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EMG BIOFEEDBACK VIDEOGAME SYSTEM FOR THE GAIT REHABILITATION OF HEMIPARETIC INDIVIDUALS
BACKGROUND
Digital health technologies (DHTs) are increasingly used in physical rehabilitation to support individuals to successfully engage with the frequent, intensive, and lengthy activities required to optimise recovery from illness or injury. Despite this, little is known about if, or how DHTs utilise techniques to support and promote behaviour change in physical rehabilitation.
OBJECTIVE
Using stroke rehabilitation as an exemplar, this scoping review aimed to identify the frequency and nature of behaviour change approaches utilised within DHT-based physical rehabilitation interventions.
METHODS
Databases (Embase, Medline, PyscINFO, CINAHL, Cochrane Library and AMED) were searched using keywords relating to behaviour change, DHT, physical rehabilitation and stroke. Results were independently screened by two reviewers. Sources were included if they reported a completed primary research study in which a behaviour change approach could be identified within a DHT-based, physical stroke rehabilitation intervention. Data including the study design, DHT utilised, and behaviour change approaches were charted. Specific behaviour change techniques were coded to the behaviour change technique taxonomy (BCTTv1).
RESULTS
From a total of 1973 identified sources, 103 studies (5%) were included for data charting. The most common reason for exclusion at full text screening, was the absence of an articulated approach to behaviour change (165/245, 67%). Almost half of the included studies (45/103, 44%) were described as pilot or feasibility studies. Virtual reality (VR) was the most frequently identified DHT type (58/103, 56%) and almost two-thirds of studies focused on upper limb rehabilitation (65/103, 63%). Only a limited number of studies (18/103, 17%) included a theoretical rationale for behaviour change. The most frequently used BCTTv1 clusters were feedback and monitoring (88/103, 85%), reward and threat (56, 54%), goals and planning (33, 32%), and shaping knowledge (33, 32%). Relationships between feedback and monitoring, and reward and threat were identified with prominent use of both in VR.
CONCLUSIONS
Despite most DHTs being assumed to promote engagement in rehabilitation, this scoping review demonstrates that very few studies of DHT-based physical stroke rehabilitation overtly utilised any form of behaviour change approach. From those studies that did consider behaviour change, most did not report robust underpinning theory. Future development and research of DHT-based rehabilitation needs to explicitly articulate how using DHTs may support the behaviour change required for optimal engagement in physical rehabilitation as well as establish their effectiveness. This understanding is likely to support the realisation of the transformative potential of DHTs in rehabilitation.
The purpose of this work is to conduct a study of EMG signals, in order to identify five types of movements: Extension, Short Flexion, Full Flexion, Turn Left and Turn Right. These signals were obtained from brachial muscle of six people of different gender and biotype using a data acquisition system designed for this purpose. A set of EMG signals were stored and processed to obtain a set of features that adequately represent each type of movement and then train an artificial neural network to identify them. After training phase, other set of EMG signals was used to verify system performance and were obtained high percentages of success about 83% to 99%. Finally, the trained system was used to simulate a virtual interface that plays these movements and controls a videogame that allows us to observe its behavior in real-time.
While electromyography (EMG) is widely used in experimental physiotherapy (PT), its use as a diagnostic aid and supplementary tool in clinical PT remains limited. We report an integrated, wireless PT system for the forearm, which registers muscle movements via EMG, records and wirelessly transmits the data to a laptop computer, and runs software to discriminate between arm motions. The next step is to interface with a custom video game which responds to user movement and provides real-time biofeedback to the user, allowing them to visualize whether they are performing their PT exercise properly. Our device incorporates existing technologies into a functional ensemble compatible with both at-home and clinical use. Current testing (n=7 subjects) indicates that the sensor is capable of discriminating between 6 classes of PT exercises with 92% accuracy (2 classes with 96% accuracy).
One of the major challenges in designing exercise games (exergames) is maintaining engagement and motivation over time. Previous research has shown that games can increase the motivation exercise. However, these games are typically developed with limited set of specific exercises in mind, which limits genre choices, and potentially long term motivation. To address this critical issue, we are interested in employing any exercise as an interface to common, existing, sedentary games, such as a car racing game. Specifically, in this paper, we aim to take initial steps toward this goal. As a proof of concept, we developed 2 exercised based interfaces for a car racing game - a game genre minimally used for exergames. We present the evaluation of our novel exercise interfaces to the racing game, and offer general guidelines for converting common classes of sedentary games to exergames.
Background
Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring.
Methods
Using off-the-shelf wireless sensors we designed a prototype WBAN which features a standard ZigBee compliant radio and a common set of physiological, kinetic, and environmental sensors.
Results
We introduce a multi-tier telemedicine system and describe how we optimized our prototype WBAN implementation for computer-assisted physical rehabilitation applications and ambulatory monitoring. The system performs real-time analysis of sensors' data, provides guidance and feedback to the user, and can generate warnings based on the user's state, level of activity, and environmental conditions. In addition, all recorded information can be transferred to medical servers via the Internet and seamlessly integrated into the user's electronic medical record and research databases.
Conclusion
WBANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for prolonged periods of time. To make this technology ubiquitous and affordable, a number of challenging issues should be resolved, such as system design, configuration and customization, seamless integration, standardization, further utilization of common off-the-shelf components, security and privacy, and social issues.
The use of virtual-reality technology in the areas of rehabilitation and therapy continues to grow, with encouraging results being reported for applications that address human physical, cognitive, and psychological functioning. This article presents a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis for the field of VR rehabilitation and therapy. The SWOT analysis is a commonly employed framework in the business world for analyzing the factors that influence a company's competitive position in the marketplace with an eye to the future. However, the SWOT framework can also be usefully applied outside of the pure business domain. A quick check on the Internet will turn up SWOT analyses for urban-renewal projects, career planning, website design, youth sports programs, and evaluation of academic research centers, and it becomes obvious that it can be usefully applied to assess and guide any organized human endeavor designed to accomplish a mission. It is hoped that this structured examination of the factors relevant to the current and future status of VR rehabilitation will provide a good overview of the key issues and concerns that are relevant for understanding and advancing this vital application area.
Human-robot control interfaces have received increased attention during the last decades. With the introduction of robots in every-day life, especially in developing services for people with special needs (i.e. elderly or impaired persons), there is a strong necessity of simple and natural control interfaces. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. EMG signals are recorded using surface EMG electrodes placed on the userpsilas skin, letting the userpsilas upper limb free of bulky interface sensors or machinery usually found in conventional human-controlled systems. The proposed interface allows the user to control in real-time an anthropomorphic robot arm in three dimensional (3D) space, by decoding EMG signals to motion. However, since EMG changes due to muscle fatigue are present in this kind of control interface, a probabilistic framework has been developed, which can detect in real-time the muscle fatigue level. By complying to those fatigue-related signal changes, the proposed method can provide accurate decoding of motion through long periods of time. The system is used for the continuous control of a robot arm in 3D space, using only EMG signals from the upper limb. The method is tested for a long period of operation, proving that muscle fatigue does not affect the decoder accuracy. The efficiency of the method is assessed through real-time experiments including random arm motions in 3D space.
This paper proposes a universal entertainment interface for operation of amusement machines, such as video game machines and radio control toys. In the proposed interface system, biological signals are used as input, where users can choose some specific biological signal and configuration of signal measurement in accordance with their preference, physical condition (disabled or not), and degree of the disability. From the input signals, users' intention of operation can be estimated with a probabilistic neural network (PNN), and then, control commands can be determined accordingly. With the proposed interface, people, even those with severe physical disabilities, are able to operate amusement machines. To verify validity of the proposed method, experiments were conducted with a video game machine.
The “Rutgers Ankle” is a Stewart platform-type haptic interface designed for use in rehabilitation. The system supplies six-degree-of-freedom (DOF) resistive forces on the patient's foot, in response to virtual reality-based exercises. The Rutgers Ankle controller contains an embedded Pentium board, pneumatic solenoid valves, valve controllers, and associated signal conditioning electronics. The rehabilitation exercise used in our case study consists of piloting a virtual airplane through loops. The exercise difficulty can be selected based on the number and placement of loops, the airplane speed in the virtual environment, and the degree of resistance provided by the haptic interface. Exercise data is stored transparently, in real time, in an Oracle database. These data consist of ankle position, forces, and mechanical work during an exercise, and over subsequent rehabilitation sessions. The number of loops completed and the time it took to do that are also stored online. A case study is presented of a patient nine months post-stroke using this system. Results showed that, over six rehabilitation sessions, the patient improved on clinical measures of strength and endurance, which corresponded well with torque and power output increases measured by the Rutgers Ankle. There were also substantial improvements in task accuracy and coordination during the simulation and the patient's walking and stair-climbing ability.
Sixty surviving patients had their walking ability and speed assessed regularly over the first 3 months after an acute stroke. Sixty-four matched controls were studied to allow categorisation of speed as 'slow' or 'normal'. Fourteen patients never had any significant loss of walking speed; fifteen patients never recovered the ability to walk and one patient remained dependent upon verbal support. Of the 30 showing significant recovery, only 10 regained normal speed, and 8 remained dependent upon a physical aid at 3 months. Plotting individual recovery curves of walking speed over time showed the wide range of change which may be expected. It is argued that timing of gait over 10 metres is a valid reliable measure that is currently underused.
The enhancement of an existing myoelectric control system has been investigated. The original one-channel system used an artificial neural network to classify myoelectric patterns. This research shows that a two-channel control system can improve the classification accuracy of the pattern classifier significantly, thus improving the reliability of the prosthesis.
Stroke is the most disabling chronic condition, newly affecting 35000 persons in Canada each year. Because of declining fatality, a growing number of persons will have to cope with stroke-related disability. The purpose of this paper is to describe the disabilities experienced by persons with stroke during the first year and explore the evolution of impairment, disability, handicap and health-related quality of life.
The data for this paper come from a series of longitudinal and cross-sectional studies, collectively known as the McGill Stroke Rehabilitation Research Program.
Within the first week post-stroke, getting out of bed and walking over a short distance, even with assistance, was a strong predictor of discharge home. Most of the improvement in measures of impairment and disability occurred during the first month and, by 3 months, there was still considerable room for improvement in all measures: 85% of persons were still impaired on gait speed, 78% had not reached age-specific norms for upper extremity function, 68% still demonstrated slow physical mobility, 37% needed some assistance with basic activities of daily living and 29% were still impaired on balance. By 1 year, 73% of persons scored the maximum for basic activities of daily living but 51 and 67% of persons reported their physical health and mental health to be lower than expected. Among a hardy group of stroke survivors, still living in the community 1 year post-stroke, the most striking area of difficulty was endurance, as measured by the 6 minute walk test. Those subjects well enough to complete this task (50% of sample) were able to walk, on average, only 250 metres, equivalent to 40% of their predicted ability. This series of snapshots taken at different points in time suggests that much of the improvement in impairment and disability occurs during the first month and then reaches a plateau. Handicap and quality of life continue to be issues later. Rehabilitation strategies need to consider the multifaceted nature of disablement, which in itself changes with time post-stroke.
In this study, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from ulnar nerves of 59 patients to interpret data. The data of the patients were diagnosed by the neurologists as 19 patients were normal, 20 patients had neuropathy and 20 patients had myopathy. The amount of FFT coefficients had been reduced by using principal components analysis (PCA). This would facilitate calculation and storage of EMG data. PCA coefficients were applied to multilayer perceptron (MLP) and support vector machine (SVM) and both classified systems of performance values were computed. Consequently, the results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with MLP.
A system for electromyographic (EMG) triggering of robot-assisted therapy (dubbed the EMG game) for stroke patients is presented. The onset of a patient's attempt to move is detected by monitoring EMG in selected muscles, whereupon the robot assists her or him to perform point-to-point movements in a horizontal plane. Besides delivering customized robot-assisted therapy, the system can record signals that may be useful to better understand the process of recovery from stroke. Preliminary experiments aimed at testing the proposed system and gaining insight into the potential of EMG-triggered, robot-assisted therapy are reported.
The original use of biofeedback to train single muscle activity in static positions or movement unrelated to function did not correlate well to motor function improvements in patients with central nervous system injuries. The concept of task-oriented repetitive training suggests that biofeedback therapy should be delivered during functionally related dynamic movement to optimize motor function improvement. Current, advanced technologies facilitate the design of novel biofeedback systems that possess diverse parameters, advanced cue display, and sophisticated control systems for use in task-oriented biofeedback. In light of these advancements, this article: (1) reviews early biofeedback studies and their conclusions; (2) presents recent developments in biofeedback technologies and their applications to task-oriented biofeedback interventions; and (3) discusses considerations regarding the therapeutic system design and the clinical application of task-oriented biofeedback therapy. This review should provide a framework to further broaden the application of task-oriented biofeedback therapy in neuromotor rehabilitation.
Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.
This case report describes the implementation of gait training intervention that used an electromechanical gait trainer with simultaneous functional electrical stimulation (FES) for 2 patients with acute ischemic stroke.
Two individuals with post-stroke hemiplegia of less than 6 weeks' duration participated in a 4-week gait training program as an adjunct to physical therapy received at a hospital. After the 4-week intervention, both patients were discharged from the hospital, and they returned after 6 months for a follow-up evaluation.
By the end of the 4-week intervention, both patients had shown improvements in scores on the Barthel Index, Berg Balance Scale, Functional Ambulation Categories Scale, 5-m timed walking test, and Motricity Index. In the 6-month follow-up evaluation, both patients continued to have improvements in all outcome measures.
This case report shows that, following the use of an electromechanical gait trainer simultaneously with FES, patients after acute stroke had improvements in gait performance, functional activities, balance, and motor control in the long term.
Upper and lower limb robotic tools for neuro-rehabilitation are effective in reducing motor impairment but they are limited in their ability to improve real world function. There is a need to improve functional outcomes after robot-assisted therapy. Improvements in the effectiveness of these environments may be achieved by incorporating into their design and control strategies important elements key to inducing motor learning and cerebral plasticity such as mass-practice, feedback, task-engagement, and complex problem solving.
This special issue presents nine articles. Novel strategies covered in this issue encourage more natural movements through the use of virtual reality and real objects and faster motor learning through the use of error feedback to guide acquisition of natural movements that are salient to real activities. In addition, several articles describe novel systems and techniques that use of custom and commercial games combined with new low-cost robot systems and a humanoid robot to embody the " supervisory presence" of the therapy as possible solutions to exercise compliance in under-supervised environments such as the home.
Spasticity in stroke patients interferes with coordinated muscle firing patterns of the lower extremity leading to gait abnormalities. The goal of this study was to improve ankle function during walking by augmenting treadmill gait retraining with a visual EMG biofeedback technique. Eight stroke patients who could ambulate between 0.5 and 0.9 m/s participated in the study. The training consisted of 12 sessions of treadmill walking during which the activity of the tibialis anterior and gastrocnemius lateralis muscles of the affected side was displayed on a computer screen. Targets were shown to indicate to the subject when to activate the monitored muscles. Gait evaluations were performed before and after the training period to test the hypothesis that ankle mechanics improved following the intervention. Improvements in gait function were characterized by changes in temporal gait parameters and lower extremity kinematics and kinetics. Subjects showed an increase in gait speed, time of single leg support on the affected side, ankle power generation at push-off and a reduction in knee extensor moment. These results indicate that treadmill gait retraining augmented via visual EMG-biofeedback facilitates improvements in hemiparetic gait.
Previous studies have suggested that task-oriented biofeedback training may be effective for functional motor improvement. The purpose of this project was to design an interactive, multimodal biofeedback system for the task-oriented training of goal-directed reaching. The central controller, based on a user context model, identifies the state of task performance using multisensing data and provides augmented feedback, through interactive 3D graphics and music, to encourage the patients' self-regulation and performance of the task. The design allows stroke patients to train with functional tasks, and receive real-time performance evaluation through successful processing of multimodal sensory feedback. In addition, the environment and training task is customizable. Overall, the system delivers an engaging training experience. Preliminary results of a pilot study involving stroke patients demonstrate the potential of the system to improve patients' reaching performance.
This paper presents a two-part study investigating the use of forearm surface electromyographic (EMG) signals for real-time control of a robotic arm. In the first part of the study, we explore and extend current classification-based paradigms for myoelectric control to obtain high accuracy (92-98%) on an eight-class offline classification problem, with up to 16 classifications/s. This offline study suggested that a high degree of control could be achieved with very little training time (under 10 min). The second part of this paper describes the design of an online control system for a robotic arm with 4 degrees of freedom. We evaluated the performance of the EMG-based real-time control system by comparing it with a keyboard-control baseline in a three-subject study for a variety of complex tasks.
Locomotor training (LT) using a treadmill can improve walking ability over conventional rehabilitation in individuals with hemiparesis, although the personnel requirements often necessary to provide LT may limit its application. Robotic devices that provide consistent symmetrical assistance have been developed to facilitate LT, although their effectiveness in improving locomotor ability has not been well established.
Forty-eight ambulatory chronic stroke survivors stratified by severity of locomotor deficits completed a randomized controlled study on the effects of robotic- versus therapist-assisted LT. Both groups received 12 LT sessions for 30 minutes at similar speeds, with guided symmetrical locomotor assistance using a robotic orthosis versus manual facilitation from a single therapist using an assist-as-needed paradigm. Outcome measures included gait speed and symmetry, and clinical measures of activity and participation.
Greater improvements in speed and single limb stance time on the impaired leg were observed in subjects who received therapist-assisted LT, with larger speed improvements in those with less severe gait deficits. Perceived rating of the effects of physical limitations on quality of life improved only in subjects with severe gait deficits who received therapist-assisted LT.
Therapist-assisted LT facilitates greater improvements in walking ability in ambulatory stroke survivors as compared to a similar dosage of robotic-assisted LT.
This paper introduces a newly developed gait rehabilitation device. The device, called LOPES, combines a freely translatable and 2-D-actuated pelvis segment with a leg exoskeleton containing three actuated rotational joints: two at the hip and one at the knee. The joints are impedance controlled to allow bidirectional mechanical interaction between the robot and the training subject. Evaluation measurements show that the device allows both a "pa- tient-in-charge" and "robot-in-charge" mode, in which the robot is controlled either to follow or to guide a patient, respectively. Electromyography (EMG) measurements (one subject) on eight important leg muscles, show that free walking in the device strongly resembles free treadmill walking; an indication that the device can offer task-specific gait training. The possibilities and limitations to using the device as gait measurement tool are also shown at the moment position measurements are not accurate enough for inverse-dynamical gait analysis.
This paper proposes a human-assisting manipulator teleoperated by electromyographic (EMG) signals and arm motions. The proposed method can realize a new master-slave manipulator system that uses no mechanical master controller. A person whose forearm has been amputated can use this manipulator as a personal assistant for desktop work. The control system consists of a hand and wrist control part and an arm control part. The hand and wrist control part selects an active joint in the manipulator's end-effector and controls it based on EMG pattern discrimination. The arm control part measures the position of the operator's wrist joint or the amputated part using a three-dimensional position sensor, and the joint angles of the manipulator's arm, except for the end-effector part, are controlled according to this position, which, in turn, corresponds to the position of the manipulator's joint. These control parts enable the operator to control the manipulator intuitively. The distinctive feature of our system is to use a novel statistical neural network for EMG pattern discrimination. The system can adapt itself to changes of the EMG patterns according to the differences among individuals, different locations of the electrodes, and time variation caused by fatigue or sweat. Our experiments have shown that the developed system could learn and estimate the operator's intended motions with a high degree of accuracy using the EMG signals, and that the manipulator could be controlled smoothly. We also confirmed that our system could assist the amputee in performing desktop work.
Measuring and understanding infant behavior in a daily living space is required for preventing infant injury. This paper describes a wearable electromyography (EMG) sensor for measuring grasping behavior of infant in a daily living space. The prototype system of a wearable EMG sensor developed by the authors is presented. This paper also describes a method for counting grasping behavior using the developed system. The results of the experiment conducted for an adult show that the system can estimate grasping times within an error of 25%. This paper also presents a downsized wearable EMG sensor.
The study describes how a computer game was used as a kinaesthetic training aid to resolve sensorimotor functional deficits in a patient recovering from a subarachnoid haemorrhage. The game required the patient, a 34-year-old male, to initiate the appropriate finger movements (as used in his daily work) in response to a visual stimulus, and by the recording and display of temporal reaction times, allowed him to quantify and improve his alacrity. Comparisons between pre- and post-training data showed that the patient significantly reduced both the number of moves required and the time taken to complete a game. The study convincingly demonstrates that individually 'tailored' and extensively practised training procedures can reduce functional deficits and even enable full occupational rehabilitation.
Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk. Computationally fast implementations are presented, and the efficacy of the approach is examined through simulation studies and application to data. These studies indicate that in many circumstances dramatic gains in classification accuracy can be achieved.
Surface electromyography (SEMG) systems are utilized throughout the medical industry to study abnormal electrical activity of the human muscle. Historically, SEMG systems employ surface (skin) mounted sensors that transmit electrical muscle data to a computer base via an umbilical cord. A typical SEMG analysis may exercise multiple sensors, each representing a unique data channel, positioned about the patient's body. Data transmission cables are linked between the surface mounted sensor nodes and a backpack worn by the patient. As the number of sensors increases, the patient's freedom of mobility decreases due to the lengthy data cables linked between the surface sensors and the backpack. An N-channel wireless SEMG system has been developed based on the ZigBee wireless standard. The system includes N-channels, each consisting of a wireless ZigBee transmitting modem, an 8-bit microcontroller, a low-pass filter and a pre-amplifier. All channels stream data to a central computer via a wireless receiving modem attached directly to the computer. The data is displayed to the user through graphical development software called LabView. The wireless surface electromyography (WSEMG) system successfully transmits reliable electrical muscle data from the patient to a central computer. The wireless EMG system offers an attractive alternative to traditional wired surface electromyography systems as patient mobility is less compromised.
We developed an interface to the commercial video game Guitar Heroreg III using surface electromyography (EMG) to create a novel training and evaluation device for upperextremity amputees. Rather than pressing the keys with onepsilas fingers as in the normal game, in our modified version a user merely flexes his or her index, middle, or ring finger muscles, and the resulting myoelectric activity is recorded using six or more EMG electrodes placed around the forearm. The acquired data is processed in real-time using pattern recognition algorithms to derive intended motion, and the results are used to control the game. Performance metrics reported by the gamepsilas built-in scoring system are used to evaluate classifier performance. To confirm the functionality of the system, three non-amputee users evaluated the EMG-controlled game (called ldquoAir-Guitar Herordquo) and reported that it was effective, fun, and engaging. Ultimately, we intend to use this system as a performance assay for different types of motor decoding algorithms for dexterous control of upper-extremity neuroprostheses.
Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.
In some stroke rehabilitation programs, robotic systems have been used to aid the patient to train. In this study, a myoelectrically controlled robotic system with 1 degree-of-freedom was developed to assist elbow training in a horizontal plane with intention involvement for people after stroke. The system could provide continuous assistance in extension torque, which was proportional to the amplitude of the subject's electromyographic (EMG) signal from the triceps, and could provide resistive torques during movement. This study investigated the system's effect on restoring the upper limb functions of eight subjects after chronic stroke in a twenty-session rehabilitation training program. In each session, there were 18 trials comprising different combinations of assistive and resistive torques and an evaluation trial. Each trial consisted of five cycles of repetitive elbow flexion and extension between 90 degrees and 0 degrees at a constant velocity of 10 degrees/s. With the assistive extension torque, subjects could reach a more extended position in the first session. After 20 sessions of training, there were statistically significant improvements in the modified Ashworth scale, Fugl-Meyer scale for shoulder and elbow, motor status scale, elbow extension range, muscle strength, and root mean square error between actual elbow angle and target angle. The results showed that the twenty-session training program improved upper limb functions.
To assess the efficacy of biofeedback therapy in poststroke rehabilitation.
A meta-analysis of the reported randomized control trials of biofeedback therapy in poststroke rehabilitation was performed. Data were analyzed using the effect size method and pooled using the Der Simonian-Laird Random Effects Model. Study quality was assessed according to the method of Chalmers.
All included studies were conducted in academically based rehabilitation settings.
Patients were in the rehabilitative phase of their illness. The timing of the intervention from the acute event did vary greatly between and within studies.
Biofeedback therapy was applied to a paretic limb of patients in the study group. Both treatment and control groups received standard physical therapy.
Change in range of motion of a joint of a paretic limb as a result of treatment was examined. This measure was chosen after the eligible studies were evaluated for combinable end points.
A total of eight studies were included in the analysis. The mean effect size for change in lower extremity range of motion as a result of biofeedback therapy was 1.50 (95% confidence interval [CI]: -0.59, 3.59). For the upper extremity the effect size was 2.30 (95% CI: -1.06, 5.66). Both results were not significant at the p < .05 level. Statistical tests showed significant heterogeneity among studies, validating the use of the Random Effects Model.
Results of pooling available randomized control trials do not support the efficacy of biofeedback in restoring the range of motion of hemiparetic joints. Nevertheless, because the calculated mean effect sizes were large, with associated wide confidence intervals, the possibility of a type II error masking an important clinical benefit needs to be considered in evaluating this result.
We have presented a new method for the decomposition of clinical electromyographic signals, NNERVE, which utilizes a novel "pseudo-unsupervised" neural network approach to signal decomposition. In this paper, we present a detailed performance analysis. We present definitions for quantitative performance criteria. NNERVE is shown to be highly reliable over a wide range of neural network architectures. It is also minimally sensitive to learning parameters. The degradations of performance over a wide range of signals and parameters are shown to be gradual, slight and graceful. These characteristics are shown to translate directly into a high degree of robustness over widely varying signals. Real signals obtained from the entire range of patients encountered in clinical situations are shown to be correctly handled without any modifications or adjustments of any parameters. This neural network method is then directly compared to a prior traditional signal processing method and is shown quantitatively to have consistently superior performance on both simulated and real signals. Clinically acceptable performance over a wide range of signals, recorded using standard clinical methodology, and the lack of a need for user interaction, will facilitate the use of motor unit quantitation in routine clinical electromyography.
To quantify the initial deficit, change, and outcome in gait velocity during inpatient rehabilitation following stroke.
The initial deficit on admission to rehabilitation was quantified by comparing 42 stroke patients with 42 controls matched by gender and age. The change in the stroke patients during the next 8 weeks was quantified and gait outcome was compared with functional and normal criteria.
Patients were referred from four inpatient rehabilitation centers at the time of admission following a median of 16.5 days in the acute hospital.
Selection criteria: ability to give informed consent; unilateral first stroke; ability to walk 10 meters.
Patients participated in a median of 17.38 hours of individual physical therapy including a median of 6.92 hours of gait training during the 8 weeks.
Gait velocity.
Gait velocity was initially 38.6% (26.7m/min SD = 14.9) of the performance of controls and improved to 55.1% (38.1m/min). At outcome only 24% exceeded the 5th percentile of controls (48.1m/min) or the velocity required to cross the typical signalled intersection (46.2m/min). The change was only 26% of the initial deficit. Fifty-five percent of the patients improved beyond the 95% confidence intervals surrounding the error of measuring change. Indices of responsiveness indicated that there was a high signal-to-noise ratio and a robust effect size.
Gait velocity discriminated the effect of stroke and the change during rehabilitation.
To examine the efficacy of electromyographic (EMG) biofeedback compared with conventional physiotherapy for improving lower extremity function in stroke patients.
A literature search covering the years 1976 to 1995 in MEDLINE, CINAHL, and EXCERPTA MEDICA.
Studies of adults after stroke, in which the treatment group received biofeedback alone or with conventional physical therapy and the control group received conventional physical therapy. Outcomes included functional measures related to the lower extremity. The study design criterion was that all must be randomized controlled trials.
Study quality was assessed independently by two observers-using eight criteria. Data for analysis were extracted by two observers to ensure accuracy.
For outcomes that were analyzed in more than one study, meta-analyses were done. Seventy-nine studies were identified as potentially relevant and eight studies met the selection criteria. The mean effect sizes were: for ankle dorsiflexion muscle strength, 1.17 (95% CI, .50-1.85; p = .0006); for gait quality, .48 (95% CI, -.06-1.01; p = .08); for ankle range of motion, .07 (95% CI, -.42-0.57; p = .78); for ankle angle during gait, .52 (95% CI, -.18-1.21; p = .14); for stride length, .09 (95% CI, -.56-.73; p = .80); and for gait speed, .31 (95% CI, -.16-.78; p = .20).
The results indicate that EMG biofeedback is superior to conventional therapy alone for improving ankle dorsiflexion muscle strength.
A method of automated detection of onset and termination of rhythmic muscle activity in electromyograms (EMGs) is presented. A threshold level in the EMG is computed, such that amplitudes in the EMG signal exceeding this level indicate muscle activity. The threshold level is determined using a statistical criterion based on the amplitude distribution of the entire EMG signal. The working of the method is illustrated with EMG signals recorded from chewing muscles. EMG signals with a good as well as a worse signal-to-noise ratio are presented. The method can be used for any EMG signal containing cyclic bursts of activity and thus may be applied in studies on rhythmic movements, such as chewing, walking and breathing. An automated method of EMG burst detection has the advantage that large amounts of EMG data can be easily and objectively processed.
Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson's disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.
1.1. The integrated electromyogram parallels tension in human muscle contracting isometrically.2.2. No quantitative relation between EMG and tension exists when a muscle is allowed to change in length.3.3. No quantitative relation between EMG and muscle power exists.4.4. The amplitude of the EMG characteristically diminishes in large human muscles when they are stretched. This phenomenon has not been fully explained.5.5. It is shown that a lag of approximately 0.08 ± 0.02 sec. exists between peak electrical activity and peak tension of human muscle, which should be taken into account in the analysis of rapid movements.
To assess how important community ambulation is to stroke survivors and to assess the relation between the level of community ambulation achieved and other aspects of mobility.
A multicenter observational survey.
Community setting in New Zealand.
One hundred fifteen stroke survivors living at home were referred from physical therapy (PT) services at 3 regional hospitals at the time of discharge and were assessed within 1 week after returning home. Another 15 people with stroke who did not require further PT when discharged were assessed within 2 weeks after they returned home to provide insight into community ambulation status for those without mobility impairment, as recognized by health professionals.
Not applicable.
Self-reported levels of community ambulation ascertained by questionnaire, gait velocity (m/min), Functional Ambulation Categories (FAC) score, and Rivermead Mobility Index (RMI) score.
Mean gait velocity for the participants was 53.9 m/min (95% confidence interval [CI], 52.3-61.1); mean treadmill distance was 165.5 m (95% CI, 141.6-189.5); median RMI score was 14; and median FAC score was 6. Mobility scores for the 15 people who did not require PT were within the normal range. Based on self-reported levels of ambulation, 19 (14.6%) participants were unable to leave the home unsupervised, 22 (16.9%) were walking as far as the letterbox, 10 (7.6%) were limited to walking within their immediate environment, and 79 (60.7%) could access shopping malls and/or places of interest. Participants with different levels of community ambulation showed a significant difference in gait velocity (P<.001). The ability to "get out and about" in the community was considered to be either essential or very important by 97 subjects (74.6%).
Community ambulation is a meaningful outcome after stroke. However, despite good mobility outcomes on standardized measures for this cohort of home-dwelling stroke survivors, nearly one third were not getting out unsupervised in the community. Furthermore, gait velocity may be a measure that discriminates between different categories of community ambulation. These findings may have implications for PT practice for people with mobility problems after stroke.
The purpose of this research was to develop and test an analytical tool that would recognize and classify the surface electromyographic (EMG) signal of co-activating muscles of the leg into pre-defined patterns of muscle activity: burst, tonic, and tonic-burst. Developed to study the task of landing from a jump in children, the pattern recognition technique (PRT) quantifies the full-wave rectified surface EMG signal over a short-duration sampling window by a single linear regression value. Shifting the sampling window across the data string ultimately defines the signal by a set of regression values that produce the recognizable burst, tonic and tonic-burst patterns on a least-squares surface plot. Statistical comparison of the PRT to the classical combination of threshold detection (+2 S.D. of mean baseline activity) and visual inspection proves the PRT to be more reliable on repeated measures for event detection and classification, with a Kappa statistic of 0.83 compared to 0.54 for threshold detection. Application of the PRT to motor control studies is presented for the regulation of the mechanical response of the leg during impact. Responsiveness of the PRT is tested, issues of accuracy and validity are addressed, and limitations in spatial-temporal resolution are identified.
Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on artificial neural networks (ANNs). Logistic regression as well as feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used FFT and autoregressive (AR) model by using maximum likelihood estimation (MLE) of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or nonepileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying AR with MLE in connection with WNN, we obtained novel and reliable classifier architecture. The network is constructed by the error backpropagation neural network using Morlet mother wavelet basic function as node activation function. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN and logistic regression based counterpart. Within the same group, the WNN-based classifier was more accurate than the FEBANN-based classifier, and the logistic regression-based classifier.
Background and purpose:
This report considers the measurement of community ambulation for people with stroke. The conceptual issues underlying measurement of community ambulation are reviewed, and tests that measure either the task itself or at least some of its components are identified and discussed.
Conclusions:
The findings from this review suggest that although some progress has been made toward identifying community ambulation as a stand-alone entity, reliable and valid measures have not yet been developed. Gait speed, which is used often as a proxy measure for community ambulation, does not consistently reflect the level of community ambulation attained, and continued reliance on its use, particularly the 10-m timed walk, is misplaced. The limitations of the measures reviewed here point toward self-report as being the most useful outcome for current clinical use. However, this report highlights the need for research to first inform a theoretical framework for the measurement of community ambulation, from which a measurement tool or a battery of measurements can be developed and tested.
To determine ambulatory activity in a sample of community-dwelling people with chronic hemiparetic stroke and to examine whether deficits in balance and gait and cardiovascular and metabolic fitness are key determinants of ambulatory activity levels.
Descriptive correlational.
Home and community.
Twenty-eight men and 22 women (N=50) over the age 45 years with more than 6 months of hemiparetic gait after ischemic stroke.
Not applicable.
Ambulatory activity (total daily step activity), mobility deficit severity (Berg Balance Scale [BBS] scores, timed 10-m walks), and cardiovascular fitness (energy costs of hemiparetic gait, peak exercise capacity [VO2peak]).
Mean ambulatory activity profiles were extremely low (2837 steps/d vs reported 5000-6000 steps/d in sedentary older adults). Ambulatory activity levels were strongly associated with BBS scores (r=.581, P<.001) and self-selected floor walking velocity (r=.554, P<.001). Participants also had profound cardiovascular deconditioning (mean VO2peak, 11.7+/-2.8 mL.kg(-1).min(-1)). The energy costs of hemiparetic gait were high (8.7+/-1.7 mL.kg(-1).min(-1)), representing 76% of physiologic fitness reserve. Although the relationships of economy of gait and VO2peak to ambulatory activity was not statistically significant, both the VO2peak and the physiologic fitness reserve, as expressed by fractional utilization, were strongly related to balance (r=.374, P=.02; r=-.430, P< .01, respectively.) The BBS predicted 30% of the variance in ambulatory activity.
Ambulatory activity levels and cardiovascular fitness in patients with chronic stroke are extremely low. Mobility deficits, particularly in balance, are associated with low ambulatory activity. Balance-related inactivity may be an important factor in deconditioning. Further studies are needed to better understand whether task-oriented exercise enhances balance and whether increases in daily ambulatory activity yield improved cardiovascular fitness in chronic stroke survivors.
The major goal of neurological rehabilitation includes restoration of mobility. In mobility we include walking, standing up, sitting down, weight shifting from one leg to the other, turning around, initiating and stopping locomotion, as well as climbing stairs. The therapeutic procedures include: different concepts of physiotherapy stressing different features, like force exercise, reduction of spasticity, gait symmetry, utilization of equilibrium reflexes, stepping automation, endurance training, repetition of rhythmic movements etc. The spectrum of available therapies was recently widened by treadmill training with partial body-weight support, gait machines, by functional electrical stimulation (FES), locomotor pharmacotherapy, selective reduction of spasticity by botulinum-toxin (BTX) injections, acoustic and visual cuing and biofeedback. These methods pertaining to gait improvement will also be described. Technical aids should be prescribed earlier, since their costs are usually almost negligible if compared to the costs for a prolonged inpatient treatment. Treadmill training with partial body-weight support in a parachute harness allows early training of postural reactions and stepping. The gait pattern can be considerably improved by FES. A new approach includes mechanical and computer controlled training machines to enable the repetitive training of complex gait cycles without overstressing therapists. First results demonstrate positive effects beyond the classical retraining procedures.
To assess the utility of virtual reality (VR) in stroke rehabilitation.
The Medline, Proquest, AMED, CINAHL, EMBASE and PsychInfo databases were electronically searched from inception/1980 to February 2005, using the keywords: Virtual reality, rehabilitation, stroke, physiotherapy/physical therapy and hemiplegia. Articles that met the study's inclusion criteria were required to: (i) be published in an English language peer reviewed journal, (ii) involve the use of VR in a stroke rehabilitation setting; and (iii) report impairment and/or activity oriented outcome measures. Two assessors independently assessed each study's quality using the American Academy for Cerebral Palsy and Developmental Medicine (AACPDM) grading system.
Eleven papers met the inclusion criteria: Five addressed upper limb rehabilitation, three gait and balance, two cognitive interventions, and one both upper and lower limb rehabilitation. Three were judged to be AACPDM Level I/Weak, two Level III/Weak, three Level IV/Weak and three Level V quality of evidence. All articles involved before and after interventions; three randomized controlled trials obtained statistical significance, the remaining eight studies found VR-based therapy to be beneficial. None of the studies reported any significant adverse effects.
VR is a potentially exciting and safe tool for stroke rehabilitation but its evidence base is too limited by design and power issues to permit a definitive assessment of its value. Thus, while the findings of this review are generally positive, the level of evidence is still weak to moderate, in terms of research quality. Further study in the form of rigorous controlled studies is warranted.
Rising healthcare costs combined with an increase in the number of people living with disabilities due to stroke have created a need for affordable stroke therapy that can be administered in both home and clinical environments. Studies show that robot and computer-assisted devices are promising tools for rehabilitating persons with impairment and disabilities due to stroke. Studies also have shown that highly motivating therapy produces neuromotor relearning that aids the rehabilitative process. Combining these concepts, this paper discusses TheraDrive, a simple, but novel robotic system for more motivating stroke therapy. We conducted two feasibility studies. The paper discusses these studies. Findings demonstrate the ability of the system to grade therapy and the sensitivity of its metrics to the level of motor function in the impaired arm. In addition, findings confirm the ability of the system to administer fun therapy leading to improved motor performance on steering tasks. However, further work is needed to improve the system's ability to increase motor function in the impaired arm.
A possibility for capacitive sensor system for measuring the surface Laplacian electromyogram (Laplacian EMG) was studied under conditions whereby a thin cloth was inserted between the electrodes and the skin of the subject. The system was designed based on a tri-polar concentric electrode unit and on a principle of the capacitive electrode involving the conductive electrodes, the cloth, and the skin. A pilot sensor and detecting circuit using this design were assembled and evaluated to explore the feasibility of this approach. The experimental results showed that the Laplacian EMG obtained in this way was comparable and synchronized with the surface EMG obtained using traditional bipolar electrodes, although its S/N was reduced. Though there are still a lot of challenges to be addressed to achieve practical performances, it seems promising for use in human-machine interfaces because the proposed approach eliminates discomfort due to conventional electrode-to-skin coupling.
Products: Mindball Game Retrieved
- I P I Ab
AB, I. P. I. (2008). Products: Mindball Game Retrieved May 5, 2009, from
http://www.mindball.se/product.html
Heart Disease and Stroke Statistics -2009 Update
- A H Association
Association, A. H. (2009). Heart Disease and Stroke Statistics -2009 Update. Dallas,
Texas: American Heart Association.
Virtual reality-based post-stroke hand rehabilitation
- R Boian
- A Sharma
- C Han
- A Merians
- G Burdea
- S Adamovich
Boian, R., Sharma, A., Han, C., Merians, A., Burdea, G., Adamovich, S., et al.
(2002). Virtual reality-based post-stroke hand rehabilitation. Paper presented
at the Medicine Meets Virtual Reality Conference, Newport Beach, CA.
Gait training for persons with stroke (GTS): Hammel Neurorehabilitation and Research Center
- J Brincks
- J Nielsen
- C Kock-Jensen
Brincks, J., Nielsen, J., & Kock-Jensen, C. (2009). Gait training for persons with
stroke (GTS): Hammel Neurorehabilitation and Research Center, University
of Aarhus, Denmark.
Analog control with surface electromyography
- W Eisenhower
- S Mcmichael
Eisenhower, W., & McMichael, S. (2005). Analog control with surface
electromyography. Electrical and Computer Engineering. University of
Maryland, College Park.