Pattern Matching for Real-Time Extraction of Fast and Slow Spectral Components From sEMG Signals.

Previous studies have demonstrated the potential of surface electromyography (sEMG) spectral decomposition in evaluating muscle performance, motor learning, and early diagnosis of muscle conditions. However, decomposition techniques require large data sets and are computationally demanding, making their implementation in real-life scenarios challenging. Based on the hypothesis that spectral components will present low inter-subject variability, the present paper proposes the foundational principles for developing a real-time system for their extraction by utilizing a pre-defined library of components derived from an extensive data set to match new measurements. The model library was tailored to fulfill specific requirements for real-time system application and the challenges encountered during implementation are discussed in the paper. For system validation, four distinct data sets comprising isotonic and isometric muscle activations were utilized. The extracted during validation showed low inter-subject variability, suggesting that a wide range of physiological variations can be described with them. The adoption of the proposed system for muscle analysis could provide a deeper understanding of the underlying mechanisms governing different motor conditions and neuromuscular disorders, as it allows for the measurement of these components in various daily-life scenarios.

Enriched sensory feedback delivered during a voluntary action boosts subjective time compression.

Introduction: The subjective experience of time can be influenced by various factors including voluntary actions. In our previous study, we found that the subjective time experience of an action outcome can be compressed when an individual performs a continuous action compared to a single action, suggesting that the sense of agency (SoA), the feeling of control over one's own action outcomes, contributes to the subjective time compression. We hypothesized that enhancing SoA by providing sensory feedback to participants would further compress the subjective time experience. Methods: To test the hypothesis, we used a temporal reproduction task where participants reproduced the duration of a previously exposed auditory stimulus by performing different voluntary actions: a combination of single actions with single auditory feedback, continuous action with single auditory feedback, or continuous action with multiple auditory feedback. Results: The results showed that the continuous action conditions, regardless of the type of auditory feedback, led to a compression of the subjective time experience of the reproduced tone, whereas the single action condition did not. Furthermore, a greater degree of subjective time compression during continuous action and a stronger SoA were revealed when enriched with multiple auditory feedback. Discussion: These results indicate that enriching auditory feedback can increase subjective time compression during voluntary action, which in turn enhances SoA over action outcomes. This suggests the potential for developing new techniques to artificially compress the subjective time experience of daily events.

Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat.

In this paper, we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning. Specifically, we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions. The novelty of our method lies in using the temporal difference (TD) algorithm to optimize the state decision process, which enables the robots to make informed decisions about their behavior choices. To assess the similarity between robot and rat behavior, we use Pearson correlation. We then use TD-λ to update the state value function and make state decisions based on probability. The robots execute these decisions using our dynamics-based controller. Our results demonstrate that our method can generate rat-like behaviors on both short- and long-term timescales, with interaction information entropy comparable to that between real rats. Overall, our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion.

Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder-elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion-extension and pronation-supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson's correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.

Artifact removal from sEMG signals recorded during fully unsupervised daily activities.

Objective: In this study, we propose a method for removing artifacts from superficial electromyography (sEMG) data, which have been widely proposed for health monitoring because they encompass the basic neuromuscular processes underlying human motion. Methods: Our method is based on a spectral source decomposition from single-channel data using a non-negative matrix factorization. The algorithm is validated with two data sets: the first contained muscle activity coupled to artificially generated noises and the second comprised signals recorded under fully unsupervised conditions. Algorithm performance was further assessed by comparison with other state-of-the-art approaches for noise removal using a single channel. Results: The comparison of methods shows that the proposed algorithm achieves the highest performance on the noise-removal process in terms of signal-to-noise ratio reconstruction, root means square error, and correlation coefficient with the original muscle activity. Moreover, the spectral distribution of the extracted sources shows high correlation with the noise sources traditionally associated to sEMG recordings. Conclusion: This research shows the ability of spectral source separation to detect and remove noise sources coupled to sEMG signals recorded during unsupervised daily activities which opens the door to the implementation of sEMG recording during daily activities for motor and health monitoring.

Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review.

PURPOSE: Stroke, spinal cord injury and other neuromuscular disorders lead to impairments in the human body. Upper limb impairments, especially hand impairments affect activities of daily living (ADL) and reduce the quality of life. The purpose of this review is to compare and evaluate the available robotic rehabilitation and assistive devices that can lead to motor recovery or maintain the current motor functional level. METHODS: A systematic review was conducted of the literature published in the years from 2016-2021, to focus on the most recent rehabilitation and assistive devices available in the market or research environments. RESULTS: A total of 230 studies published between 2016 and 2021 were identified from various databases. 107 were excluded with various reasons. Twenty-eight studies were taken into detailed review, to determine the efficacy of robotic devices in improving upper limb impairments or maintaining the current level from getting worse. CONCLUSION: It was concluded that with a good strategy and treatment plan; appropriate and regular use of these robotic rehabilitation and assistive devices do lead to improvements in current conditions of most of the subjects and prolonged use may lead to motor recovery.Implications for RehabilitationStroke, accidents, spinal cord injuries and other neuromuscular disorders lead to impairments. Upper limb impairments have a tremendous adverse affect on ADL and reduces quality of life drastically.Advancement in technology has led to the designing of many robotic assistive and rehabilitation devices to assist in motor recovery or aid in ADL.This review analyses different available devices for rehabilitation and assistance and points out that use of these devices in time does help in motor recovery. Most of the studies reviewed showed improvements for the user.Future devices should be more portable and easier to use from home.

Forearm muscle activity estimation based on anatomical structure of muscles.

Estimation of muscle activity using surface electromyography (sEMG) is an important non-invasive method that can lead to a deeper understanding of motor-control strategies in humans. Measurement using multiple active electrodes is necessary to estimate not only surface muscle activity but also deep muscle activity in dynamic motion. In this paper, we propose a method for estimating muscle activity of dynamic motions based on anatomical knowledge of muscle structures. To estimate muscle activity, a large number of signal sources are set in the muscle model, and connections between the signal sources are defined a priori based on the anatomical structure of the muscles. The signal source activities are first estimated by minimizing the Kullback-Leibler divergence with a continuity cost. Then, the muscle activity is computed from the signal source activity. In the experiments, five healthy participants performed five types of motion and the forearm sEMG was measured with 20-channel active electrodes. The estimation results for these motions were visualized in four dimensions as the three-dimensional position of the muscle over time. The results showed that the estimation was accurate, with a reproduction rate of 95% for the measured sEMG and continuity of the muscle activity. In addition, the results suggest the advantage of the proposed method over the conventional approaches in terms of estimating the muscle activity for both dynamic and abnormal motions.

Quantification of the Swallowing Mechanism Through Muscle Synergy Analysis.

Decreased swallowing function increases the risk of choking and aspiration pneumonia. Videofluoroscopy and computed tomography allow for detailed observation of the swallowing movements but have radiation risks. Therefore, we developed a method using surface electromyography (sEMG) to noninvasively assess swallowing function without radiation exposure. A 44-channel flexible sEMG sensor was used to measure the sEMG signals of the hyoid muscles during swallowing in 14 healthy young adult and 14 elderly subjects. Muscle synergy analysis was performed to extract the muscle synergies from the sEMG signals, and the three synergies were extracted from the hyoid muscle activities during the swallowing experiments. The experimental results showed that the three synergies represent the oral, early pharyngeal, and late pharyngeal swallowing phases and that swallowing strength is tuned by the strength of the muscle activities, whereas swallowing volume is controlled by adjusting muscle activation timing. In addition, the timing of the swallowing reflex is slower in elderly individuals. The results confirm that the proposed approach successfully quantifies swallowing function from sEMG signals, mapping the signals to the swallowing phases.

Dynamic causal model application on hierarchical human motor control estimation in visuomotor tasks.

Introduction: In brain function research, each brain region has been investigated independently, and how different parts of the brain work together has been examined using the correlations among them. However, the dynamics of how different brain regions interact with each other during time-varying tasks, such as voluntary motion tasks, are still not well-understood. Methods: To address this knowledge gap, we conducted functional magnetic resonance imaging (fMRI) using target tracking tasks with and without feedback. We identified the motor cortex, cerebellum, and visual cortex by using a general linear model during the tracking tasks. We then employed a dynamic causal model (DCM) and parametric empirical Bayes to quantitatively elucidate the interactions among the left motor cortex (ML), right cerebellum (CBR) and left visual cortex (VL), and their roles as higher and lower controllers in the hierarchical model. Results: We found that the tracking task with visual feedback strongly affected the modulation of connection strength in ML → CBR and ML↔VL. Moreover, we found that the modulation of VL → ML, ML → ML, and ML → CBR by the tracking task with visual feedback could explain individual differences in tracking performance and muscle activity, and we validated these findings by leave-one-out cross-validation. Discussion: We demonstrated the effectiveness of our approach for understanding the mechanisms underlying human motor control. Our proposed method may have important implications for the development of new technologies in personalized interventions and technologies, as it sheds light on how different brain regions interact and work together during a motor task.

Synergetic synchronized oscillation by distributed neural integrators to induce dynamic equilibrium in energy dissipation systems.

The synchronization phenomenon is common to many natural mechanical systems. Joint friction and damping in humans and animals are associated with energy dissipation. A coupled oscillator model is conventionally used to manage multiple joint torque generations to form a limit cycle in an energy dissipation system. The coupling term design and the frequency and phase settings become issues when selecting the oscillator model. The relative coupling relationship between oscillators needs to be predefined for unknown dynamics systems, which is quite challenging problem. We present a simple distributed neural integrators method to induce the limit cycle in unknown energy dissipation systems without using a coupled oscillator. The results demonstrate that synergetic synchronized oscillation could be produced that adapts to different physical environments. Finding the balanced energy injection by neural inputs to form dynamic equilibrium is not a trivial problem, when the dynamics information is not priorly known. The proposed method realized self-organized pattern generation to induce the dynamic equilibrium for different mechanical systems. The oscillation was managed without using the explicit phase or frequency knowledge. However, phase, frequency, and amplitude modulation emerged to form an efficient synchronized limit cycle. This type of distributed neural integrator can be used as a source for regulating multi-joint coordination to induce synergetic oscillations in natural mechanical systems.

Demonstration experiment of telemedicine using ultrasonography and telerehabilitation with 5G communication system in aging and depopulated mountainous area.

Objective: The challenges of an aging population worldwide are the increased number of people needing medical and nursing care and inadequate medical resources. Information and communication technologies have progressed remarkably, leading to innovations in various areas. 5G communication systems are capable of high-capacity, high-speed communication with low latency and are expected to transform medicine. We aimed to report a demonstration experiment of telerehabilitation and telemedicine using a mobile ultrasound system in a depopulated area in a mountainous terrain, where 32% of the population are 65 years or older. Methods: At the core hospital, a physician or physical therapist remotely performed ultrasonography or rehabilitation on a subject in a clinic. Five general residents participated in the telerehabilitation as subjects. The delay time and video quality transmitted with 5G and long-term evolution (LTE) communication systems were compared. The physician or physical therapist subjectively evaluated the quality and delay of the transmitted images and subject acceptability. Results: Of seven physical therapists, six and three responded that the video quality was "good" for telerehabilitation with 5G/4K resolution and LTE, respectively. Five physical therapists and one physical therapist reported that the delay time was "acceptable" with 5G/4K resolution and LTE, respectively. For telemedicine using a mobile ultrasound system, the responses for 5G were "the delay was acceptable" and "rather acceptable." In contrast, both respondents' responses for LTE were "not acceptable." Conclusions: Multiple high-definition images can be transmitted with lower latency in telerehabilitation and telemedicine using mobile ultrasound imaging systems with a 5G communication system. These differences affected the subjective evaluation of the doctors and physical therapists.

Telerehabilitation Based on Markerless Motion Capture and IMT-2020 (5G) Networks.

Eldercare programs such as health consultations and physiotherapy that improve the well-being and extend the life expectancy of people in rural or sparsely populated areas is a socially important though costly problem. We ran a pilot project to test the effectiveness potential of telerehabilitation using markerless motion capture technology integrated in a fast and low-latency IMT-2020 (5G) mobile network. Accelerating technological innovations and the surge in advances of telehealth will greatly impact conventional home visit or outpatient rehabilitation services, working in concert with or even supplanting them, given the potential lower cost and better utilization of time.

Accurate modulation of photoprinting under stiffness imaging feedback for engineering ECMs with high-fidelity mechanical properties.

Engineered extracellular matrices (ECMs) that replicate complex in-vivo features have shown great potential in tissue engineering. Biocompatible hydrogel microstructures have been widely used to replace these native ECMs for physiologically relevant research. However, accurate reproduction of the 3D hierarchical and nonuniform mechanical stiffness inside one integrated microstructure to mimic the complex mechanical properties of native ECMs presents a major challenge. Here, by using digital holographic microscopy (DHM)-based stiffness imaging feedback, we propose a novel closed-loop control algorithm to achieve high-accuracy control of mechanical properties for hydrogel microstructures that recapitulate the physiological properties of native ECMs with high fidelity. During photoprinting, the photocuring area of the hydrogel is divided into microscale grid areas to locally control the photocuring process. With the assistance of a motorized microfluidic channel, the curing thickness is controlled with layer-by-layer stacking. The DHM-based stiffness imaging feedback allows accurate adjustment of the photocuring degree in every grid area to change the crosslinking network density of the hydrogel, thus enabling large-span and high-resolution modulation of mechanical properties. Finally, the gelatin methacrylate was used as a typical biomaterial to construct the high-fidelity biomimetic ECMs. The Young's modulus could be flexibly modulated in the 10 kPa to 50 kPa range. Additionally, the modulus gradient was accurately controlled to within 2.9 kPa. By engineering ECM with locally different mechanical properties, cell spreading along the stiff areas was observed successfully. We believe that this method can regenerate complex biomimetic ECMs that closely recapitulate in-vivo mechanical properties for further applications in tissue engineering and biomedical research.

Quantification of high and low sEMG spectral components during sustained isometric contraction.

Superficial Electromyography (sEMG) spectrum contains aggregated information from several underlying physiological processes. Due to technological limitations, the isolation of these processes is challenging, and therefore, the interpretation of changes in muscle activity frequency is still controversial. Recent studies showed that the spectrum of sEMG signals recorded from isotonic and short-term isometric contractions can be decomposed into independent components whose spectral features recall those of motor unit action potentials. In this paper sEMG spectral decomposition is tested during muscle fatigue induced by long-term isometric contraction where sEMG spectral changes have been widely studied. The main goals of this work are to validate spectral component extraction during long-term isometric muscle activation and the quantification of energy exchange between the low- and high-frequency bands of sEMG signals during muscle fatigue.

Clarify Sit-to-Stand Muscle Synergy and Tension Changes in Subacute Stroke Rehabilitation by Musculoskeletal Modeling.

Post-stroke patients exhibit distinct muscle activation electromyography (EMG) features in sit-to-stand (STS) due to motor deficiency. Muscle activation amplitude, related to muscle tension and muscle synergy activation levels, is one of the defining EMG features that reflects post-stroke motor functioning and motor impairment. Although some qualitative findings are available, it is not clear if and how muscle activation amplitude-related biomechanical attributes may quantitatively reflect during subacute stroke rehabilitation. To better enable a longitudinal investigation into a patient's muscle activation changes during rehabilitation or an inter-subject comparison, EMG normalization is usually applied. However, current normalization methods using maximum voluntary contraction (MVC) or within-task peak/mean EMG may not be feasible when MVC cannot be obtained from stroke survivors due to motor paralysis and the subject of comparison is EMG amplitude. Here, focusing on the paretic side, we first propose a novel, joint torque-based normalization method that incorporates musculoskeletal modeling, forward dynamics simulation, and mathematical optimization. Next, upon method validation, we apply it to quantify changes in muscle tension and muscle synergy activation levels in STS motor control units for patients in subacute stroke rehabilitation. The novel method was validated against MVC-normalized EMG data from eight healthy participants, and it retained muscle activation amplitude differences for inter- and intra-subject comparisons. The proposed joint torque-based method was also compared with the common static optimization based on squared muscle activation and showed higher simulation accuracy overall. Serial STS measurements were conducted with four post-stroke patients during their subacute rehabilitation stay (137 ± 22 days) in the hospital. Quantitative results of patients suggest that maximum muscle tension and activation level of muscle synergy temporal patterns may reflect the effectiveness of subacute stroke rehabilitation. A quality comparison between muscle synergies computed with the conventional within-task peak/mean EMG normalization and our proposed method showed that the conventional was prone to activation amplitude overestimation and underestimation. The contributed method and findings help recapitulate and understand the post-stroke motor recovery process, which may facilitate developing more effective rehabilitation strategies for future stroke survivors.

Bio-inspired engineering of a perfusion culture platform for guided three-dimensional nerve cell growth and differentiation.

Collagen provides a promising environment for 3D nerve cell culture; however, the function of perfusion culture and cell-growth guidance is difficult to integrate into such an environment to promote cell growth. In this paper, we develop a bio-inspired design method for constructing a perfusion culture platform for guided nerve cell growth and differentiation in collagen. Based on the anatomical structure of peripheral neural tissue, a biomimetic porous structure (BPS) is fabricated by two-photon polymerization of IP-Visio. The micro-capillary effect is then utilized to facilitate the self-assembly of cell encapsulated collagen into the BPS. 3D perfusion culture can be rapidly implemented by inserting the cell-filled BPS into a pipette tip connected with syringe pumps. Furthermore, we investigate the nerve cell behavior in the BPS. 7-channel aligned cellular structures surrounded with a Schwann cell layer can be stably formed after a long-time perfusion culture. Differentiation of PC12 cells and mouse neural stem cells shows 3D neurite outgrowth alignment and elongation in collagen. The calcium activities of differentiated PC12 cells are visualized for confirming the preliminary formation of cell function. These results demonstrate that the proposed bio-inspired 3D cell culture platform with the advantages of miniaturization, structure complexity and perfusion has great potential for future application in the study of nerve regeneration and drug screening.

Classification of Motor Impairments of Post-Stroke Patients Based on Force Applied to a Handrail.

Many patients suffer from declined motor abilities after a brain injury. To provide appropriate rehabilitation programs and encourage motor-impaired patients to participate further in rehabilitation, sufficient and easy evaluation methodologies are necessary. This study is focused on the sit-to-stand motion of post-stroke patients because it is an important daily activity. Our previous study utilized muscle synergies (synchronized muscle activation) to classify the degree of motor impairment in patients and proposed appropriate rehabilitation methodologies. However, in our previous study, the patient was required to attach electromyography sensors to his/her body; thus, it was difficult to evaluate motor ability in daily circumstances. Here, we developed a handrail-type sensor that can measure the force applied to it. Using temporal features of the force data, the relationship between the degree of motor impairment and temporal features was clarified, and a classification model was developed using a random forest model to determine the degree of motor impairment in hemiplegic patients. The results show that hemiplegic patients with severe motor impairments tend to apply greater force to the handrail and use the handrail for a longer period. It was also determined that patients with severe motor impairments did not move forward while standing up, but relied more on the handrail to pull their upper body upward as compared to patients with moderate impairments. Furthermore, based on the developed classification model, patients were successfully classified as having severe or moderate impairments. The developed classification model can also detect long-term patient recovery. The handrail-type sensor does not require additional sensors on the patient's body and provides an easy evaluation methodology.

Effects of Force Modulation on Large Muscles during Human Cycling.

Voluntary force modulation is defined as the ability to tune the application of force during motion. However, the mechanisms behind this modulation are not yet fully understood. In this study, we examine muscle activity under various resistance levels at a fixed cycling speed. The main goal of this research is to identify significant changes in muscle activation related to the real-time tuning of muscle force. This work revealed significant motor adaptations of the main muscles utilized in cycling as well as positive associations between the force level and the temporal and spatial inter-cycle stability in the distribution of sEMG activity. From these results, relevant biomarkers of motor adaptation could be extracted for application in clinical rehabilitation to increase the efficacy of physical therapy.

Subjective time compression induced by continuous action.

Increasing evidence indicates that voluntary actions can modulate the subjective time experience of its outcomes to optimize dynamic interaction with the external environment. In the present study, using a temporal reproduction task where participants reproduced the duration of an auditory stimulus to which they were previously exposed by performing different types of voluntary action, we examined how the subjective time experience of action outcomes changed with voluntary action types. Two experiments revealed that the subjective time experience of action outcomes was compressed, compared with physical time, if the action was performed continuously (Experiment 1), possibly enhancing the experience of controlling the action outcome, or if the action was added an extra task-unrelated continuous action (Experiment 2), possibly reflecting different underlying mechanisms from subjective time compression induced by the task-related continuous action. The majority of prior studies have focused on the subjective time experience of action outcomes when actions were performed voluntarily or not, and no previous study has examined the effects of differences in voluntary action types on the subjective time experience of action outcomes. These findings may be useful in situations in which people wish to intentionally compress their own time experience of daily events through their voluntary actions.

Electroencephalographic Functional Connectivity With the Tacit Learning System Prosthetic Hand: A Case Series Using Motor Imagery.

We previously created a prosthetic hand with a tacit learning system (TLS) that automatically supports the control of forearm pronosupination. This myoelectric prosthetic hand enables sensory feedback and flexible motor output, which allows users to move efficiently with minimal burden. In this study, we investigated whether electroencephalography can be used to analyze the influence of the auxiliary function of the TLS on brain function. Three male participants who had sustained below-elbow amputations and were myoelectric prosthesis users performed a series of physical movement trials with the TLS inactivated and activated. Trials were video recorded and a sequence of videos was prepared to represent each individual's own use while the system was inactivated and activated. In a subsequent motor imagery phase during which electroencephalography (EEG) signals were collected, each participant was asked to watch both videos of themself while actively imagining the physical movement depicted. Differences in mean cortical current and amplitude envelope correlation (AEC) values between supplementary motor areas (SMA) and each vertex were calculated. For all participants, there were differences in the mean cortical current generated by the motor imagery tasks when the TLS inactivated and activated conditions were compared. The AEC values were higher during the movement imagery task with TLS activation, although their distribution on the cortex varied between the three individuals. In both S1 and other brain areas, AEC values increased in conditions with the TLS activated. Evidence from this case series indicates that, in addition to motor control, TLS may change sensory stimulus recognition.