Posts Tagged Exoskeleton

[ARTICLE] Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training – Full Text

Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.


Stroke is a severe neurological disease caused by the blockages or rupture of cerebral blood vessels, leading to significant physical disability and cognitive impairment (12). The recent statistics from the World Health Organization indicate that worldwide 15 million people annually suffer from the effect of stroke, and more than 5 million stroke patients survive and, however, require a prolonged physical therapy to recover motor function. Recent trends predict increased stroke incidence at younger ages in the upcoming years (34). Approximately four-fifths of all survived stroke patients suffer from the problems of hemiparesis or hemiplegia and, as a result, have difficulties in performing activities of daily living (ADL). Stroke causes tremendous mental and economic pressure on the patients and their families (5). Medical research has proved that, owing to the neural plasticity of the human brain, appropriate rehabilitation trainings are beneficial for stroke survivors to recover musculoskeletal motor abilities. Repetitive and task-oriented functional activities have substantial positive effects on improving motor coordination and avoiding muscle atrophy (67). Traditional stroke rehabilitation therapy involves many medical disciplines, such as orthopedics, physical medicine, and neurophysiology (89). Physiotherapists and medical personnel are required to provide for months one-on-one interactions to patients that are labor intensive, time consuming, patient-passive, and costly. Besides, the effectiveness of traditional therapeutic trainings is limited by the personal experiences and skills of therapists (1011).

In recent decades, robot-assisted rehabilitation therapies have attracted increasing attention because of their unique advantages and promising applications (1213). Compared with the traditional manual repetitive therapy, the use of robotic technologies helps improve the performance and efficiency of therapeutic training (14). Robot-assisted therapy can deliver high-intensive, long-endurance, and goal-directed rehabilitation treatments and reduce expense. Besides, the physical parameters and the training performance of patients can be monitored and evaluated via built-in sensing systems that facilitate the improvement of the rehabilitation strategy (1516). Many therapeutic robots have been developed to improve the motor functions of the upper extremity of disabled stroke patients exhibiting permanent sensorimotor arm impairments (17). The existing robots used for upper limb training can be basically classified into two types: end-point robots and exoskeleton robots. End-point robots work by applying external forces to the distal end of impaired limbs, and some examples are MIME (18), HipBot (19), GENTLE/s (20), and TA-WREX (21). Comparatively, exoskeleton robots have complex structures similar to anatomy of the human skeleton; some examples of such robots are NMES (22), HES (23), NEUROExos (24), CAREX-7 (25), IntelliArm (26), BONES (27), and RUPERT (28). The joints of the exoskeleton need to be aligned with the human anatomical joints for effective transfer of interactive forces.

The control strategies applied in therapeutic robots are important to ensure the effectiveness of rehabilitation training. So far, according to the training requirement of patients with different impairment severities, many control schemes have been developed to perform therapy and accelerate recovery. Early rehabilitation robot systems implemented patient-passive control algorithms to imitate the manual therapeutic actions of therapists. These training schemes are suitable for patients with severe paralysis to passively execute repetitive reaching tasks along predefined trajectories. Primary clinical results indicate that patient-passive training contributes to motivating muscle contraction and preventing deterioration of arm functions. The control of the human–robot interaction system is a great challenge due to its highly nonlinear characteristics. Many control algorithms have been proposed to enhance the tracking accuracy of passive training, such as the robust adaptive neural controller (29), fuzzy adaptive backstepping controller (30), neural proportional–integral–derivative (PID) controller (31), fuzzy sliding mode controller (32), and neuron PI controller (33).

The major disadvantage of patient-passive training is that the active participation of patients is neglected during therapeutic treatment (34). Several studies suggest that, for the patients who have regained parts of motor functions, the rehabilitation treatment integrated with the voluntary efforts of patients facilitates the recovery of lost motor ability (35). The patient-active control, normally referred as patient-cooperative control and assist-as-needed control, is capable of regulating the human–robot interaction depending on the motion intention and the disability level of patients. Keller et al. proposed an exoskeleton for pediatric arm rehabilitation. A multimodal patient-cooperative control strategy was developed to assist upper limb movements with an audiovisual game-like interface (36). Duschauwicke et al. proposed an impedance-based control approach for patient-cooperative robot-aided gait rehabilitation. The affected limb was constrained with a virtual tunnel around the desired spatial path (37). Ye et al. proposed an adaptive electromyography (EMG) signals-based control strategy for an exoskeleton to provide efficient motion guidance and training assistance (38). Oldewurtel et al. developed a hybrid admittance–impedance controller to maximize the contribution of patients during rehabilitation training (39). Banala et al. developed a force-field assist-as-need controller for intensive gait rehabilitation training (40). However, there are two limitations in the existing patient-cooperative control strategies. Firstly, the rehabilitation training process is not completely patient-active, as the patient needs to perform training tasks along a certain predefined trajectory. Secondly, existing control strategies are executed in self-designed virtual scenarios that are generally too simple, rough, and uninteresting. Besides, applying a certain control strategy to different virtual reality scenarios is difficult.

Taking the above issues into consideration, the main contribution of this paper is to develop a control strategy for an upper limb exoskeleton to assist disabled patients in performing active rehabilitation training in a virtual scenario based on their own active motion intentions. Firstly, the overall structure design and the real-time control system of the exoskeleton system are briefly introduced. A dynamic model of the human–robot interaction system is then established using the Lagrangian approach. After that, an admittance-based patient-active controller combined with an audiovisual therapy interface is proposed to induce the active participation of patients during training. Existing commercial virtual games without a specific predetermined training trajectory can be integrated into the controller via a virtual keyboard unit. Finally, three types of experiments, namely the position tracking experiment without interaction force, the free arm movement experiment, and the virtual airplane-game operation experiment, are conducted with healthy and disabled subjects. The experimental results demonstrate the feasibility of the proposed exoskeleton and control strategy.

Exoskeleton Robot Design

The architecture of the proposed exoskeleton is shown in Figure 1. This wearable force-feedback exoskeleton robot has seven actuated degrees of freedom (DOFs) and two passive DOFs covering the natural range of movement (ROM) of humans in ADL. The robot has been designed with an open-chain structure to mimic the anatomy of the human right arm and provide controllable assistance torque to each robot joint. There are three actuated DOFs at the shoulder for internal/external rotation, abduction/adduction, and flexion/extension; two DOFs at the elbow for flexion/extension and pronation/supination; and two DOFs at the wrist for flexion/extension and ulnal/radial deviation. Besides, since the center of rotation of the glenohumeral joint varies with the shoulder girdle movement, the robot is mounted on a self-aligning platform with two passive translational DOFs to compensate the human–robot misalignment and to guarantee interaction comfort. […]

Figure 1. Architecture of upper limb rehabilitation exoskeleton (1-Self-aligning platform; 2-AC servo motor; 3-Bowden cable components; 4-Support frame; 5-Wheelchair; 6-Elbow flexion/extension; 7-Proximal force/torque sensor; 8-Wrist flexion/extension; 9-Wrist ulnal/radial deviation; 10-Distal force/torque sensor; 11-Forearm pronation/supination; 12-Auxiliary links; 13-Shoulder flexion/extension; 14-Shoulder abduction/adduction; 15-Shoulder internal/external; 16-Free-length spring).


Continue —>  Frontiers | Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training | Neurology

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[WEB SITE] Robotic trousers could help disabled people walk again

Robotic trousers could help disabled people walk again

Balloon muscles. Credit: University of Bristol

Could the answer to mobility problems one day be as easy as pulling on a pair of trousers? A research team led by Bristol University’s Professor Jonathan Rossiter has recently unveiled a prototype pair of robotic trousers that they hope could help some disabled people walk without other assistance.

As an engineer who researches ways of helping people with spinal chord injuries move their limbs again, I’m acutely aware of how the loss of mobility can affect a person’s quality of life, and how restoring that movement can help. Given the staggering number of people with disabilities (over 6.5m people with  in the UK alone) and our ageing population, devices that improve mobility could help a large segment of the population.

Yet despite 50 years of research, this kind of technology has rarely been adopted outside the lab. So is the novel development of robotic  on course to finally take a working mobility technology into the home?

Unlike the rigid robotic device in the Wallace and Gromit animated film The Wrong Trousers, the new so-called “Right Trousers” use soft  to create movement, as well as harnessing the wearer’s real muscles. These mimic human muscles in producing a force simply by becoming shorter and pulling on both ends.

By bundling several artificial muscles together, the assistive trousers can move a joint such as the knee, and help the user with movements such as standing up from a chair. Because the artificial muscles are elastic and soft they are safer than traditional motors used in rigid robotic exoskeletons that, although powerful, are stiff and uncomfortable.

The researchers have put forward several different ideas for how to shorten the artificial muscles and create movement. One design adapts the concept of air muscles, which are effectively balloons that expand sideways and shorten in length as they fill with air.

Another proposed design uses electricity to shorten an artificial  made from a gel placed between two copper plates. The gel is attracted to areas of high electrical voltage. So creating two different voltages in the plates forces the gel to shrink around one of them, bringing them closer together and shortening the muscle.

Another technology integrated in the assistive trousers is functional electrical stimulation (FES). Electrodes woven into the trousers strategically located over muscles can send specially designed electrical impulses into the body to hijack the communication channel between the brain and the muscles and directly command muscles to contract. By using existing muscles and bypassing the brain, the assistive trousers can even command muscles that the wearers might have difficulty using on their own (for example due to stroke).

The trousers can also help users who struggle to stand for any length of time thanks to specially made plastic knee braces that stiffen as they cool. Controlling the temperature of the braces allows the knee to move or lock in position to maintain standing without much effort needed by the muscles (real or artificial).

Other features include an automatic belt, using a mechanism similar to the air muscles, to make it easy and safe to put on and take off the trousers.

Robotic trousers could help disabled people walk again

Knee brace. Credit: University of Bristol

The researchers suggest creating an embedded electronic system that receives information about the wearer’s motion and state from sensors embedded throughout the trousers, and controls all of the systems to tailor movements to the user’s needs. The electronics would allow users to control their movement via controls directly woven onto the trousers. The challenge will be to time the movement of the artificial muscles and the electrical stimulation of the real muscles in response to the user’s posture.

Remaining challenges

The Right Trousers are unique in their approach to merging cutting edge research and well-established techniques in a single prototype. Aside from the novelty of robotic trousers, what makes the device so compelling as a practical assistive technology is the fact it can be adapted to many different users. This raises the hope it could be widely adopted where other previous ventures have failed.

However, this is only the prototype. A working product is probably at least five years away and significant questions must be answered to get to that stage. Where will it store all the power it needs? How can all the systems be miniaturised and embedded in the trousers so they don’t become bulky and awkward to wear? Can the controller predict the best action to take amid the ever-changing complexity of real environments where users will be walking?

Yet other technologies have the potential to improve the trousers even further. Brain-computer interfaces that can decode brain signals are now being used in systems that help paralysed people move again. Controlling the assistive trousers by thought could make taking a step effortless again for many people.

via Robotic trousers could help disabled people walk again

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[Study] Effects of Exoskeleton Robotic Training Device on Upper Extremity in Brain The Effects of Exoskeleton Robotic Training Device


The purpose of this study is to examine the effects of the EMG-driven exoskeleton hand robotic training device on upper extremity motor and physiological function, daily functions, quality of life and self-efficacy in brain injury patients.

Full Title of Study: “The Effects of the EMG-driven Exoskeleton Hand Robotic Training Device on Upper Extremity Motor and Physiological Function, Daily Functions, Quality of Life and Self-efficacy in Brain Injury Patients”

Study Type

  • Study Type: Interventional
  • Study Design
    • Allocation: Randomized
    • Intervention Model: Crossover Assignment
    • Primary Purpose: Treatment
    • Masking: Single (Outcomes Assessor)
  • Study Primary Completion Date: November 1, 2018

Detailed Description

In the Robot-assisted group, participants receive training including passive movement, active movement, and game practices.

Let’s see the operation of the robot system by video. First, the passive movement. Patients could perform a movement of full hand, or thumb/second/middle finger together.

Second, the active movement. There were three types of active movement, including full hand grasp/ release/ or grasp and release together.

The researcher chose two out of three of the movements. Third, the game mode. There were several games to practice the active movement, including only distal part/ or distal plus proximal part together.

In the Conventional group, participants receive conventional occupational therapy.

The intervention was conducted 1.5 hour a day, 3 days a week for consecutive 4 weeks.


more —>  Effects of Exoskeleton Robotic Training Device on Upper Extremity in Brain…

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[Abstract] Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications.


Stroke has become the leading cause of disability and the second-leading cause of mortality worldwide. Dyskinesia complications are the major reason of these high death and disability rates. As a tool for rapid motion function recovery in stroke patients, exoskeleton robots can reduce complications and thereby decrease stroke mortality rates. However, existing exoskeleton robots interfere with the wearer’s natural motion and damage joints and muscles due to poor human-machine coupling. In this paper, a novel ergonomic soft bionic exoskeleton robot with 7 degrees of freedom was proposed to address these problems based on the principles of functional anatomy and sports biomechanics. First, the human motion system was analysed according to the functional anatomy, and the muscles were modelled as tension lines. Second, a soft bionic robot was established based on the musculoskeletal tension line model. Third, a robot control method mimicking human muscle control principles was proposed and optimized on a humanoid platform manufactured using 3D printing. After the control method was optimized, the motion trajectory similarities between humans and the platform exceeded 87%. Fourth, the force-assisted effect was tested based on electromyogram signals, and the results showed that muscle signals decreased by 58.17% after robot assistance. Finally, motion-assistance experiments were performed with stroke patients. The joint movement level increased by 174% with assistance, which allowed patients to engage in activities of daily living. With this robot, stroke patients could recover their motion functions, preventing complications and decreasing fatality and disability rates.


via Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications. – PubMed – NCBI

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[WEB PAGE] Bionic exoskeleton could help people walk again

Researchers at University of Pittsburgh are combining this robotic technology of an exoskeleton with sensory technology to make paralyzed muscles work with the use of ultrasound. The team recently created a prototype hybrid exoskeleton. The hybrid aspect comes from the two types of technology being used in this project, with electrodes sending ultrasound noninvasively to make paralyzed muscles work while the battery-powered bionic exoskeleton provides additional support to promote movement. “We’re trying to create a situation where the patient controls the exoskeleton, not the other way around,” said Nitin Sharma, associate professor of mechanical engineering and materials science in Pitt’s Swanson School of Engineering and the team’s principal investigator.

Current rehabilitative technologies predict remaining muscle function and how much assistance is needed for muscle movement, a process called electromyography. Correctly measuring how much assistance any rehabilitative device should provide is a challenge with this method, as it is limited to large muscle groups.

However, Sharma’s research uses ultrasound, rather than electricity, delivered through sensors placed on the body. This aims to more accurately measure how much movement a target muscle group can generate. Ultrasound stimulates the tissue beneath the skin’s surface using high-frequency sound waves that cannot be heard by humans. While the ultimate goal is to coordinate muscle movement for the entire leg, Sharma’s team is focusing on the ankle for now because it is “much more complicated” than other parts of the leg, Sharma said. “Unlike the knee joint which moves in one direction, the ankle can be flexed in multiple directions and different muscles activate that joint,” Sharma said. “With electromyography, it’s very challenging because there is no correct place to put these sensors, so we want to use ultrasound to figure that out.”

Sensory technology

The prototype exoskeleton is being developed at Pitt’s Neuromuscular Control and Robotics Laboratory, also known as the Sharma Lab, and is wired to a power source. The final product will be able to function with a portable battery. In addition, the team is working on designs that will integrate these exoskeletons with wheelchairs other mobility technologies.

Sharma said the team will next find out whether the exoskeleton affects neurological behavior and muscle mass in the legs. The team also aims to slim down the 17 kilogram (37.5 pounds) prototype to make the exoskeleton more user friendly. “We added knee motors to the design, making it heavier. But we will be replacing many of our parts with aluminum and carbon fiber parts in the near future, so we are targeting a weight of under 12 kilograms (about 26.5 pounds) with the upgrades,” said Albert Dodson, a research associate in the Sharma Lab. “Exoskeletons are heavy, so what we’re proposing is that since people will be using their muscles, you don’t need these big exoskeletons,” Sharma said. “And if you use both your own muscles and these exoskeletons, you could also save power and walk for longer periods of time.”

Source: University of Pittsburgh

via Bionic exoskeleton could help people walk again – MedicalView

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[Abstract + References] Development of Hand Exoskeleton Prototype for Assisted Rehabilitation – Conference paper


The present work describes the design and construction of an exoskeleton prototype for hand rehabilitation. The device is designed for independent movements on each finger, considering the anthropometry of Mexican people and the rehabilitation paradigm. The stages for the prototype development are the design, mechanical characterization, position and orientation control, and exoskeleton assembly. As a result, the first prototype of exoskeleton for hand rehabilitation was obtained, thus, indicating the materials used in the different pieces and performing some physical tests for the validation of the mechanism.


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[WEB SITE] Task-Oriented Exercise Using the ArmeoSpring Arm & Hand Rehabilitation Exoskeleton -CLINICAL TRIAL: In-Progress / Currently Recruiting Participants


In-Progress / Currently Recruiting Participants

July 26, 2018

What Do We Do?

Participants will complete a 6 week (18 session) task-oriented therapy program using the ArmeoSpring by Hocoma. ArmeoSpring is specifically designed for individuals who are beginning to regain active movement of the arm and hand and can benefit those who have suffered strokes, traumatic brain injury or neurological disorders resulting in hand and arm impairment.


Screen for eligibility, perform baseline evaluations.

VISIT 2-19

Treatment sessions, either 30 minutes or 1 hour per session with either 1 on 1 therapist attention or 1 therapist for 2 patients.

  • 6 weeks of 30 minutes of one on one training or equivalent to 18 sessions.
  • 6 weeks of 30 minutes of two to one training or equivalent to 18 sessions.
  • 6 weeks of 1 hour of one on one training or equivalent to 18 sessions.
  • 6 weeks of 1 hour of two to one training or equivalent to 18 sessions.

Discharge evaluation (Armeo device evaluations & clinical evaluations).


3-month Follow up evaluation (Armeo device evaluations & clinical evaluations).

What is the ArmeoSpring?

The ArmeoSpring is suitable for the widest range of individuals from severely to moderately affected.

  • This ergonomic exoskeleton enables functional and self-initiated movement therapy.
  • Provides an extensive 3D workspace and supports simultaneous arm and hand therapy.
  • Motivates participants with Augmented Performance Feedback exercises for arm and hand training.
  • Provides assessments for an objective analysis and documentation of individual progress.

How Do I Enroll?

Please contact us at or (914) 597-2111 for a free consultation.

Visit Enrollment Form & Process – Upper Limb Program, download and complete the enrollment forms and return to the Restorative Neurology Clinic.


For more Visit SITE —> Task-Oriented Exercise Using the ArmeoSpring Arm & Hand Rehabilitation Exoskeleton | Burke Neurological Institute | Weill Cornell Medicine

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[WEB SITE] ReWalk Exoskeleton – Rehabilitation Technology – PhysioFunction

Rewalk Exoskeleton


What is the ReWalk?

The ReWalk is a robotic Exoskeleton that can be worn for personal use at home and out in the community.

The robotic device provides hip and knee motion to enable individuals with spinal cord injury to stand upright, walk, turn, climb and descend stairs. The system can be customised to provide optimal fit to ensure safety, function and joint function.

ReWalk allows people to walk independently as the robotic device mimics the natural gait pattern of the legs.

What are the benefits?

The benefits of using the ReWalk include:

  • Ability to walk upright rather than sit in a wheelchair
  • Improve mobility and quality of life measures such as:

  • Improvements in bowel and bladder function

  • Maintenance of bone mass

  • Reduction of some medications for certain ailments

  • Emotional and psychosocial benefits

How can you trial and purchase a ReWalk for home use?

At our Midlands ‘Centre of Excellence Clinic’ in Northampton.

Firstly, we will book you into our clinic for an initial assessment where you will be able to trial the device*.

Providing you are suitable for the device, you will be given the option to purchase your own ReWalk and at the same time we will discuss the rehab package on offer that will help you achieve maximum use of your ReWalk.

If you live outside the Midlands and need accommodation, we can also help find you an accessible place to stay.

*Prior to the assessment we will need to establish your suitability for the device as ReWalk is intended for use by individuals with lower limb disabilities whose hands and shoulders can support crutches or a walker. Your height will need to be between 160cm – 190cm (5’3″ to 6’2″). Weight requirement is up to 100kg (220lbs). Other factors such as bone density and range of motion will be considered and evaluated.

via ReWalk Exoskeleton | Rehabilitation Technology | PhysioFunction

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[VIDEO] Robotic Exoskeleton Helps People With Neurological Disorders – YouTube

This robotic exoskeleton helps people get their mobility back. Harmony, the robotic exoskeleton, can assist individuals who have had strokes or spinal injuries.

For more visit:…

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[ARTICLE] A Neuromuscular Interface for Robotic Devices Control – Full Text


A neuromuscular interface (NI) that can be employed to operate external robotic devices (RD), including commercial ones, was proposed. Multichannel electromyographic (EMG) signal is used in the control loop. Control signal can also be supplemented with electroencephalography (EEG), limb kinematics, or other modalities. The multiple electrode approach takes advantage of the massive resources of the human brain for solving nontrivial tasks, such as movement coordination. Multilayer artificial neural network was used for feature classification and further to provide command and/or proportional control of three robotic devices. The possibility of using biofeedback can compensate for control errors and implement a fundamentally important feature that has previously limited the development of intelligent exoskeletons, prostheses, and other medical devices. The control system can be integrated with wearable electronics. Examples of technical devices under control of the neuromuscular interface (NI) are presented.

1. Introduction

Development of neurointerface technology is a topical scientific focus, with the demand for such systems driven by the need for humans to communicate with numerous electronic computing and robotic devices (RD), for example, in medical applications such as prosthetic limbs and exoskeletons. At present, multichannel recording of neuromuscular activity and the development of neurointerface applications that implement unique mechanisms for high-dimensional data processing are areas of major interest.

One of the most suitable signals aiming at controlling external RDs is electromyographic (EMG) activity. Multichannel signals from the human peripheral nervous system have been previously successfully used to control external devices and novel methods of EMG acquisition and control strategies have recently been implemented [18]. When controlling anthropomorphic RD, the human pilot independently coordinates and plans the trajectory of motion using the massive computing power of the human brain [910]. The use of afferent neural pathways allows the activation of biological feedback; using this principle is fundamentally important to the development of rehabilitation exoskeletons, prostheses, and various other medical applications.

The disadvantages of using EMG interfaces in rehabilitation are the presence of muscle fatigue and insufficient residual muscle activity. On the other hand electroencephalographic (EEG) interfaces proved to be the best due to a direct link to the nervous system by measurement of brain activity during therapy [1112]. The brain mechanisms that enable humans to facilitate the control of external devices remain largely unknown. However, despite this knowledge gap, appropriate collection, detection, and classification can enable brain-controlled signals from the human body to be utilized for highly efficient and even intelligent control of multiparameter RDs. But brain-machine interfaces (BMI) have some limitations such as low reliability and accuracy when it comes to complex functional task training.

A possible solution to these problems is the combined use of the advantages of both types of interfaces. Such interfaces are called hybrid, for example, hybrid BMI (hBMI); the use of EMG input here can lead to a more accurate classification of EEG patterns [1315]. However, the task of developing an EMG interface is still relevant.

Considering the problem of motion recognition and decoding of EMG signals, note that there are several generally applicable methods of software signal processing: linear discriminant analysis (LDA) [20], support vector machines (SVM) [21], artificial neural networks (ANN) [22], fuzzy algorithms [2223], etc.

Despite significant progress in the field of machine learning and its application in medical tasks [24], algorithms are still based on applying ANN technologies and solving optimization problems. Creation of a universal algorithm that can adapt to different conditions in a technical control system was proven theoretically impossible, at least in the context of existing theories [2526]. Compared to traditionally controlled electronic devices, neurocontrolled devices may offer the advantage of adapting due to human brain plasticity.

The present study focuses on the development of methods and technologies for remote control of RDs in specific applications. The objective was to integrate human bioelectrical signals into a control loop. Online collection and interpretation of multisite EMG signals were performed to control a variety of robotic systems. Technical solutions were developed to associate patterns of muscular activity (and human brain, if possible) with the commands to the controlled object by employing a user-defined translation algorithm. EMG interface solution is driven by multilayer ANN feature classifier. User-defined programmable function translates sensory signals into motor commands to successfully control a variety of existing commercial RDs.[…]

Continue —> A Neuromuscular Interface for Robotic Devices Control

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