Posts Tagged Electromyography

[Abstract] Hand strengthening exercises in chronic stroke patients: Dose-response evaluation using electromyography

Abstract

Study Design

Cross-sectional.

Purpose of the Study

This study evaluates finger flexion and extension strengthening exercises using elastic resistance in chronic stroke patients.

Methods

Eighteen stroke patients (mean age: 56.8 ± 7.6 years) with hemiparesis performed 3 consecutive repetitions of finger flexion and extension, using 3 different elastic resistance levels (easy, moderate, and hard). Surface electromyography was recorded from the flexor digitorum superficialis (FDS) and extensor digitorum (ED) muscles and normalized to the maximal electromyography of the non-paretic arm.

Results

Maximal grip strength was 39.2 (standard deviation: 12.5) and 7.8 kg (standard deviation: 9.4) in the nonparetic and paretic hand, respectively. For the paretic hand, muscle activity was higher during finger flexion exercise than during finger extension exercise for both ED (30% [95% confidence interval {CI}: 19-40] vs 15% [95% CI: 5-25] and FDS (37% [95% CI: 27-48] vs 24% [95% CI: 13-35]). For the musculature of both the FDS and ED, no dose-response association was observed for resistance and muscle activity during the flexion exercise (P > .05).

Conclusion

The finger flexion exercise showed higher muscle activity in both the flexor and extensor musculature of the forearm than the finger extension exercise. Furthermore, greater resistance did not result in higher muscle activity during the finger flexion exercise. The present results suggest that the finger flexion exercise should be the preferred strengthening exercise to achieve high levels of muscle activity in both flexor and extensor forearm muscles in chronic stroke patients. The finger extension exercise may be performed with emphasis on improving neuromuscular control.

Level of Evidence

4b.

Source: Hand strengthening exercises in chronic stroke patients: Dose-response evaluation using electromyography – Journal of Hand Therapy

, , , , , , , , , ,

Leave a comment

[ARTILE] Changes in gait kinematics and muscle activity in stroke patients wearing various arm slings – Full Text

Abstract

Stroke patients often use various arm slings, but the effects of different slings on the joint kinematics and muscle activity of the arm in the gait have not been investigated. The effects of joint kinematics and muscle activity in the gait were investigated to provide suggestions for gait training for stroke patients. In all, 10 chronic stroke patients were voluntarily recruited. An eight-camera three-dimensional motion analysis system was used to measure joint kinematics while walking; simultaneously, electromyography data were collected for the anterior and posterior deltoids and latissimus dorsi. The amplitude of pelvic rotation on the less-affected side differed significantly among the different arm slings (P<0.05). Changes in the knee kinematics of the less-affected side also differed significantly (P<0.05), while there were no significant differences in the muscle activity of the affected arm. In stroke patients, an extended arm sling is more useful than no sling or a flexed arm sling in terms of the amplitude of the rotation of the less-affected pelvic side in the stance phase while walking. The less-affected knee joint is flexed more without a sling than with any sling. All arm slings support the extension of the contralateral knee.

INTRODUCTION

Stroke is a major cause of morbidity worldwide. Approximately 800,000 patients have strokes annually (Lloyd-Jones et al., 2010). Patients with stroke have disabilities that result from paralysis, and most complain of difficulty walking (Jørgensen et al., 1995). Bovonsunthonchai et al. (2012) showed that the affected upper extremity is important for improving the performance and coordination of gait in stroke patients. In addition, the movement of the upper extremity improves the range of motion at the ankle as well as trunk stability (Stephenson et al., 2010).
Stroke patients often develop a subluxation of the shoulder on the affected side, because they can no longer support the weight of their own arm due to paralysis (Griffin et al., 1986). Consequently, arm slings are often necessary. Stroke patients often use a hemisling. Faghri et al. (1994) stated that use of a hemisling induced flexion synergy patterns of the upper trunk and delayed functional activity. However, few studies have examined how different arm slings, including a hemisling, affect the gait patterns of stroke patients. Reported studies have examined the hemisling in terms of the gait patterns (Yavuzer and Ergin, 2002), balance (Acar and Karatas, 2010), and energy consumption (Han et al., 2011) of stroke patients.
There are various types of arm sling, such as the flexed sling (a single-strap hemisling), extended sling (Bobath sling, Rolyan sling), GivMohr sling (Dieruf et al., 2005), and elastic arm sling (Hwang and An, 2015). The sling supports some of the weight of the arm and simultaneously limits the motion of the upper extremities. Pontzer et al. (2009)suggested that the arms serve as passive mass dampers to decrease the rotation of the torso and head. Lieberman et al. (20072008) also held that the arms serve as passive dampers to minimise vertical motion. The trunk and shoulders act as elastic linkages between the pelvis, shoulder girdle, and arms (Pontzer et al., 2009).
Some studies have examined the activities of the arm muscle during walking (Lieberman et al., 2007Prentice et al., 2001), while other studies have found that most of the arm swing is passive, while a small torque may actively occur in shoulder rotation (Jackson et al., 1978Kubo et al., 2004). The muscle activity of the upper extremities is still the subject of debate (Collins et al., 2009Kubo et al., 2004Kuhtz-Buschbeck and Jing, 2012). However, the restrictive effects and support provided by various arm slings could have different effects on the muscle activities of the affected arm in stroke patients.
Therefore, we investigated how the muscle activities of the affected arm and kinematic data taken during walking are influenced by flexion-type (hemisling), extension-type (Rolyan sling), and elastic arm slings under elastic tension. We discuss which arm should be used for clinical gait training.

Continue —> Changes in gait kinematics and muscle activity in stroke patients wearing various arm slings – ScienceCentral

Fig. 1 The conditions of the various arm slings: (A) none, (B) a flexed type, (C) an extended type, and (D) an elastic type.

, , , , ,

Leave a comment

[ARTICLE] Hybrid Assistive Neuromuscular Dynamic Stimulation Therapy: A New Strategy for Improving Upper Extremity Function in Patients with Hemiparesis following Stroke – Full Text

Abstract

Hybrid Assistive Neuromuscular Dynamic Stimulation (HANDS) therapy is one of the neurorehabilitation therapeutic approaches that facilitates the use of the paretic upper extremity (UE) in daily life by combining closed-loop electromyography- (EMG-) controlled neuromuscular electrical stimulation (NMES) with a wrist-hand splint. This closed-loop EMG-controlled NMES can change its stimulation intensity in direct proportion to the changes in voluntary generated EMG amplitudes recorded with surface electrodes placed on the target muscle. The stimulation was applied to the paretic finger extensors. Patients wore a wrist-hand splint and carried a portable stimulator in an arm holder for 8 hours during the daytime. The system was active for 8 hours, and patients were instructed to use their paretic hand as much as possible. HANDS therapy was conducted for 3 weeks. The patients were also instructed to practice bimanual activities in their daily lives. Paretic upper extremity motor function improved after 3 weeks of HANDS therapy. Functional improvement of upper extremity motor function and spasticity with HANDS therapy is based on the disinhibition of the affected hemisphere and modulation of reciprocal inhibition. HANDS therapy may offer a promising option for the management of the paretic UE in patients with stroke.

1. Functional Recovery of Upper Extremity Motor Function following Stroke

Stroke is a common health-care problem that causes physical impairment, disability, and problems in social participation. The most common impairment caused by stroke is motor impairment. Motor impairment affects the control of the unilateral upper and lower extremities. Recovery of function in the hemiparetic upper extremity is noted in fewer than 15% of patients after stroke [1].

Patients often compensate for their paretic upper extremity by using their intact upper extremity in the performance of everyday tasks [2]. It is supposed that strong reliance on compensatory overuse of the intact upper extremity inhibits functional recovery of the impaired upper extremity. This may explain the limited improvement of the functional capability of the paretic upper extremity in activities of daily living (ADL).

Principles of motor rehabilitation following stroke have been described as being dose-dependent and task-specific [3]. High-intensity practice and task-specific training are recommended for functional recovery. Several systematic reviews [4, 5] have explored whether high-intensity therapy improves recovery, and the principle that increased intensive training is helpful is widely accepted. Task-specific training is a well-accepted principle in motor rehabilitation. Training should target the goals that are relevant for the needs of the patients and preferably be given in the patient’s own environment.

The goal of upper extremity rehabilitation is to improve the capability of the paretic upper extremity for ADL. Constraint-induced movement therapy (CIMT) has been developed to enhance the forced use of the paretic hand in ADL with reduction of the compensatory overuse of the intact upper extremity. However, to participate in CIMT, the candidates must be able to voluntary extend their fingers and wrist at least 10 degrees, practice for 6 hours daily in a 2-week course, and spend waking hours with their nonparetic hand in a mitt [6].

To counter potential problems inherent in the intensive services needed for CIMT, we developed an alternative therapeutic approach that provides high-intensity training to facilitate the use of the paretic upper extremity in daily living by combining closed-loop electromyography- (EMG-) controlled neuromuscular electrical stimulation (NMES) with a wrist-hand splint for patients with moderate to severe hemiparesis. Fujiwara et al. called this hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy [7].

2. HANDS Therapy

A PubMed literature search was conducted using the MeSH terms stroke, rehabilitation, upper extremity function, and neuromuscular electrical stimulation, and 71 articles were identified. A further search of PubMed with the terms stroke, rehabilitation, upper extremity function, neuromuscular electrical stimulation, and splint identified 4 articles, all regarding HANDS therapy.

HANDS therapy facilitates the use of the paretic upper extremity in daily living by combining closed-loop EMG-controlled NMES with a wrist-hand splint for patients with moderate to severe hemiparesis. This HANDS system is active for 8 hours, and patients are instructed to use their paretic hand as much as possible while wearing the HANDS system. Their nonparetic upper extremity is not restrained. The patients are also instructed to practice bimanual activities in their ADL. All participants in HANDS therapy are admitted, and the length of the intervention is 21 days. They receive 90 minutes of occupational therapy per day, 5 days a week. Each session of occupational therapy consists of gentle stretching exercise of the paretic upper extremity and active muscle reeducation exercise. All participants are instructed how to use their paretic hand in ADL with the HANDS system. Occupational therapists are directed toward participants’ goals and focused on their particular impairments and disabilities; thus, the specific therapy that each patient receives varies [7, 8].

Fujiwara et al. [7, 8] reported the indications for HANDS therapy as follows: () no cognitive deficits; () no pain in the paretic upper extremity; () passive extension range of motion (ROM) greater than 0 degrees of the affected wrist and −10 degrees of the metacarpophalangeal joints; () detectable surface EMG signals in the affected extensor digitorum communis (EDC) or extensor pollicis longus (EPL) when the patient intends to extend their fingers; () ability to raise the paretic hand to the height of the nipple; () scores of Fugl-Meyer test position sense of joints in the glenohumeral joint, elbow, wrist, and thumb of 1 or more; and () the ability to walk without physical assistance in daily life (e.g., including patients who can walk independently with a cane and/or an orthosis). The exclusion criteria were () history of major psychiatric or previous neurological disease, including seizures; () cognitive impairment precluding appropriately giving informed consent or the patient’s Mini Mental Examination Scale score was below 25; () patients with severe pain in the paretic upper extremity; () patients with a pacemaker or other implanted stimulator; and () patients with visuospatial neglect or apraxia.

Previous reports showed that none of the patients experienced any discomfort or significant disability with the HANDS therapy.

2.1. Closed-Loop Electromyography- (EMG-) Controlled Neuromuscular Electrical Stimulation (NMES)

Twenty-nine articles were found in PubMed using the terms stroke, electromyography, neuromuscular electrical stimulation, and upper extremity. Thirteen of 29 articles were on EMG-triggered NMES. Six of 29 articles were on EMG-controlled NMES. Two involved contralaterally controlled electrical stimulation.

EMG-triggered NMES applies preset electrical stimulation when EMG activity reaches a target threshold. The stimulus intensity and duration are determined and not changeable. EMG-controlled NMES applies electrical stimulation during voluntary contraction and changes the stimulation intensity in proportion to the changes in EMG amplitude.

For assistive stimulation, HANDS therapy used closed-loop EMG-controlled NMES, which was developed by Muraoka [9] and commercially available with MURO stimulation (Pacific Supply, Osaka, Japan). This closed-loop EMG-controlled NMES is portable and attaches to the arm (Figure 1). The surface electrodes pick up EMG signals at the target muscle and simultaneously stimulate it in direct proportion to the picked-up EMG signal, with the exception of the 25 ms after delivering each stimulation pulse, in which stimulation artifacts and M wave are observed. The external adjustment unit sets () range of stimulus intensity; () sensitivity of the EMG; () threshold of EMG amplitude that starts stimulation; and () gradient of stimulus intensity change to the change of EMG amplitude. Once these parameters were set with the external adjustment unit, the stimulator memorized these parameters.

Continue —>  Hybrid Assistive Neuromuscular Dynamic Stimulation Therapy: A New Strategy for Improving Upper Extremity Function in Patients with Hemiparesis following Stroke

, , , , , , , ,

Leave a comment

[ARTICLE] Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient – Full Text

A hand exoskeleton driven by myoelectric pattern recognition was designed for stroke rehabilitation. It detects and recognizes the user’s motion intent based on electromyography (EMG) signals, and then helps the user to accomplish hand motions in real time. The hand exoskeleton can perform six kinds of motions, including the whole hand closing/opening, tripod pinch/opening, and the “gun” sign/opening. A 52-year-old woman, 8 months after stroke, made 20×2-hour visits over 10 weeks to participate in robot-assisted hand training. Though she was unable to move her fingers on her right hand before the training, EMG activities could be detected on her right forearm. In each visit, she took 4×10-minute robot-assisted training sessions, in which she repeated the aforementioned six motion patterns assisted by our intent-driven hand exoskeleton. After the training, her grip force increased from 1.5 kg to 2.7 kg, her pinch force increased from 1.5 kg to 2.5 kg, her score of Box & Block test increased from 3 to 7, her score of Fugl-Meyer (Part C) increased from 0 to 7, her hand function increased from Stage 1 to Stage 2 in Chedoke-McMaster assessment. The results demonstrate the feasibility of robot-assisted training driven by myoelectric pattern recognition after stroke.

Introduction

Robot-assisted upper limb training is considered to be more efficient (1) and economic (2) than conventional therapy in neurorehabilitation. Controlling the robot with the user’s own electromyography (EMG) signals connects the user’s intended motion and his actual movements. It can therefore enhance therapeutic effects and promote motor learning (35). Various EMG-driven robots and exoskeletons have been developed for neurorehabilitation (68), primarily based on one-to-one mapping, which typically maps one channel of EMG signal to a corresponding single degree-of-freedom (DOF) or variable such as speed and torque using a conventional “on-off” or proportional strategy. Robots based on such control strategy work well on training joints with only a few DOFs such as elbow and wrist. However, a human hand has up to 27 DOFs (9) and is controlled by complex temporal and spatial coordination of multiple muscles. It is therefore not feasible to regain hand dexterity through conventional control strategies. Myoelectric pattern-recognition techniques have been developed to extract motion intentions from EMG signals (10, 11). The extracted intentions can then be used to control a multiple-DOF robot such as a prosthesis (12). Previous studies have also shown that motion intentions can still be extracted after neurological impairment (1315). We therefore developed an intent-driven hand training system. The system employs an exoskeleton hand, which is controlled by myoelectric pattern recognition. As soon as the user’s intention is detected (usually within 250 ms), the system is able to assist to accomplish the intended motions (16).

Case Report

Subject

A 52-year-old woman participated in this robotic hand-assisted training 8 months after stroke. She was right-handed before stroke and had hemiplegia on her right side after her stroke. She was able to walk independently with an ankle foot orthosis but had difficulties in moving her right arm. Her fingers were flexed naturally. She was unable to move any of the fingers on her right hand, but EMG signals were able to be recorded from her forearm. Her Fugl–Meyer score (Part A–D, max 66) was 16, with a 0 in Part C (Hand, max 14). She had no pain when her whole hand was passively opened or closed. She did not receive any other hand or upper limb therapies while participating in this study. During her visits, she was able to understand and follow all the instructions.

Exoskeleton Hand

The exoskeleton hand, Hand of Hope (Rehab-Robotics, Hong Kong), was used in this study to help the subject move her hand (Figure 1). The exoskeleton hand has five individual fingers. Each finger is actuated by a linear actuator that can pull and push linearly. The mechanical design of the fingers converts these linear movements into the rotations of a virtual metacarpophalangeal (MCP) joint and a virtual proximal interphalangeal (PIP) joint. Both joints rotate together to help the hand perform closing and opening movements (7). The motion range is 55° and 65° for MCP and PIP joints, respectively. The subject’s palm and five fingers are fixed to the exoskeleton hand with Velcro belts. Each finger can be bent or straightened individually by the exoskeleton hand. The exoskeleton hand stands on a brace, which also supports the subject’s forearm, so that the subject can be totally relaxed when attached to the exoskeleton. The exoskeleton hand used in this study can perform six different motion patterns, including hand closing (HC); hand opening (HO); thumb, index, and middle fingers closing (TIMC or tripod pinch); thumb, index, and middle fingers opening; middle, ring, and little fingers closing (MRLC or the “gun” sign); and middle, ring, and little fingers opening. The exoskeleton hand can perform HC, TIMC, or MRLC when it is open. However, after performing any one from these three patterns, it can only return to the original open status (e.g., there is no direct way from the “tripod pinch” to the “gun” sign).

Figure 1. Training with the exoskeleton hand driven by myoelectric pattern recognition.

Continue —> Frontiers | Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient | Stroke

, , , , , , , ,

Leave a comment

[WEB SITE] One step at a time

IMAGE: DR. KIM (LEFT) WITH DR. SHARMA AND A HYBRID EXOSKELETON PROTOTYPE IN THE NEUROMUSCULAR CONTROL AND ROBOTICS LABORATORY IN THE SWANSON SCHOOL OF ENGINEERING. view more CREDIT: SWANSON SCHOOL OF ENGINEERING

PITTSBURGH (March 7, 2017) … The promise of exoskeleton technology that would allow individuals with motor impairment to walk has been a challenge for decades. A major difficulty to overcome is that even though a patient is unable to control leg muscles, a powered exoskeleton could still cause muscle fatigue and potential injury.

However, an award from the National Science Foundation’s Cyber-Physical Systems (CPS) program will enable researchers at the University of Pittsburgh to develop an ultrasound sensor system at the heart of a hybrid exoskeleton that utilizes both electrical nerve stimulation and external motors.

Principal investigator of the three year, $400,000 award is Nitin Sharma, assistant professor of mechanical engineering and materials science at Pitt’s Swanson School of Engineering. Co-PI is Kang Kim, associate professor of medicine and bioengineering. The Pitt team is collaborating with researchers led by Siddhartha Sikdar, associate professor of bioengineering and electrical and computer engineering at George Mason University, who also received a $400,000 award for the CPS proposal, “Synergy: Collaborative Research: Closed-loop Hybrid Exoskeleton utilizing Wearable Ultrasound Imaging Sensors for Measuring Fatigue.”

This latest funding furthers Dr. Sharma’s development of hybrid exoskeletons that combine functional electrical stimulation (FES), which uses low-level electrical currents to activate leg muscles, with powered exoskeletons, which use electric motors mounted on an external frame to move the wearer’s joints.

“One of the most serious impediments to developing a human exoskeleton is determining how a person who has lost gait function knows whether his or her muscles are fatigued. An exoskeleton has no interface with a human neuromuscular system, and the patient doesn’t necessarily know if the leg muscles are tired, and that can lead to injury,” Dr. Sharma explained. “Electromyography (EMG), the current method to measure muscle fatigue, is not reliable because there is a great deal of electrical “cross-talk” between muscles and so differentiating signals in the forearm or thigh is a challenge.”

To overcome the low signal-to-noise ratio of traditional EMG, Dr. Sharma partnered with Dr. Kim, whose research in ultrasound focuses on analyzing muscle fatigue.

“An exoskeleton biosensor needs to be noninvasive, but systems like EMG aren’t sensitive enough to distinguish signals in complex muscle groups,” Dr. Kim said. “Ultrasound provides image-based, real-time sensing of complex physical phenomena like neuromuscular activity and fatigue. This allows Nitin’s hybrid exoskeleton to switch between joint actuators and FES, depending upon the patient’s muscle fatigue.”

In addition to mating Dr. Sharma’s hybrid exoskeleton to Dr. Kim’s ultrasound sensors, the research group will develop computational algorithms for real-time sensing of muscle function and fatigue. Human subjects using a leg-extension machine will enable detailed measurement of strain rates, transition to fatigue, and full fatigue to create a novel muscle-fatigue prediction model. Future phases will allow the Pitt and George Mason researchers to develop a wearable device for patients with motor impairment.

“Right now an exoskeleton combined with ultrasound sensors is just a big machine, and you don’t want to weigh down a patient with a backpack of computer systems and batteries,” Dr. Sharma said. “The translational research with George Mason will enable us to integrate a wearable ultrasound sensor with a hybrid exoskeleton, and develop a fully functional system that will aid in rehabilitation and mobility for individuals who have suffered spinal cord injuries or strokes.”

###

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Source: One step at a time | EurekAlert! Science News

, , , , , , , ,

Leave a comment

[Abstract] Robotic Devices to Enhance Human Movement Performance.

Abstract

Robotic exoskeletons and bionic prostheses have moved from science fiction to science reality in the last decade. These robotic devices for assisting human movement are now technically feasible given recent advancements in robotic actuators, sensors, and computer processors. However, despite the ability to build robotic hardware that is wearable by humans, we still do not have optimal controllers to allow humans to move with coordination and grace in synergy with the robotic devices. We consider the history of robotic exoskeletons and bionic limb prostheses to provide a better assessment of the roadblocks that have been overcome and to gauge the roadblocks that still remain. There is a strong need for kinesiologists to work with engineers to better assess the performance of robotic movement assistance devices. In addition, the identification of new performance metrics that can objectively assess multiple dimensions of human performance with robotic exoskeletons and bionic prostheses would aid in moving the field forward. We discuss potential control approaches for these robotic devices, with a preference for incorporating feedforward neural signals from human users to provide a wider repertoire of discrete and adaptive rhythmic movements.

Source: Robotic Devices to Enhance Human Movement Performance: Kinesiology Review: Vol 6, No 1

, , , , , ,

Leave a comment

[Abstract] Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans

Abstract:

Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of 7 major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested 6 movement directions and 4 force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.

Source: Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans – IEEE Xplore Document

, , , , , , , ,

Leave a comment

[Abstract] Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient

Abstract:

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user’s movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

I. Introduction

Stroke is a leading cause of adult disability around the world. A large number of stroke survivors are left with a unilateral arm or leg paralysis. After completing conventional rehabilitation therapy, a significant number of stroke survivors are left with limited reaching and grasping capabilities [1].

Source: Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient – IEEE Xplore Document

, , , , , , , , , , , , ,

Leave a comment

[ARTICLE] Wearable System for Device Control using Bio-Electrical Signal – Full Text PDF

Abstract

In today’s world, wearable devices are progressively being used for the enhancement of the nature of the life of individuals. Human Machine Interface (HMI) has been studied for dominant the mechanical device rehabilitation aids through biosignals like EOG and EMG etc., and so on. EMG signals have been studied in detail due to the occurrence of a definite signal pattern. The current proposal focuses on the advancement of a Wearable Device control by using EMG signals of hand movements for controlling the electronic devices. EMG signals are utilized for the production of the control indicators to develop the device control. Also, an EMG sign procurement framework was produced. To create different control signals relying on the sufficiency and length of time of signal segments, the obtained EMG signals were then prepared for device control.

1. Introduction

1.1 Need for Rehabilitation Techniques

A major a part of our society is littered with one or the opposite reasonably disabilities owing to accidents and neuro-logic disorders. These patients rely upon the members of the family or care takers for his or her day to day activities like quality, communication with atmosphere, mistreatment the home instrumentation, etc1,2.

Rehabilitation devices facilitate the patients with disabilities to measure, work, play or study severally. Moreover, they improve the standard of life led by these individuals and maintain their shallowness.

1.2 EMG based Methods

Electrical potentials generated during muscle contraction are measured by EMG. The contraction of somatic cell takes place once it receives associate degree impulse. The myogram ascertained is that the add of all the action potentials that occur round the conductor site. In most of the cases, the amplitude of the myogram will increase as a result of contraction. Myogram signals is used for a range of applications together with clinical applications, HCI and interactive gaming. They’re non-heritable simply and are comparatively high in magnitude than alternative bio-signals. On the opposite hand, myogram signals area unit simply liable to noise. myogram signals contain difficult styles of noise as a result of inherent instrumentation noise, non-particulate
radiation, motion artifacts, and therefore the interaction of various tissues. Hence, to filter the unwanted noise in myogram, preprocessing is critical3. The myogram signals even have completely different signatures counting on age, muscle development, motor unit ways, skin fat layer, and gesture designs. The external appearances of 2 individuals’ gestures would possibly look identical, however the characteristic myogram signals area unit completely different4.

Full Text PDF

 

, , , ,

Leave a comment

[Abstract] Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback

Abstract:

The objective of this work was to design and experiment a robotic hand rehabilitation device integrated with a wireless EEG system, going towards patient active participation maximization during the exercise. This has been done through i) hand movement actively triggered by patients muscular activity as revealed by electromyographic signals (i.e., a target hand movement for the rehabilitation session is defined, the patient is required to start the movement and only when the muscular activity overcomes a predefined threshold, the patient-initiated movement is supported); ii) an EEG-based biofeedback implemented to make the user aware of his/her level of engagement (i.e., brain rhythms power ratio Beta/Alpha). The designed system is composed by the Gloreha hand rehabilitation glove, a device for electromyographic signals recording, and a wireless EEG headset. A strong multidisciplinary approach was the base to reach this goal, which is the fruitful background of the Think and Go project. Within this project, research institutes (Politecnico di Milano), clinical centers (INRCA-IRCCS), and companies (ab medica s.p.a., Idrogent, SXT) have worked together throughout the development of the integrated robotic hand rehabilitation device. The integrated device has been tested on a small pilot group of healthy volunteers. All the users were able to calibrate and correctly use the system, and they reported that the system was more challenging to be used with respect to the standard passive hand mobilization session, and required more attention and involvement. The results obtained during the preliminary tests are encouraging, and demonstrate the feasibility of the proposed approach.

Source: Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback – IEEE Xplore Document

, , , , , , , , , , ,

Leave a comment

%d bloggers like this: