Forearm Electromyography Research Articles

EMG pattern classification to control a hand orthosis for functional grasp assistance after stroke.

Wearable orthoses can function both as assistive devices, which allow the user to live independently, and as rehabilitation devices, which allow the user to regain use of an impaired limb. To be fully wearable, such devices must have intuitive controls, and to improve quality of life, the device should enable the user to perform Activities of Daily Living. In this context, we explore the feasibility of using electromyography (EMG) signals to control a wearable exotendon device to enable pick and place tasks. We use an easy to don, commodity forearm EMG band with 8 sensors to create an EMG pattern classification control for an exotendon device. With this control, we are able to detect a user's intent to open, and can thus enable extension and pick and place tasks. In experiments with stroke survivors, we explore the accuracy of this control in both non-functional and functional tasks. Our results support the feasibility of developing wearable devices with intuitive controls which provide a functional context for rehabilitation.

Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses

In this paper, we quantify the extent to which shoulder orientation, upper-arm electromyography (EMG), and forearm EMG are predictors of distal arm joint angles during reaching in eight subjects without disability as well as three subjects with a unilateral transhumeral amputation and targeted reinnervation. Prior studies have shown that shoulder orientation and upper-arm EMG, taken separately, are predictors of both elbow flexion/extension and forearm pronation/supination. We show that, for eight subjects without disability, shoulder orientation and upper-arm EMG together are a significantly better predictor of both elbow flexion/extension during unilateral (R2=0.72) and mirrored bilateral (R2=0.72) reaches and of forearm pronation/supination during unilateral (R2=0.77) and mirrored bilateral (R2=0.70) reaches. We also show that adding forearm EMG further improves the prediction of forearm pronation/supination during unilateral (R2=0.82) and mirrored bilateral (R2=0.75) reaches. In principle, these results provide the basis for choosing inputs for control of transhumeral prostheses, both by subjects with targeted motor reinnervation (when forearm EMG is available) and by subjects without target motor reinnervation (when forearm EMG is not available). In particular, we confirm that shoulder orientation and upper-arm EMG together best predict elbow flexion/extension (R2=0.72) for three subjects with unilateral transhumeral amputations and targeted motor reinnervation. However, shoulder orientation alone best predicts forearm pronation/supination (R2=0.88) for these subjects, a contradictory result that merits further study.

前腕筋電位に基づく手指動作識別技術

前腕筋電位に基づく手指動作識別技術

Evaluating protocols for normalizing forearm electromyograms during power grip

Many studies use a reference task of an isometric maximum voluntary power grip task in a mid-pronated forearm posture to normalize their forearm electromyographic (EMG) signal amplitude. Currently there are no recommended protocols to do this. In order to provide guidance on the topic, we examined the EMG amplitude of six forearm muscles (three flexors and three extensors) during twenty different maximal voluntary efforts that included various gripping postures, force and moment exertions and compared them to a frequently used normalization task of exerting a maximum grip force, termed the reference task. 16 participants (8 male and 8 female, aged 18–26) were recruited for this study. Overall, maximal muscle activity was produced during the resisted moment tasks. When contrasted with the reference task, the resisted moment tasks produced EMG activity that was up to 2.8 times higher (p<0.05). Although there was no one task that produced greater EMG values than the reference task for all forearm muscles, the resisted flexor and extensor moment tasks produced similar, if not higher EMG activity than the reference task for the three flexors and three extensor muscles, respectively. This suggests that researchers wishing to normalize forearm EMG activity during power gripping prehensile tasks should use resisted flexor and extensor moment tasks to obtain better estimates of the forearm muscles’ maximum electrical activation magnitudes.

Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training.

BackgroundAbnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies.MethodsThirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity.ResultsFacilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001).ConclusionsResidual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice.

Human Hand Motion Analysis With Multisensory Information

In order to study and analyze human hand motions that contain multimodal information, a generalized framework integrating multiple sensors is proposed and consists of modules of sensor integration, signal preprocessing, correlation study of sensory information, and motion identification. Three types of sensors are integrated to simultaneously capture the finger angle trajectories, the hand contact forces, and the forearm electromyography (EMG) signals. To facilitate the rapid acquisition of human hand tasks, methods to automatically synchronize and segment manipulation primitives are developed in the signal preprocessing module. Correlations of the sensory information are studied by using Empirical Copula and demonstrate that there exist significant relationships between muscle signals and finger trajectories and between muscle signals and contact forces. In addition, recognizing different hand grasps and manipulations based on the EMG signals is investigated by using Fuzzy Gaussian Mixture Models (FGMMs) and results of comparative experiments show FGMMs outperform Gaussian Mixture Models and support vector machine with a higher recognition rate. The proposed framework integrating the state-of-the-art sensor technology with the developed algorithms provides researchers a versatile and adaptable platform for human hand motion analysis and has potential applications especially in robotic hand or prosthetic hand control and human-computer interaction.

Placement of forearm surface EMG electrodes in the assessment of hand loading in manual tasks

Surface electromyography (EMG) is commonly used to study the loading of the forearm. Pro-supination movements cause surface electrodes to move in relation to the underlying muscles. We studied the effects of different electrode locations and forearm postures on the association between the EMG signals and external hand load in a laboratory experiment. Eleven subjects performed simulated work tasks with the forearm in neutral, pronated or supinated postures and with systematic variation of external load. The tasks included isometric gripping, pushing and pulling, and lifting and lowering weights. Surface EMG was recorded by six pairs of electrodes located on the forearm. The associations were studied using multiple regression models. EMG activity varied according to the forearm posture, location of electrodes and type of simulated task. Variation was lowest with a through-forearm setting of electrodes. This setting also showed the highest correlation between external loads and the EMG activity [coefficient of determination (R 2) = 0.25–0.66]. Practitioner Summary: Moving of surface electrodes in relation to the underlying muscles interferes with the assessment of loading in ergonomic settings. This laboratory experiment showed that a through-forearm location of electrodes seems to be an optimal option in the assessment of forearm loading.

An accurate forearm EMG signal classification method using two‐channel electrode

AbstractAn accurate electromyography (EMG) classification algorithm to control a virtual hand prosthesis with 12 degrees of freedom using two surface EMG electrodes is presented in this paper. We propose the application of independent component analysis (ICA) for blind‐source separation of the EMG signals obtained from two electrodes. One of the problems affecting the EMG classification accuracy is the location dependence of the EMG signal due to the superposition of signals from multiple sources. ICA is used to separate the two signals obtained from two surface electrodes into two independent EMG signals prior to the feature extraction and classification processes. We demonstrate that the EMG classification accuracy can be improved using the ICA algorithm. We also propose a novel eigen‐based feature that is extracted from the short‐time Fourier transform (STFT) magnitude spectrum. Our new feature not only decreases feature dimensions but also performs better than other well‐known features. We also implement the EMG classification scheme on the virtual robot arm. The performance shows promising result as indicated by a decrease in the Davies–Bolden (DB) index after applying the ICA © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Intra-session and inter-day reliability of forearm surface EMG during varying hand grip forces

Surface electromyography (EMG) is widely used to evaluate forearm muscle function and predict hand grip forces; however, there is a lack of literature on its intra-session and inter-day reliability. The aim of this study was to determine reliability of surface EMG of finger and wrist flexor muscles across varying grip forces. Surface EMG was measured from six forearm flexor muscles of 23 healthy adults. Eleven of these subjects undertook inter-day test–retest. Six repetitions of five randomized isometric grip forces between 0% and 80% of maximum force (MVC) were recorded and normalized to MVC. Intra- and inter-day reliability were calculated through the intraclass correlation coefficient (ICC) and standard error of measurement (SEM).Normalized EMG produced excellent intra-session ICC of 0.90 when repeated measurements were averaged. Intra-session SEM was low at low grip forces, however, corresponding normalized SEM was high (23–45%) due to the small magnitude of EMG signals. This may limit the ability to evaluate finer forearm muscle function and hand grip forces in daily tasks. Combining EMG of functionally related muscles improved intra-session SEM, improving within-subject reliability without taking multiple measurements. Removing and replacing electrodes inter-day produced poor ICC (ICC<0.50) but did not substantially affect SEM.

Forearm EMG Signal Classification Based on Singular Value Decomposition and Wavelet Packet Transform Features

The myoelectric signal (MES) with broad applications in various areas especially in prosthetics and myoelectric control, is one of the biosignals utilized in helping humans to control equipments. In this paper, a technique for feature extraction of forearm electromyographic (EMG) signals using wavelet packet transform (WPT) and singular value decomposition (SVD) is proposed. In the first step, the WPT is employed to generate a wavelet decomposition tree from which features are extracted. In the second step, an algorithm based on singular value decomposition (SVD) method is introduced to compute the feature vectors for every hand motion. This technique can successfully identify eight hand motions including forearm pronation, forearm supination, wrist flexion, wrist abduction, wrist adduction, chuck grip, spread fingers and rest state. These motions can be obtained by measuring the surface EMG signal through sixteen electrodes mounted on the pronator and supinator teres, flexor digitorum, sublimas, extensor digitorum communis, and flexor and extensor carpi ulnaris. Moreover, through quantitative comparison with other feature extraction methods like entropy concept in this paper, SVD method has a better performance. The results showed that proposed technique can achieve a classification recognition accuracy of over 96% for the eight hand motions.

Assessment of electrosurgical hand controls integrated into a laparoscopic grasper

The aim of this study was to quantitatively and qualitatively determine the optimal ergonomic placement of novel electrosurgical hand controls integrated into a standard laparoscopic grasper to optimize functionality. This device will allow laparoscopic surgeons to hand-operate standard electrosurgical equipment, eliminating the use of electrosurgical foot pedals, which are prone to activation errors and cause uncomfortable body positions for the physician. Three hand control designs were evaluated by 26 participants during the performance of four basic inanimate laparoscopic electrosurgical tasks. Task completion time, actuation force, forearm electromyography (EMG) and user preference were evaluated for each hand control design. Task speed was controlled using a metronome to minimize subject variability, and resulted in no significant completion time differences between task types (P > 0.05). Hand control design 1 (CD 1) resulted in the ability to generate significantly greater actuation force for three of the four tasks (P < 0.05) with minimal forearm muscle activation. Additionally, CD 1 was rated significantly better for comfort and ease-of-use compared to the other two hand control designs (P < 0.05). As a result, CD 1 was determined to be an advantageous ergonomic design for the novel electrosurgical hand controls.

Feature extraction of forearm EMG signals for prosthetics

This paper presents a new technique for feature extraction of forearm electromyographic (EMG) signals using a proposed mother wavelet matrix (MWM). A MWM including 45 potential mother wavelets is suggested to help the classification of surface and intramuscular EMG signals recorded from multiple locations on the upper forearm for ten hand motions. Also, a surface electrode matrix (SEM) and a needle electrode matrix (NEM) are suggested to select the proper sensors for each pair of motions. For this purpose, EMG signals were recorded from sixteen locations on the forearms of six subjects in ten hand motion classes. The main goal in classification is to define a proper feature vector able to generate acceptable differences among the classes. The MWM includes the mother wavelets which make the highest difference between two particular classes. Six statistical feature vectors were compared using the continuous form of wavelet packet transform. The mother wavelet functions are selected with the aim of optimum classification between two classes using one of the feature vectors. The locations where the satisfactory signals are captured are selected from several mounted electrodes. Finally, three ten-by-ten symmetric MWM, SEM, and NEM represent the proper mother wavelet function and the surface and intramuscular selection for recording the ten hand motions.

Synchronised Paroxysmal Ocular Tilt Reaction and Limb Dystonia

ABSTRACTA case of paroxysmal ocular tilt reaction due to a midbrain cavernoma showing co-existing limb dystonia is described with particular reference to the synchronisation of eye and limb paroxysms. The single case has been observed over a 20-year period with video documentation of eye and limb movements. The function of the relevant neuro-anatomical circuits have been investigated. The now 51-year-old female patient suffered from a right midbrain haemorrhage 20 years earlier. She developed a synchronized paroxysmal ocular tilt reaction and focal limb dystonia, associated with a unilateral startle-like response. Video-oculography and electromyography during paroxysms demonstrated left forearm electromyography spikes time locked with bursts of ocular torsion. Whilst the ocular tilt reaction may be explained by a single irritative lesion of the right interstitial nucleus of Cajal, additional involvement of the right rostral interstitial nucleus of the medial longitudinal fasciculus was suggested by abnormal binocular scleral coil recordings of the torsional vestibulo ocular response. There was a marked unilateral acoustic startle-like response, not explained by damage to either nucleus. Treatment with acetazolamide abolished the attacks. The observations suggest that the coexistence of skew deviation, conjugate ocular torsion and limb dystonia in meso-diencephalic lesions may occur because neural centres controlling these eye and limb movements are closely related anatomically, rather than through the activation of a “hard wired” physiological synkinesis. We propose that the paroxysms are generated within the zona incerta where excitatory auditory, somatosensory and motor neurons are densely packed and extensively project to centres of gaze and motor control.

Towards an EMG-Controlled Prosthetic Hand Using a 3-D Electromagnetic Positioning System

This paper proposes a novel method of using electromyographic (EMG) potentials generated by the forearm muscles during hand and finger movements to control an artificial prosthetic hand worn by an amputee. Surface EMG sensors were used to record a sequence of forearm EMG potential signals via a PC sound card and a novel 3-D electromagnetic positioning system together with a data-glove mounted with 11 miniature electromagnetic sensors used to acquire corresponding human hand pose in real time. The synchronized measurements of hand posture and associated EMG signals stored as prototypes embody a numerical expression of the current hand shape in the form of a series of data frames, each comprising a set of postures and associated EMG data. This allows a computer generated graphical 3-D model, combined with synthesized EMG signals, to be used to evaluate the approach. This graphical user interface could also enable handicapped users to practice controlling a robotic prosthetic hand using EMG signals derived from their forearm muscles. We believe this task might be made easier using a dictionary of stored task-specific prototype data frames acquired from able-bodied users. By comparing the resulting EMG data frames with stored prototypes, the most likely data frame sequence can be identified and used to control a robotic hand so that it carries out the user's desire. We explore the feasibility of this approach by applying frequency analysis on the signal derived from a multichannel EMG measurement device and identify pattern recognition techniques in the time and frequency domains to determine plausible hand shapes. This approach offers several advantages over existing methods. First, it simplifies the classification procedure, saving computational time and the requirement for the optimization process, and second, it increases the number of recognizable hand shapes, which in turn improves the dexterity of the prosthetic hand and the quality of life for amputees. The database of EMG prototypes could be employed to optimize the accuracy of the system within a machine learning paradigm. By making a range of EMG prototype databases available, prosthetic hand users could train themselves to use their prosthesis using the visual reference afforded by the virtual hand model to provide feedback

Changes in neuromuscular function due to intermittently increased workload during repetitive work in cold conditions

This study attempted to determine whether repetitive work in the cold affects the number of electromyographic (EMG) gaps and whether cold-induced deterioration in neuromuscular function can be restored by altering work intensity. During 2 hours of simulated sausage packing, the occurrence of EMG gaps was studied in eight upper-extremity muscles at 19 degrees C and 4 degrees C. The additional effect of continuous, light repetitive work [wrist flexion-extension 10% of the maximal voluntary contraction (MVC), Cwork] and intermittently increased workload (10% or 30% MVC) (Iwork) on forearm muscle EMG gaps, the median frequency of the EMG spectrum, EMG activity, and muscle fatigue (as indicated by changes in the MVC of wrist flexion) at 4 degrees C was studied. During work simulation, the mean skin temperature decreased from 33.3 (SD 0.1) degrees C to 30.6 (SD 0.2) degrees C (P<0.05), and the number of EMG gaps diminished in seven muscles by 1.4-68.2% (P<0.05). During Iwork there were 44% and 37% more EMG gaps in the forearm flexors and extensors, respectively, and the median frequency shifted to higher frequencies (P<0.05) as compared with the situation in Cwork. The average EMG activity of the forearm flexors was 13% lower (P<0.05) during Iwork than during Cwork. Wrist flexion MVC decreased 18% and 15% in Cwork and Iwork, respectively. The cold-induced decrease in the number of EMG gaps and increased muscle strain and fatigue can be, at least partially, restored by intermittently increasing the workload (ie, breaking the monotonous work cycle).

Comparison of Shock Transmission and Forearm Electromyography Between Experienced and Recreational Tennis Players During Backhand Strokes

To test the hypothesis that recreational tennis players transmit more shock impact from the racket to the elbow joint than experienced tennis players during the backhand stroke. Also, to test whether recreational tennis players used higher electromyographic (EMG) activities in common wrist extensor and flexor around epicondylar region at follow-through phase. A repeated-measure, cross-sectional study. National College of Physical Education and Sports at Taipei, Taiwan. Twenty-four male tennis players with no abnormal forearm musculoskeletal injury participated in the study. According to performance level, subjects were categorized into 2 groups: experienced and recreational. Impact transmission and wrist extensor-flexor EMG for backhand acceleration, impact, and follow-through phases were recorded for each player. An independent t test with a significance level of 0.05 was used to examine mean differences of shock impact and EMG between the 2 test groups. One-way ANOVA associated with Tukey multiple comparisons was used to identify differences among different impact locations and EMG phases. Experienced athletes reduced the racket impact to the elbow joint by 89.2%, but recreational players reduced it by only 61.8%. The largest EMG differences were found in the follow-through phase (P<0.05). Experienced athletes showed that their extensor and flexor EMGs were at submaximal level for follow-through phase, whereas recreational players maintained their flexor and extensor EMGs at either supramaximal or maximal level. Our results support the hypothesis that recreational players transmit more shock impact from the racket to the elbow joint and use larger wrist flexor and extensor EMG activities at follow-through phase of the backhand stroke. Follow-through control is proposed as a critical factor for reduction of shock transmission. Clinicians or trainers should instruct beginners to quickly release their grip tightness after ball-to-racket impact to reduce shock impact transmission to the wrist and elbow.

The development and validation of equations to predict grip force in the workplace: contributions of muscle activity and posture

The inherent difficulty of measuring forces on the hand in ergonomic workplace assessments has led to the need for equations to predict grip force. A family of equations was developed, and validated, for the prediction of grip force using forearm electromyography (six finger and wrist muscles) as well as posture of the wrist (flexed, neutral and extended) and forearm (pronated, neutral, supinated). Inclusion of muscle activity was necessary to explain over 85% of the grip force variance and was further improved with wrist posture but not forearm posture. Posture itself had little predictive power without muscle activity (<1%). Nominal wrist posture improved predictive power more than the measured wrist angle. Inclusion of baseline muscle activity, the activity required to simply hold the grip dynamometer, greatly improved grip force predictions, especially at low force levels. While the complete model using six muscles and posture was the most accurate, the detailed validation and error analysis revealed that equations based on fewer components often resulted in a negligible reduction in predictive strength. Error was typically less than 10% under 50% of maximal grip force and around 15% over 50% of maximal grip force. This study presents detailed error analyses to both improve upon previous studies and to allow an educated decision to be made on which muscles to monitor depending on expected force levels, costs and error deemed acceptable by the potential user.

Effect of concrete block weight and wall height on electromyographic activity and heart rate of masons

Work-related musculoskeletal disorders (MSDs) are common among construction workers, such as masons. Few interventions are available to reduce masons' exposure to heavy lifting, a risk factor for MSDs. The purpose of this study was to determine whether one such intervention, the use of light-weight concrete blocks (LWBs), reduces physiological loads compared to standard-weight blocks (SWBs). Using a repeated measures design, 21 masons each constructed two 32-block walls, seven courses (rows) high, entirely of either SWBs or LWBs. Surface electromyography (EMG), from arm and back muscles, and heart rate was sampled. For certain muscles, EMG amplitudes were slightly lower when masons were laying LWBs compared to SWBs. Upper back and forearm extensor EMG amplitudes were greater for the higher wall courses for both block weights. There were no significant differences in heart rate between the two blocks. Interventions that address block weight and course height may be effective for masons.

Job enlargement and mechanical exposure variability in cyclic assembly work

Cyclic assembly work is known to imply a high risk for musculoskeletal disorders. To have operators rotate between work tasks is believed to be one way of decreasing this risk, since it is expected to increase variation in mechanical and psychological exposures (physical and mental loads). This assumption was investigated by assessing mechanical exposure variability in three assembly tasks in an electronics assembly plant, each on a separate workstation, as well as in a ‘job enlargement’ scenario combining all three stations. Five experienced operators worked for 1 h on each station. Data on upper trapezius and forearm extensor muscle activity were obtained by means of electromyography (EMG), and working postures of the head and upper arms were assessed by inclinometry. The cycle-to-cycle variance of parameters representing the three exposure dimensions: level, frequency and duration was estimated using ANOVA algorithms for each workstation separately as well as for a balanced combination of all three. For a particular station, the variability of trapezius EMG activity levels relative to the mean was higher than for extensor EMG: between-cycles coefficients of variation (CV) about 0.15 and 0.10, respectively. A similar relationship between CV applied to the parameter describing frequency of EMG activity. Except for head inclination levels, the between-cycles CV was larger for posture parameters than for EMG. The between-cycles variance increased up to six fold in the job enlargement scenario, as compared to working at only one station. The difference in mean exposure between workstations was larger for trapezius EMG parameters than for forearm extensor EMG and postures, and hence the effect of job enlargement on exposure variability was more pronounced for the trapezius. For some stations, job enlargement even implied less cycle-to-cycle variability in forearm extensor EMG parameters than working at that station only. Whether the changes in exposure variability associated with job enlargement were sufficient to imply a decreased risk for musculoskeletal disorders is not known.

Effect of Computer Keyboard Slope on Wrist Position and Forearm Electromyography of Typists Without Musculoskeletal Disorders

Positioning a computer keyboard with a downward slope reduces wrist extension needed to use the keyboard and has been shown to decrease pressure in the carpal tunnel. However, whether a downward slope of the keyboard reduces electromyographic (EMG) activity of the forearm muscles, in particular the wrist extensors, is not known. Sixteen experienced typists participated in this study and typed on a conventional keyboard that was placed on slopes at angles of 7.5, 0, -7.5, and -15 degrees. Electromyographic activity of the extensor carpi ulnaris (ECU), flexor carpi ulnaris (FCU), and flexor carpi radialis (FCR) muscles was measured with surface electrodes, while the extension and ulnar deviation angles of the right and left wrists were measured with electrogoniometers. Wrist extension angle decreased from approximately 12 degrees of extension while typing on a keyboard with a 7.5-degree slope to 3 degrees of flexion with the keyboard at a slope of -15 degrees. Although the differences were in the range of 1% to 3% of maximum voluntary contraction (MVC), amplitude probability distribution function (APDF) of root-mean-square EMG data points from the ECU, FCU, and FCR muscles varied across keyboard slopes. Wrist extension decreased as the keyboard slope decreased. Furthermore, a slight decrease in percentage of MVC of the ECU muscle was noted as the keyboard slope decreased. Based on biomechanical modeling and published work on carpal tunnel pressure, both of these findings appear to be positive with respect to comfort and fatigue, but the exact consequences of these findings on the reduction or prevention of injuries have yet to be determined. The results may aid physical therapists and ergonomists in their evaluations of computer keyboard workstations and in making recommendations for interventions with regard to keyboard slope angle.