Multimodal sensorimotor assessment of hand and forearm asymmetries: a reliability and correlational study.

Background: Effective rehabilitation of upper limb musculoskeletal disorders requires multimodal assessment to guide clinicians' decision-making. Furthermore, a comprehensive assessment must include reliable tests. Nevertheless, the interrelationship among various upper limb tests remains unclear. This study aimed to evaluate the reliability of easily applicable upper extremity assessments, including absolute values and asymmetries of muscle mechanical properties, pressure pain threshold, active range of motion, maximal isometric strength, and manual dexterity. A secondary aim was to explore correlations between different assessment procedures to determine their interrelationship. Methods: Thirty healthy subjects participated in two experimental sessions with 1 week between sessions. Measurements involved using a digital myotonometer, algometer, inclinometer, dynamometer, and the Nine-Hole Peg test. Intraclass correlation coefficients, standard error of the mean, and minimum detectable change were calculated as reliability indicators. Pearson's correlation was used to assess the interrelationship between tests. Results: For the absolute values of the dominant and nondominant sides, reliability was 'good' to 'excellent' for muscle mechanical properties, pressure pain thresholds, active range of motion, maximal isometric strength, and manual dexterity. Similarly, the reliability for asymmetries ranged from 'moderate' to 'excellent' across the same parameters. Faster performance in the second session was consistently found for the Nine-Hole Peg test. No systematic inter-session errors were identified for the values of the asymmetries. No significant correlations were found between tests, indicating test independence. Conclusion: These findings indicate that the sensorimotor battery of tests is reliable, while monitoring asymmetry changes may offer a more conservative approach to effectively tracking recovery of upper extremity injuries.

Linking the preference in a bilateral asymmetric task with handedness, footedness, and eyedness: The case of ice-hockey.

Most people know whether they are left-handed or right-handed, and usually base this assessment on preferences during one-handed tasks. There are several manual tasks that require the contribution of both hands, in which, in most cases, each hand plays a different role. In this specific case, holding an ice-hockey stick is particularly interesting because the hand placement may have an incidence on the playing style. In this study (n = 854), the main objective was to determine to what extent the way of holding an ice-hockey stick is associated with other lateralized preferences. Amongst the 131 participants reporting a preference for the left hand in unilateral tasks, 70.2% reported a preference for shooting right (placing the right hand in the middle of the stick); and amongst the 583 participants reporting a preference for writing with the right hand, 66.2% reported a preference for shooting left. 140 (16.4%) participants were classified as ambidextrous and 61.4% of them reported a preference for shooting right. This preference on the ice-hockey stick is closely correlated (uncrossed preference) to the way one holds a rake, shovel, or broom, or a golf club, but inversely related to the way one holds an ax and a baseball bat. The link between the way of holding the ice-hockey stick and eyedness or footedness is weak. These results are contrasted with the results reported by Loffing et al. (2014) and reveal the need to clarify the exact nature and requirements of the targeted tasks when studying bilateral asymmetric preferences.

Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test.

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals.

Currently, surface EMG signals have a wide range of applications in human-computer interaction systems. However, selecting features for gesture recognition models based on traditional machine learning can be challenging and may not yield satisfactory results. Considering the strong nonlinear generalization ability of neural networks, this paper proposes a two-stream residual network model with an attention mechanism for gesture recognition. One branch processes surface EMG signals, while the other processes hand acceleration signals. Segmented networks are utilized to fully extract the physiological and kinematic features of the hand. To enhance the model's capacity to learn crucial information, we introduce an attention mechanism after global average pooling. This mechanism strengthens relevant features and weakens irrelevant ones. Finally, the deep features obtained from the two branches of learning are fused to further improve the accuracy of multi-gesture recognition. The experiments conducted on the NinaPro DB2 public dataset resulted in a recognition accuracy of 88.25% for 49 gestures. This demonstrates that our network model can effectively capture gesture features, enhancing accuracy and robustness across various gestures. This approach to multi-source information fusion is expected to provide more accurate and real-time commands for exoskeleton robots and myoelectric prosthetic control systems, thereby enhancing the user experience and the naturalness of robot operation.

End-to-End Ultrasonic Hand Gesture Recognition.

As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand gesture recognition (HGR) systems in terms of robustness and accuracy. This research presents a novel machine learning (ML)-based end-to-end solution for hand gesture recognition with low-cost micro-electromechanical (MEMS) system ultrasonic transducers. In contrast to prior methods, our ML model processes the raw echo samples directly instead of using pre-processed data. Consequently, the processing flow presented in this work leaves it to the ML model to extract the important information from the echo data. The success of this approach is demonstrated as follows. Four MEMS ultrasonic transducers are placed in three different geometrical arrangements. For each arrangement, different types of ML models are optimized and benchmarked on datasets acquired with the presented custom hardware (HW): convolutional neural networks (CNNs), gated recurrent units (GRUs), long short-term memory (LSTM), vision transformer (ViT), and cross-attention multi-scale vision transformer (CrossViT). The three last-mentioned ML models reached more than 88% accuracy. The most important innovation described in this research paper is that we were able to demonstrate that little pre-processing is necessary to obtain high accuracy in ultrasonic HGR for several arrangements of cost-effective and low-power MEMS ultrasonic transducer arrays. Even the computationally intensive Fourier transform can be omitted. The presented approach is further compared to HGR systems using other sensor types such as vision, WiFi, radar, and state-of-the-art ultrasound-based HGR systems. Direct processing of the sensor signals by a compact model makes ultrasonic hand gesture recognition a true low-cost and power-efficient input method.

Developing a Novel Prosthetic Hand with Wireless Wearable Sensor Technology Based on User Perspectives: A Pilot Study.

Myoelectric hands are beneficial tools in the daily activities of people with upper-limb deficiencies. Because traditional myoelectric hands rely on detecting muscle activity in residual limbs, they are not suitable for individuals with short stumps or paralyzed limbs. Therefore, we developed a novel electric prosthetic hand that functions without myoelectricity, utilizing wearable wireless sensor technology for control. As a preliminary evaluation, our prototype hand with wireless button sensors was compared with a conventional myoelectric hand (Ottobock). Ten healthy therapists were enrolled in this study. The hands were fixed to their forearms, myoelectric hand muscle activity sensors were attached to the wrist extensor and flexor muscles, and wireless button sensors for the prostheses were attached to each user's trunk. Clinical evaluations were performed using the Simple Test for Evaluating Hand Function and the Action Research Arm Test. The fatigue degree was evaluated using the modified Borg scale before and after the tests. While no statistically significant differences were observed between the two hands across the tests, the change in the Borg scale was notably smaller for our prosthetic hand (p = 0.045). Compared with the Ottobock hand, the proposed hand prosthesis has potential for widespread applications in people with upper-limb deficiencies.

Anthropomorphic Tendon-Based Hands Controlled by Agonist-Antagonist Corticospinal Neural Network.

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist-antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems.

Channel Selection for Gesture Recognition Using Force Myography: A Universal Model for Gesture Measurement Points.

Gesture recognition has emerged as a significant research domain in computer vision and human-computer interaction. One of the key challenges in gesture recognition is how to select the most useful channels that can effectively represent gesture movements. In this study, we have developed a channel selection algorithm that determines the number and placement of sensors that are critical to gesture classification. To validate this algorithm, we constructed a Force Myography (FMG)-based signal acquisition system. The algorithm considers each sensor as a distinct channel, with the most effective channel combinations and recognition accuracy determined through assessing the correlation between each channel and the target gesture, as well as the redundant correlation between different channels. The database was created by collecting experimental data from 10 healthy individuals who wore 16 sensors to perform 13 unique hand gestures. The results indicate that the average number of channels across the 10 participants was 3, corresponding to an 75% decrease in the initial channel count, with an average recognition accuracy of 94.46%. This outperforms four widely adopted feature selection algorithms, including Relief-F, mRMR, CFS, and ILFS. Moreover, we have established a universal model for the position of gesture measurement points and verified it with an additional five participants, resulting in an average recognition accuracy of 96.3%. This study provides a sound basis for identifying the optimal and minimum number and location of channels on the forearm and designing specialized arm rings with unique shapes.

Neural mechanisms of costly helping in the general population and mirror-pain synesthetes.

It has been argued that experiencing the pain of others motivates helping. Here, we investigate the contribution of somatic feelings while witnessing the pain of others onto costly helping decisions, by contrasting the choices and brain activity of participants that report feeling somatic feelings (self-reported mirror-pain synesthetes) against those that do not. Participants in fMRI witnessed a confederate receiving pain stimulations whose intensity they could reduce by donating money. The pain intensity could be inferred either from the facial expressions of the confederate in pain (Face condition) or from the kinematics of the pain-receiving hand (Hand condition). Our results show that self-reported mirror-pain synesthetes increase their donation more steeply, as the intensity of the observed pain increases, and their somatosensory brain activity (SII and the adjacent IPL) was more tightly associated with donation in the Hand condition. For all participants, activation in insula, SII, TPJ, pSTS, amygdala and MCC correlated with the trial by trial donation made in the Face condition, while SI and MTG activation was correlated with the donation in the Hand condition. These results further inform us about the role of somatic feelings while witnessing the pain of others in situations of costly helping.

Cross-Subject Lifelong Learning for Continuous Estimation From Surface Electromyographic Signal.

The employment of surface electromyographic (sEMG) signals in the estimation of hand kinematics represents a promising non-invasive methodology for the advancement of human-machine interfaces. However, the limitations of existing subject-specific methods are obvious as they confine the application to individual models that are custom-tailored for specific subjects, thereby reducing the potential for broader applicability. In addition, current cross-subject methods are challenged in their ability to simultaneously cater to the needs of both new and existing users effectively. To overcome these challenges, we propose the Cross-Subject Lifelong Network (CSLN). CSLN incorporates a novel lifelong learning approach, maintaining the patterns of sEMG signals across a varied user population and across different temporal scales. Our method enhances the generalization of acquired patterns, making it applicable to various individuals and temporal contexts. Our experimental investigations, encompassing both joint and sequential training approaches, demonstrate that the CSLN model not only attains enhanced performance in cross-subject scenarios but also effectively addresses the issue of catastrophic forgetting, thereby augmenting training efficacy.

Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms.

Intuitive regression control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time regression performance, but accurately labeling intended hand kinematics after hand amputation is challenging. In this study, we quantified the accuracy and precision of labeling hand kinematics using two common training paradigms: 1) mimic training, where participants mimic predetermined motions of a prosthesis, and 2) mirror training, where participants mirror their contralateral intact hand during synchronized bilateral movements. We first explored this question in healthy non-amputee individuals where the ground-truth kinematics could be readily determined using motion capture. Kinematic data showed that mimic training fails to account for biomechanical coupling and temporal changes in hand posture. Additionally, mirror training exhibited significantly higher accuracy and precision in labeling hand kinematics. These findings suggest that the mirror training approach generates a more faithful, albeit more complex, dataset. Accordingly, mirror training resulted in significantly better offline regression performance when using a large amount of training data and a non-linear neural network. Next, we explored these different training paradigms online, with a cohort of unilateral transradial amputees actively controlling a prosthesis in real-time to complete a functional task. Overall, we found that mirror training resulted in significantly faster task completion speeds and similar subjective workload. These results demonstrate that mirror training can potentially provide more dexterous control through the utilization of task-specific, user-selected training data. Consequently, these findings serve as a valuable guide for the next generation of myoelectric and neuroprostheses leveraging machine learning to provide more dexterous and intuitive control.

Gray matter volume of functionally relevant primary motor cortex is causally related to learning a hand motor task.

Variability in brain structure is associated with the capacity for behavioral change. However, a causal link between specific brain areas and behavioral change (such as motor learning) has not been demonstrated. We hypothesized that greater gray matter volume of a primary motor cortex (M1) area active during a hand motor learning task is positively correlated with subsequent learning of the task, and that the disruption of this area blocks learning of the task. Healthy participants underwent structural MRI before learning a skilled hand motor task. Next, participants performed this learning task during fMRI to determine M1 areas functionally active during this task. This functional ROI was anatomically constrained with M1 boundaries to create a group-level "Active-M1" ROI used to measure gray matter volume in each participant. Greater gray matter volume in the left hemisphere Active-M1 ROI was related to greater motor learning in the corresponding right hand. When M1 hand area was disrupted with repetitive transcranial stimulation (rTMS), learning of the motor task was blocked, confirming its causal link to motor learning. Our combined imaging and rTMS approach revealed greater cortical volume in a task-relevant M1 area is causally related to learning of a hand motor task in healthy humans.

Biases in hand perception are driven by somatosensory computations, not a distorted hand model.

To sense and interact with objects in the environment, we effortlessly configure our fingertips at desired locations. It is therefore reasonable to assume that the underlying control mechanisms rely on accurate knowledge about the structure and spatial dimensions of our hand and fingers. This intuition, however, is challenged by years of research showing drastic biases in the perception of finger geometry.1,2,3,4,5 This perceptual bias has been taken as evidence that the brain's internal representation of the body's geometry is distorted,6 leading to an apparent paradox regarding the skillfulness of our actions.7 Here, we propose an alternative explanation of the biases in hand perception-they are the result of the Bayesian integration of noisy, but unbiased, somatosensory signals about finger geometry and posture. To address this hypothesis, we combined Bayesian reverse engineering with behavioral experimentation on joint and fingertip localization of the index finger. We modeled the Bayesian integration either in sensory or in space-based coordinates, showing that the latter model variant led to biases in finger perception despite accurate representation of finger length. Behavioral measures of joint and fingertip localization responses showed similar biases, which were well fitted by the space-based, but not the sensory-based, model variant. The space-based model variant also outperformed a distorted hand model with built-in geometric biases. In total, our results suggest that perceptual distortions of finger geometry do not reflect a distorted hand model but originate from near-optimal Bayesian inference on somatosensory signals.

Spatio-temporal modulation of cortical activity during motor deadaptation depends on the feedback of task-related error.

Motor adaptations are responsible for recalibrating actions and facilitating the achievement of goals in a constantly changing environment. Once consolidated, the decay of motor adaptation is a process affected by available sensory information during deadaptation. However, the cortical response to task error feedback during the deadaptation phase has received little attention. Here, we explored changes in brain cortical responses due to feedback of task-related error during deadaptation. Twelve healthy volunteers were recruited for the study. Right hand movement and EEG were recorded during repetitive trials of a hand reaching movement. A visuomotor rotation of 30° was introduced to induce motor adaptation. Volunteers participated in two experimental sessions organized in baseline, adaptation, and deadaptation blocks. In the deadaptation block, the visuomotor rotation was removed, and visual feedback was only provided in one session. Performance was quantified using angle end-point error, averaged speed, and movement onset time. A non-parametric spatiotemporal cluster-level permutation test was used to analyze the EEG recordings. During deadaptation, participants experienced a greater error reduction when feedback of the cursor was provided. The EEG responses showed larger activity in the left centro-frontal parietal areas during the deadaptation block when participants received feedback, as opposed to when they did not receive feedback. Centrally distributed clusters were found for the adaptation and deadaptation blocks in the absence of visual feedback. The results suggest that visual feedback of the task-related error activates cortical areas related to performance monitoring, depending on the accessible sensory information.

Accuracy of Video-Based Hand Tracking for People With Upper-Body Disabilities.

Utilization of hand-tracking cameras, such as Leap, for hand rehabilitation and functional assessments is an innovative approach to providing affordable alternatives for people with disabilities. However, prior to deploying these commercially-available tools, a thorough evaluation of their performance for disabled populations is necessary. In this study, we provide an in-depth analysis of the accuracy of Leap's hand-tracking feature for both individuals with and without upper-body disabilities for common dynamic tasks used in rehabilitation. Leap is compared against motion capture with conventional techniques such as signal correlations, mean absolute errors, and digit segment length estimation. We also propose the use of dimensionality reduction techniques, such as Principal Component Analysis (PCA), to capture the complex, high-dimensional signal spaces of the hand. We found that Leap's hand-tracking performance did not differ between individuals with and without disabilities, yielding average signal correlations between 0.7-0.9. Both low and high mean absolute errors (between 10-80mm) were observed across participants. Overall, Leap did well with general hand posture tracking, with the largest errors associated with the tracking of the index finger. Leap's hand model was found to be most inaccurate in the proximal digit segment, underestimating digit lengths with errors as high as 18mm. Using PCA to quantify differences between the high-dimensional spaces of Leap and motion capture showed that high correlations between latent space projections were associated with high accuracy in the original signal space. These results point to the potential of low-dimensional representations of complex hand movements to support hand rehabilitation and assessment.

Implicit visuospatial sequence representations are accessible in both the practice and the transfer hand.

A serial reaction time task was used to test whether the representations of a probabilistic second-order sequence structure are (i) stored in an effector-dependent, effector-independent intrinsic or effector-independent visuospatial code and (ii) are inter-manually accessible. Participants were trained either with the dominant or non-dominant hand. Tests were performed with both hands in the practice sequence, a random sequence, and a mirror sequence. Learning did not differ significantly between left and right-hand practice, suggesting symmetric intermanual transfer from the dominant to the non-dominant hand and vice versa. In the posttest, RTs were shorter for the practice sequence than for the random sequence, and longest for the mirror sequence. Participants were unable to freely generate or recognize the practice sequence, indicating implicit knowledge of the probabilistic sequence structure. Because sequence-specific learning did not differ significantly between hands, we conclude that representations of the probabilistic sequence structure are stored in an effector-independent visuospatial code.

Excitation of natural spinal reflex loops in the sensory-motor control of hand prostheses.

Sensory feedback for prosthesis control is typically based on encoding sensory information in specific types of sensory stimuli that the users interpret to adjust the control of the prosthesis. However, in physiological conditions, the afferent feedback received from peripheral nerves is not only processed consciously but also modulates spinal reflex loops that contribute to the neural information driving muscles. Spinal pathways are relevant for sensory-motor integration, but they are commonly not leveraged for prosthesis control. We propose an approach to improve sensory-motor integration for prosthesis control based on modulating the excitability of spinal circuits through the vibration of tendons in a closed loop with muscle activity. We measured muscle signals in healthy participants and amputees during different motor tasks, and we closed the loop by applying vibration on tendons connected to the muscles, which modulated the excitability of motor neurons. The control signals to the prosthesis were thus the combination of voluntary control and additional spinal reflex inputs induced by tendon vibration. Results showed that closed-loop tendon vibration was able to modulate the neural drive to the muscles. When closed-loop tendon vibration was used, participants could achieve similar or better control performance in interfaces using muscle activation than without stimulation. Stimulation could even improve prosthetic grasping in amputees. Overall, our results indicate that closed-loop tendon vibration can integrate spinal reflex pathways in the myocontrol system and open the possibility of incorporating natural feedback loops in prosthesis control.

Evaluating initial usability of a hand augmentation device across a large and diverse sample.

The advancement of motor augmentation and the broader domain of human-machine interaction rely on a seamless integration with users' physical and cognitive capabilities. These considerations may markedly fluctuate among individuals on the basis of their age, form, and abilities. There is a need to develop a standard for considering these diversity needs and preferences to guide technological development, and large-scale testing can provide us with evidence for such considerations. Public engagement events provide an important opportunity to build a bidirectional discourse with potential users for the codevelopment of inclusive and accessible technologies. We exhibited the Third Thumb, a hand augmentation device, at a public engagement event and tested participants from the general public, who are often not involved in such early technological development of wearable robotic technology. We focused on wearability (fit and control), ability to successfully operate the device, and ability levels across diversity factors relevant for physical technologies (gender, handedness, and age). Our inclusive design was successful in 99.3% of our diverse sample of 596 individuals tested (age range from 3 to 96 years). Ninety-eight percent of participants were further able to successfully manipulate objects using the extra thumb during the first minute of use, with no significant influences of gender, handedness, or affinity for hobbies involving the hands. Performance was generally poorer among younger children (aged ≤11 years). Although older and younger adults performed the task comparably, we identified age costs with the older adults. Our findings offer tangible demonstration of the initial usability of the Third Thumb for a broad demographic.

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals.

Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.

Integration of music-based game approaches with wearable devices for hand neurorehabilitation: a narrative review.

BACKGROUND: Restoring hand functionality is critical for fostering independence in individuals with neurological disorders. Various therapeutic approaches have emerged to address motor function restoration, with music-based therapies demonstrating notable advantages in enhancing neuroplasticity, an integral component of neurorehabilitation. Despite the positive effects observed, there remains a gap in the literature regarding implementing music treatments in neurorehabilitation, such as Neurologic Music Therapy (NMT), especially in conjunction with emerging fields like wearable devices and game-based therapies. METHODS: A literature search was conducted in various databases, including PubMed, Scopus, IEEE Xplore, and ACM Digital Library. The search was performed using a literature search methodology based on keywords. Information collected from the studies pertained to the approach used in music therapy, the design of the video games, and the types of wearable devices utilized. RESULTS: A total of 158 articles were found, including 39 from PubMed, 34 from IEEE Xplore, 48 from Scopus, 37 from ACM Digital Library, and 35 from other sources. Duplicate entries, of which there were 41, were eliminated. In the first screening phase, 152 papers were screened for title and abstract. Subsequently, 89 articles were removed if they contained at least one exclusion criterion. Sixteen studies were considered after 63 papers had their full texts verified. CONCLUSIONS: The convergence of NMT with emerging fields, such as gamification and wearable devices designed for hand functionality, not only expands therapeutic horizons but also lays the groundwork for innovative, personalized approaches to neurorehabilitation. However, challenges persist in effectively incorporating NMT into rehabilitation programs, potentially hindering its effectiveness.