown that the proposed classifier transfer approach enables motion prediction without explicit collection of training data. Since the approach can be applied even with a small number of EEG channels, this speaks for the feasibility of the approach in real therapy sessions with patients and motivates further investigations with stroke patients.Comment: 11 pages, 6 figures, 1 tabl

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