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.

Genome-Wide mRNA and Long Non-Coding RNA Analysis of Porcine Trophoblast Cells Infected with Porcine Reproductive and Respiratory Syndrome Virus Associated with Reproductive Failure.

Porcine reproductive and respiratory syndrome (PRRS) is a vertically transmitted reproductive disorder that is typically characterized by miscarriage, premature birth, and stillbirth in pregnant sows after infection. Such characteristics indicate that PRRSV can infect and penetrate the porcine placental barrier to infect fetus piglets. The porcine trophoblast is an important component of the placental barrier, and secretes various hormones, including estrogen and progesterone, to maintain normal pregnancy and embryonic development during pregnancy. It is conceivable that the pathogenic effects of PRRSV infection on porcine trophoblast cells may lead to reproductive failure; however, the underlying detailed mechanism of the interaction between porcine trophoblast (PTR2) cells and PRRSV is unknown. Therefore, we conducted genome-wide mRNA and long non-coding RNA (lncRNA) analysis profiling in PRRSV-infected PTR2. The results showed that 672 mRNAs and 476 lncRNAs were significantly different from the control group after viral infection. Target genes of the co-expression and co-location of differential mRNAs and lncRNAs were enriched by GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, revealing that most of the pathways were involved in cell nutrient metabolism, cell proliferation, and differentiation. Specifically, the estrogen signaling pathway, the PI3K (PhosphoInositide-3 Kinase)-Akt (serine/threonine kinase) signaling pathway, and the insulin secretion related to embryonic development were selected for analysis. Further research found that PRRSV inhibits the expression of G-protein-coupled estrogen receptor 1 (GPER1), thereby reducing estrogen-induced phosphorylation of AKT and the mammalian target of rapamycin (mTOR). The reduction in the phosphorylation of AKT and mTOR blocks the activation of the GPER1- PI3K-AKT-mTOR signaling pathway, consequently restraining insulin secretion, impacting PTR2 cell proliferation, differentiation, and nutrient metabolism. We also found that PRRSV triggered trophoblast cell apoptosis, interrupting the integrity of the placental villus barrier. Furthermore, the interaction network diagram of lncRNA, regulating GPER1 and apoptosis-related genes, was constructed, providing a reference for enriching the functions of these lncRNA in the future. In summary, this article elucidated the differential expression of mRNA and lncRNA in trophoblast cells infected with PRRSV. This infection could inhibit the PI3K-AKT-mTOR pathway and trigger apoptosis, providing insight into the mechanism of the vertical transmission of PRRSV and the manifestation of reproductive failure.

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.

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.

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.

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.

Biomechanical differences during ascent on regular stairs and on a stairmill.

Stair climbing is an intense physical activity and requires large range of motion at the joints, adequate muscle strength, and balance control. A powered stairmill, integrated with a gait rehabilitation device, can potentially be used for training those who have difficulty climbing stairs. In order to assess the effectiveness of such an approach, it is necessary to understand the similarities and differences in walking on regular stairs and on a stairmill. We have conducted an experiment to compare the differences in kinematics and muscle activations during climbing on regular stairs and a stairmill. Twelve subjects participated in this study. They first walked on regular stairs five times and then performed a one-minute continuous walking on a stairmill. The results showed several important differences. During continuous walking on a stairmill, when compared to regular stairs, there was (i) an increase in the percentage of stance phase during a walking cycle, (ii) a higher angle of plantarflexion of the ankle during the transition from stance phase to swing phase, and (iii) a decrease in muscle activation of the tibialis anterior during swing phase. These differences would provide additional insights into the design of future rehabilitation systems and to interpret human data obtained from stairmills.

Temporal Features of Muscle Synergies in Sit-to-Stand Motion Reflect the Motor Impairment of Post-Stroke Patients.

Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people ( n = 12 ) and post-stroke patients ( n = 33 ), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that post-stroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for post-stroke patients that focuses on activation timing of muscle synergies.

Organization of functional modularity in sitting balance response and gait performance after stroke.

BACKGROUND: Recovery of postural adjustment, especially when seated, is important for performing activities of daily living after stroke. However, conventional clinical measures provide little insight into a common strategy for dynamic sitting balance and gait. We aimed to evaluate functional re-organization of posture and ambulatory performance after stroke. METHODS: The subjects of the study included 5 healthy men and 21 post-stroke patients. The spatiotemporal modular organization of ground reaction forces during a balance task in which the leg on the non-affected side was lifted off the ground while seated was quantified by using complex principal component analysis. FINDINGS: A 3% decrease in the temporal strength of the primary module in post-stroke patients was an independent predictor of gait performance in the hospital setting with high sensitivity and specificity. Tuning of the temporal strength was accompanied by the recovery of sitting and ambulation. INTERPRETATION: Our findings suggest that evaluation of the modular characteristics of ground reaction forces during a sitting balance task allows us to predict recovery and functional adaptation through daily physical rehabilitation.