Forearm Muscle Activity Research Articles

Characteristics, Relationships, and Differences in Muscle Activity and Impact Load Attenuation During Tennis Forehand Stroke with Different Grips.

In forehand strokes with different grips in tennis, the forearm muscle activities, the distribution and attenuation of the impact loads, and the effects of the muscles on the impact load attenuation exhibited different characteristics. This study aimed to explore these characteristics by analyzing electromyography (EMG) and acceleration data, and comparing the differences between the Eastern and Western grips. Fourteen level II or above tennis players (ten males, aged 22.4 ± 3.6 years; four females, aged 19.8 ± 2.0 years) were recruited and instructed to perform forehand strokes using the Eastern and Western grips, respectively. The EMG of eight forearm muscles and the acceleration data at the ulnar and radial sides of the wrist and elbow were collected. The root mean square (RMS), the peaks of the impact load, the amplitude of impact load attenuation (AC), and the jerk value (Jerk) were calculated. The cross-correlation coefficients and time delays of EMG-EMG, EMG-AC, and EMG-jerk were obtained using the cross-correlation method. The results showed that in the Eastern grip group (group E), the RMS of the flexor carpi ulnaris (FCU) was significantly greater than that in the Western grip group (group W). In group E, the peaks of impact load, AC, and Jerk on the Y axis of the wrist ulnar side were all significantly higher than those in group W. The activity of the extensor digitorum commonis (EDC) had significantly different effects on the amplitude and rate of impact load attenuation at specific locations in different grips, especially at the elbow (p < 0.05). The conclusion indicated that the FCU exhibited higher levels of EMG activity in the Eastern grip. This grip responded to greater impact loads with more substantial and rapid attenuation on the wrist ulnar side. Furthermore, the EDC appeared to contribute more to the amplitude of impact load attenuation in the Western grip and to have a more significant influence on the rate of impact load attenuation in the Eastern grip, especially at the elbow. These results suggest that tennis players and coaches should pay more attention to improving the strength of the EDC and FCU, which can improve sports performance and comfort, as well as prevent sports injuries.

The use of lifting straps during snatch alters muscle activation patterns

The snatch is prevalent in competitive and recreational lifting as well as strength training. Lifting straps (LS) are an accessory that wrap around the hand and the barbell, reducing the limitations of grip strength. However, this benefit has not been quantitatively supported for the snatch. The aim of this study was to compare muscle activation patterns between using LS and not using them at 80% of the snatch one-repetition maximum in a group of twelve sub-elite male weightlifters. It was hypothesized that LS would decrease forearm muscle activation and increase larger muscle group activation. Eight dominant-side muscles located in the lower limbs, back, shoulders, and upper limbs were measured via electromyography. A two-way analysis of variance (ANOVA), followed by a Tukey Pairwise comparison, revealed that LS significantly impacted (p = 0.039) muscle activation in the snatch. Specifically, latissimus dorsi activation increased the most (17.2 ± 55.7%), while biceps brachii activation decreased the most (−8.0 ± 37.0%). Comparing muscle activation across different phases of the lift showed that LS decreased forearm and bicep brachii activation by 16.0 ± 25.2% and 7.1 ± 35.7% respectively during the pull phase, and increased vastus lateralis, latissimus dorsi, and medial deltoid activation between the second part of the first pull and the catching position, with a corrected effect size exceeding 1.5. These results support the hypothesis that LS decrease forearm activation, which could reduce grip strength limitations and consequently, reduce problems associated with fatigue. Increases in vastus lateralis activation with the use of LS suggest a greater training stimulus during the snatch, which can help develop leg strength.

Effects of robot-assisted hand function therapy on brain functional mechanisms: a synchronized study using fNIRS and sEMG.

Robot-assisted hand function therapy is pivotal in the rehabilitation of patients with stroke; however, its therapeutic mechanism remains elusive. Currently, research examining the impact of robot-assisted hand function therapy on brain function in patients with stroke is scarce, and there is a lack of studies investigating the correlation between muscle activity and alterations in brain function. This study aimed to investigate the correlation between forearm muscle movement and brain functional activation by employing the synchronized use of functional near-infrared spectroscopy and surface electromyography methods. Moreover, it sought to compare neural activity patterns during different rehabilitation tasks and refine the mechanism of robot-assisted hand function therapy for post-stroke hand function impairments. Stroke patients with hand dysfunction underwent three sessions of robot-assisted hand function therapy within 2 weeks to 3 months of onset. The fNIRS-sEMG synchronous technique was used to observe brain function and forearm muscle activation. Ten participants were randomly assigned to receive mirror, resistance, or passive rehabilitation training. During the intervention, cortical and muscle activation information was obtained using fNIRS and electromyographic signals. The primary outcomes included changes in oxyhemoglobin concentration and root mean square of surface electromyography. Compared to the resting state, the Oxy-Hb concentration in the brain regions involved in three rehabilitation tasks with robot-assisted hand function therapy significantly increased (p < 0.05). Mirror therapy significantly enhanced the prefrontal cortex and the superior frontal cortex activation levels. In contrast, resistance therapy significantly promoted the activation of the supplementary motor area and the premotor cortex. Passive rehabilitation tasks showed some activation in the target brain area premotor cortex region. Robot-assisted hand function therapy has shown that forearm muscle movement is closely related to oxygenated hemoglobin concentration activity in specific brain regions during different rehabilitation tasks. The simultaneous sEMG-fNIRS study found a significant correlation between muscle movement and brain activity after stroke, which provides an important basis for understanding the treatment mechanism of hand function impairment.

Effects of the IronHand® Soft Exoskeleton on Forearm Muscle Activity During in Field Automotive Assembly Tasks

OCCUPATIONAL APPLICATIONS When compared to not using a soft-hand exoskeleton, the IronHand® caused both increases and decreases in forearm muscle activity while completing automotive assembly tasks. Surprisingly, although there were reductions in muscle activity when wearing the IronHand®, only a few of these decreases resulted in muscle activity changing such that they fell to below recommended ergonomic thresholds. Despite this, some individuals in our study clearly benefited from the device, and this suggests that there is potential for widespread use of such a device if fine tuned to the individual and task demands. Much work is still required for a design that will allow for optimal physical benefit.

An Ergonomic Golf Grip Leads to Lower Forearm Muscle Activity - A Prospective Case Series of 30 Right-Handed Amateur and Professional Golfers

BackgroundThe elbow is a common site for overuse injuries in golfers. Tendinopathies, such as medial and lateral epicondylitis, are frequently diagnosed in amateur and professional golfers. The aim of our study was to determine the effect of an ergonomic golf grip on forearm muscle activity during the five phases of the golf swing.MethodsThirty right-handed golfers with a mean age of 32 years (range, 18–70 years) and a mean handicap of 15 (range, 0–43) performed 10 golf swings with a standard and ergonomic golf grip respectively. The mean and maximum muscle activity of the Musculus (M.) extensor carpi radialis brevis (ECRB), M. flexor carpi ulnaris (FCU), M. pronator teres (PT) and M. biceps brachii (BB) of the lead and trail arms were assessed during the five phases of the golf swing using surface electromyography (EMG). Subgroup analyses were performed regarding sex, playing ability (handicap < 10 vs. ≥10), weekly playing time (≤ 5 h, 5–20 h, > 20 h) and preexisting elbow pain during golfing (VAS < 2 vs. VAS ≥ 2). Significance was set at p < 0.05.ResultsAn ergonomic golf grip resulted in a reduction in muscle activity in at least one but up to three consecutive phases of the golf swing for the ECRB, FCU and PT of the lead arm and for the PT of the trail arm. Amateurs, a playing time < 20 h per week and golfers without preexisting elbow pain were factors that were associated with greater reductions in muscle activity.ConclusionForearm muscle activity can be decreased using an ergonomic golf grip, indicating the possible role of an ergonomic golf grip as a preventive measure against overuse syndromes such as medial and lateral epicondylitis.Trial registration numberThis study was retrospectively registered at the German Clinical Trials Register DRKS-ID: DRKS00033732 (01/03/2024).

Differentiating hand gestures from forearm muscle activity using machine learning

This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the algorithm accuracies increased with RF from 85% to 97% due to the increased temporal resolution. It was also noticed that the RF performed better with an accuracy of 85% than the NN with accuracy 80% when the temporal resolution was smaller, indicating the RF will be efficient when quick-response time is important. As the window size increases, the NN showed higher performance, suggesting that NN will be useful when higher accuracy is required. Future studies should increase the sample size, include more hand gestures, use different feature extraction methods and test different algorithms to improve the accuracy and efficiency of the system.

Identification of physically demanding work tasks among cleaners: field study using electromyography.

While chronic forearm pain is a common and debilitating condition among cleaners, the most physically demanding tasks remain unidentified. The present field study examines forearm muscle activity during 9 common cleaning tasks in a real working environment. Seven healthy cleaners participated in this study (age: 35.17 ± 9.62 yr; height: 168.17 ± 8.06 cm; weight: 77.14 ± 13.78 kg; experience: 5.60 ± 3.29 yr). Surface wireless electromyography (EMG) was recorded from 2 muscles on both sides of the upper limb, flexor carpi ulnaris (FCU), and extensor carpi radialis (ECR), and normalized to maximal voluntary isometric contractions (MVIC). Top-3 demanding high-force tasks (90th percentile EMG) were the rough floor, dirty rough floor, and office floor mopping for the FCU, and mopping high walls, ceiling mopping, and baize cleaning for the ECR. Top-3 static work tasks (10th percentile EMG) were mopping low walls, ceiling mopping, and dirty rough floors mopping for the FCU and mopping of high walls, low walls, and ceiling for the ECR. The study identified the forearm muscles' most physically demanding work tasks during cleaning tasks. The development of better working tools is recommended to avoid high-force overload as well as prolonged static overload of these muscles in cleaners.

A tensor decomposition reveals ageing-induced differences in muscle and grip-load force couplings during object lifting

Do motor patterns of object lifting movements change as a result of ageing? Here we propose a methodology for the characterization of these motor patterns across individuals of different age groups. Specifically, we employ a bimanual grasp-lift-replace protocol with younger and older adults and combine measurements of muscle activity with grip and load forces to provide a window into the motor strategies supporting effective object lifts. We introduce a tensor decomposition to identify patterns of muscle activity and grip-load force ratios while also characterizing their temporal profiles and relative activation across object weights and participants of different age groups. We then probe age-induced changes in these components. A classification analysis reveals three motor components that are differentially recruited between the two age groups. Linear regression analyses further show that advanced age and poorer manual dexterity can be predicted by the coupled activation of forearm and hand muscles which is associated with high levels of grip force. Our findings suggest that ageing may induce stronger muscle couplings in distal aspects of the upper limbs, and a less economic grasping strategy to overcome age-related decline in manual dexterity.

Understanding Forearm Muscle Activity during Everyday Common Grasps: Insights for Rehabilitation, Prosthetic Control, and Human–Machine Interaction

The specific role of forearm muscles in the development of activities of daily living (ADL) remains unknown. Consequently, studying forearm muscle activity during the most commonly used grasps in ADL would yield valuable insights for hand function evaluation, rehabilitation, and advancements in prosthetic control. In this study, forearm muscle activity was analyzed in 22 healthy subjects, examining seven representative forearm areas during the performance of seven types of grasps at 50% of maximum effort. A Scheirer–Ray–Hare test revealed significant differences for grasp, spot, and their interaction (α &lt; 0.05), but not for repetition (and its interactions). Specific significant differences between grasps were found in specific spots by means of Bonferroni post hoc analyses, ensuring the possibility to discriminate between grasps, which is key to identifying the person’s intention to perform a particular grasp. The median values ranged from 4.4% to 32.8%, depending on the spot and grasp, with small 95% confidence intervals (0.5% to 5.5%). Cylindrical grasp requires the highest muscle activity among all spots, while lateral pinch demands the least. The findings elucidate the contribution, coordination, and function of each muscle in relation to each grasp, with implications for rehabilitation, prosthetics, and telerobotic and teleoperation systems.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

Objective. We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control. Approach. Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects. Main results. We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p < 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals. Significance. We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

Hand gesture recognition with deep residual network using Semg signal.

To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition. The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset. The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924. The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition.

Kinesiotaping versus counterforce brace in the management of lateral elbow tendinopathy

Purpose: The main purpose of the current study was to assess whether counter force brace (CFB) offers any additional value over Kinesiotaping (KT) in the management of tennis elbow. Materials and Methods: Forty-five females with clinically diagnosed tennis elbow participated in the current study. They were equally assigned into three groups of equal numbers. 12 sessions over 4 weeks of treatment interventions; experimental group A received the traditional physiotherapy program in addition to KT. Group B received the physiotherapy program in addition to the CFB. Group C (Control Group) received the physiotherapy program only. The degree of tennis elbow pain and myoelectrical activities of forearm muscles were measured. Results: There was a statistically significant reduction of pain in the experimental group A (KT) in comparison with the control group (p = 0.000), while a statistically significant reduction of pain (p = 0.000) and improvement of the myoelectrical activities of the forearm muscles (p = 0.000) were detected in the experimental group B (CFB) in comparison with the control group. In addition, the statistically significant reduction of pain (p = 0.000) and improvements in wrist flexors and extensors (p = 0.001 and p = 0.000, respectively) were detected in CFB compared with KT. Conclusion: Compared to KT or a physical therapy program alone, the use of CFB in the treatment of lateral elbow tendinopathy may be beneficial, especially in reducing pain and enhancing the muscle activities of the elbow flexors and extensors.

Detecting method of optimal exercise posture using autoencoder: Utilizing surface electromyography and textile stretch sensor

This study aims to validate the optimal posture for the Dumbbell Biceps Curl (DBC) exercise using Textile stretch sensors and Surface Electromyography (sEMG), and then detect inaccurate posture using a Sparse Autoencoder. To validate the optimal DBC exercise posture, we measured the effects of wrist supination and the angle of the upper body and elbow on the biceps and forearm muscles. A wrist sleeve-shaped Textile stretch sensor detects wrist supination, and sEMG measures biceps and forearm muscle activation. The experiment results confirmed that an angle between the upper body and the elbow within 90°, coupled with wrist supination, constitutes the most efficient posture, maximizing the activation of the biceps while minimizing the synergistic effects of the forearm muscles. Subsequently, this posture was learned through a Sparse Autoencoder, and the Root Mean Square Error values of the trained model were lowest in the optimal posture (Biceps: 0.090, Forearm: 0.076). This suggests that Sparse Autoencoder could be useful in identifying inaccurate exercise postures. In summary, this study aims to develop an exercise posture feedback system through the integration of modern exercise physiology and technology, particularly the fusion of AI and sensor technology. The goal is to propose the potential to detect and correct inaccurate or unsafe exercise postures.

Electromyography and kinematics data of the hand in activities of daily living with special interest for ergonomics

This work presents a dataset of human hand kinematics and forearm muscle activation collected during the performance of a wide variety of activities of daily living (ADLs), with tagged characteristics of products and tasks. A total of 26 participants performed 161 ADLs selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices). The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. Data of more than 4100 ADLs is presented in this dataset as MATLAB structures with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour. The dataset is accompanied with a custom data visualization application (ERGOMOVMUS) as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or participants’ characteristics.

Investigating the influence of keyboard inclinations on sitting and standing workstations

Given the growing global computer workforce, concerns exist about the escalation of computer related injury with Carpal tunnel syndrome (CTS) being one of the most reported work-related musculoskeletal disorders (WMSDs) among office workers. The optimal range of keyboard angles for sitting and standing positions based on wrist posture, forearm muscle activities and user preference as well as the keyboard location in relation to user position were analysed. 30 volunteers with an above 40 words per minute typing speed participated in this study. Result show that, although user prefer to use positive keyboard angle, the negatively tilted keyboard is more ergonomically friendly at both sitting and standing workstations, reducing muscle activity and awkward wrist posture while maintaining performance. The findings indicate that negative sloped keyboard might have the possibility to reduce the risk of developing CTS in office workers. Practitioner summary: This study determines the range of optimal slope of keyboard angle in a sit and stand workstation. Our results indicate a trend in the negative slope keyboard as an ergonomically friendly option for the intervention to Carpal tunnel pressure.

Impact of task variation and microbreaks on muscle fatigue at seated and standing postures.

Prolonged and sustained work posture among computer users is one of the main factors that contributes to musculoskeletal discomfort. Rest-break interventions such as task variation and microbreaks may help prevent muscle fatigue and work-related musculoskeletal disorder. We aimed to investigate the effects of task variation and microbreaks at seated and standing workstations on forearm muscle activity, namely extensor digitorum communis, extensor carpi ulnaris, flexor digitorum superficialis, and flexor carpi ulnaris; mouse operation force (vertical compression force); mouse operation parameters; and perceived body discomfort during mouse operation. Twelve healthy right-handed young adults were recruited (male: n = 7, 21.6±1.4 years; female: n = 5, 21.4±1.7 years). Participants performed three blocks of computer tasks (computer mouse operation and typing) in both seated and standing postures with each block lasting for 30 min. Surface electromyography (EMG) of the forearm muscles and operation force were monitored during computer mouse operation. Body discomfort rating was recorded at the end of each block. With simulated task variation and microbreaks, work posture and work time showed no significant difference with EMG amplitude and mouse operation force. Task variation and microbreaks could be of benefit to computer users by reducing muscle fatigue during long hours of computer work at both seated and standing workstations.

Forearm Muscle Activity and Handgrip Profile Between Physically Active and Sedentary Older Women

Background: The aging process promotes changes in skeletal muscles that influence muscle strength and fatigue, however physical exercise has a direct effect on the aging process. Objective: The aim of this study was investigated the strength and strength fatigability and the muscle activities of the forearm of elderly women’s handgrip according physical level. Method: The patterns of rapid muscle strength, maximum strength and fatigability of 32 elderly women during handgrip task were evaluated in this cross-sectional study. Both hands were evaluated and the muscle activity of the carpal flexors and extensors was monitored with an electromyography simultaneously. Volunteers were grouped into two groups based on their level of physical activity: physically active and sedentary. Results were compared by group and time factors. Results: Rapid muscle strength and maximum isometric strength were substantially higher in the physically active. Loss of strength was verified in physically active while the fatigue by muscle activity was identified in sedentary group. Discussion: The volunteers' physical activity pattern was able to change their muscle contraction strategies. The active group mainly practices strength training or combined training that includes strength training with at least 3 sessions per week. This volume was also enough to promote gains in the strength and rapid strength. Conclusion: The physical activity profile of the elderly women in this study have influenced strength, fatigability, and muscle activity during handgrip strength.

Forearm muscles activity of harp players

The practice of a musical instrument requires fine dexterity, repetitive, fast, and precise movements, as well as important efforts to set the instrument into vibration, while adopting postures often unnatural for the human body. As a result, musicians are often subject to pain and musculoskeletal disorders. In the case of plucked string instruments and especially the concert harp, the plucking force is directly related to the strings’ tension. Consequently, the choice of the strings has to be made based on both, the musician feel while playing, and the musculoskeletal consequences. This paper investigates how the string properties and the playing dynamics affect the finger and wrist muscle activity during harp playing. This study first emphasized the noteworthy recruitment of the flexor and extensor muscles (42% and 29% of MVC, respectively). Findings outlined further that the fingering choice, the adopted playing dynamics and the string’s material govern the muscular activity level and the playing control. Such results are a first step to better understand how the harp ergonomics may affect the player’s integrity and help them decide the most suitable stringing for their practice.

Utilization of deep learning to classify resistance training exercises by the fabricated resistive stretch sensor

In this study, the authors proposed a method to fabricate a resistive stretch textile sensor from polyester spandex (PET/SP) fabric and commercial single-walled carbon nanotube (SWCNT). In addition, we designed and trained a one-dimension convolutional neural network to classify four resistance workouts, which employed data acquired from the proposed sensor as the input. To figure out the most appropriate PET/SP sample for the deep learning application, we investigated morphologies and characterization of three samples in distinct conditions of the coating process. Data acquired from the proposed sensor illustrated the significant difference between activated and non-activated muscle groups in each specific exercise. With the PET/SP sample which met the requirements of the application, after 100 epochs, the deep learning model achieved 97.2% training accuracy and 90% test accuracy. This study demonstrates that the SWCNT-coated PET/SP stretch textile sensor can be utilized effectively to track the activity of forearm muscles during resistance training. Other than that, the proposed 1D-CNN, with the advantage of training time and computational cost, is able to classify time series data with high performance and thus can be applied widely in various deep learning applications, especially in the healthcare and sports industries.

The effect of local vibration applied to the forearm extensor muscles on hand function and muscle activation in stroke patients: a randomized controlled study.

This study aims to reveal the effect of low-frequency local vibration applied to the forearm extensor muscles on stroke patients' muscle activation and hand functions. Twenty-four stroke patients were randomized to the vibration group (n = 12) or control group (n = 12). The vibration was applied at a 30Hz frequency to the forearm extensor muscles with a local vibration device three days a week after the routine, conventional physical therapy sessions for four weeks. Six vibration sets were applied, including one vibration for one minute and a rest for 2min. Routine, traditional physical therapy was used for the control group in 60-min sessions for 4weeks. Patients were assessed for muscle activation with surface electromyography (MVC) and The Wolf Motor Function Test (WMFT), Functional Independent Test (FIM) was applied to all patients before and after treatment. As a result of our study, MVC measurement, WMFT and FIM scores of the vibration group showed more improvement than the control group. Measurement results of vibration group; While MVC measurement increased from 10.21 to 13.79, WMFT-Functional Ability score increased from 42 to 50, WMFT-Performance Time duration increased from 68.78 to 61.83, and FIM score increased from 74.5 to 83. and the measurement results of the control group; MVC measurement increased from 12.28 to 12.22, WMFT-Functional Ability score increased from 48.5 to 51, WMFT-Performance Time duration increased from 70.39 to 70.61, and FIM score increased from 72.5 to 80.5. It was concluded that low-frequency local vibration applied to the forearm extensor muscles improve forearm extensor muscle activation and hand motor function. NCT04562220.