Use of functional magnetic resonance imaging to identify cortical loci for lower limb movements and their efficacy for individuals after stroke.

BACKGROUND: Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. METHODS: We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl-Meyer Assessment (FMA) scale. RESULTS: The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: - 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; - 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). CONCLUSIONS: The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.

Multi-joint Assessment of Proprioception Impairments Poststroke.

OBJECTIVES: To investigate shoulder, elbow and wrist proprioception impairment poststroke. DESIGN: Proprioceptive acuity in terms of the threshold detection to passive motion at the shoulder, elbow and wrist joints was evaluated using an exoskeleton robot to the individual joints slowly in either inward or outward direction. SETTING: A university research laboratory. PARTICIPANTS: Seventeen stroke survivors and 17 healthy controls (N=34). Inclusion criteria of stroke survivors were (1) a single stroke; (2) stroke duration <1 year; and (3) cognitive ability to follow simple instructions. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Threshold detection to passive motion and detection error at the shoulder, elbow and wrist. RESULTS: There was significant impairment of proprioceptive acuity in stroke survivors as compared to healthy group at all 3 joints and in both the inward (shoulder horizontal adduction, elbow and wrist flexion, P<.01) and outward (P<.01) motion. Furthermore, the distal wrist joint showed more severe impairment in proprioception than the proximal shoulder and elbow joints poststroke (P<.01) in inward motion. Stroke survivors showed significantly larger detection error in identifying the individual joint in motion (P<.01) and the movement direction (P<.01) as compared to the healthy group. There were significant correlations among the proprioception acuity across the shoulder, elbow and wrist joints and 2 movement directions poststroke. CONCLUSIONS: There were significant proprioceptive sensory impairments across the shoulder, elbow and wrist joints poststroke, especially at the distal wrist joint. Accurate evaluations of multi-joint proprioception deficit may help guide more focused rehabilitation.

Venous congestion affects neuromuscular changes in pigs in terms of muscle electrical activity and muscle stiffness.

Early detection of venous congestion (VC)-related diseases such as deep vein thrombosis (DVT) is important to prevent irreversible or serious pathological conditions. However, the current way of diagnosing DVT is only possible after recognizing advanced DVT symptoms such as swelling, pain, and tightness in affected extremities, which may be due to the lack of information on neuromechanical changes following VC. Thus, the goal of this study was to investigate acute neuromechanical changes in muscle electrical activity and muscle stiffness when VC was induced. The eight pigs were selected and the change of muscle stiffness from the acceleration and muscle activity in terms of integral electromyography (IEMG) was investigated in three VC stages. Consequently, we discovered a significant increase in the change in muscle stiffness and IEMG from the baseline to the VC stages (p < 0.05). Our results and approach can enable early detection of pathological conditions associated with VC, which can be a basis for further developing early diagnostic tools for detecting VC-related diseases.

Classification of deep vein thrombosis stages using convolutional neural network of electromyogram with vibrotactile stimulation toward developing an early diagnostic tool: A preliminary study on a pig model.

Deep vein thrombosis (DVT) can lead to life-threatening disorders; however, it can only be recognized after its symptom appear. This study proposed a novel method that can detect the early stage of DVT using electromyography (EMG) signals with vibration stimuli using the convolutional neural networks (CNN) algorithm. The feasibility of the method was tested with eight legs before and after the surgical induction of DVT at nine-time points. Furthermore, perfusion pressure (PP), intracompartmental pressure (IP), and shear elastic modulus (SEM) of the tibialis anterior were also collected. In the proposed method, principal component analysis (PCA) and CNN were used to analyze the EMG data and classify it before and after the DVT stages. The cross-validation was performed in two strategies. One is for each leg and the other is the leave-one-leg-out (LOLO), test without any predicted information, for considering the practical diagnostic tool. The results showed that PCA-CNN can classify before and after DVT stages with an average accuracy of 100% (each leg) and 68.4±20.5% (LOLO). Moreover, all-time points (before induction of DVT and eight-time points after DVT) were classified with an average accuracy of 72.0±11.9% which is substantially higher accuracy than the chance levels (11% for 9-class classification). Based on the experimental results in the pig model, the proposed CNN-based method can classify the before- and after-DVT stages with high accuracy. The experimental results can provide a basis for further developing an early diagnostic tool for DVT using only EMG signals with vibration stimuli.

Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface.

Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54±7.78% (288 trials), 85.76 ±8.00% (240 trials), 84.65±8.11% (192 trials), and 83.29 ±8.25% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.

High-Frequency Vibrating Stimuli Using the Low-Cost Coin-Type Motors for SSSEP-Based BCI.

Steady-state somatosensory-evoked potential- (SSSEP-) based brain-computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long-time training. Despite the advancement of various noninvasive electroencephalogram- (EEG-) based BCI paradigms, researches on SSSEP with the various frequency range and related classification algorithms are relatively unsettled. In this study, we investigated the feasibility of classifying the SSSEP within high-frequency vibration stimuli induced by a versatile coin-type eccentric rotating mass (ERM) motor. Seven healthy subjects performed selective attention (SA) tasks with vibration stimuli attached to the left and right index fingers. Three EEG feature extraction methods, followed by a support vector machine (SVM) classifier, have been tested: common spatial pattern (CSP), filter-bank CSP (FBCSP), and mutual information-based best individual feature (MIBIF) selection after the FBCSP. Consequently, the FBCSP showed the highest performance at 71.5 ± 2.5% for classifying the left and right-hand SA tasks than the other two methods (i.e., CSP and FBCSP-MIBIF). Based on our findings and approach, the high-frequency vibration stimuli using low-cost coin motors with the FBCSP-based feature selection can be potentially applied to developing practical SSSEP-based BCI systems.

Improvements in hand functions and changes in proximal muscle activities in myoelectric prosthetic hand users at home: a case series.

BACKGROUND: Adaptation in proximal muscles for daily motor tasks after sustained use of a prosthetic hand has not been fully understood. OBJECTIVES: This study aimed to investigate changes in hand functions and activities of proximal muscles after multiple weeks of using a myoelectric prosthetic hand at home. STUDY DESIGN: Repeated measures. METHODS: Four people with traumatic upper-limb loss used a myoelectric prosthetic hand (bebionic) at home over the 6- to 8-week period. A user survey, Orthotics and Prosthetics User Survey for Upper Extremity Functional Status 2.0, was used to measure upper-limb functions and the degree of using the prosthetic hand each week. Their hand functions, muscle activities, and grip-specific neuromuscular effort were evaluated by the Southampton Hand Assessment Procedure at the preassessment and postassessment sessions (PRE and POST, respectively). RESULTS: All subjects increased Southampton Hand Assessment Procedure scores at PRE compared with POST with subject-specific changes in muscle activations. In a detail, at POST, subject 1 reduced the shoulder muscle activity compared with PRE, while at POST, subject 2 reduced biceps activity compared with PRE. At POST, greater pectoralis activity and reduced trapezius activity were observed in subject 3, and greater activity in those two muscles was found in subject 4 compared with PRE. CONCLUSION: After multiple weeks of using the myoelectric prosthetic hands, their hand functions during ADL tasks were improved and changes in the muscle activities were found.

Quantification of Upper Limb Isometric Force Control Abilities for Evaluating Upper Limb Functions Among Prosthetic Users.

Force control abilities are essential to interact with objects in our environments. However, there is a lack of evaluation tools and methods to test the force control abilities of the upper limb in evaluating the upper limb functions of prosthetic users. This study aimed to quantify upper limb isometric force control abilities in healthy individuals and prosthetic users using a custom-built handle with a 6-axis force/torque sensor and visual cue, namely an Upper Limb End-effector type Force control test device (ULEF). Feasibilities of the test device were demonstrated through experiments by holding the ULEF with an intact hand among healthy subjects and transradial and wrist amputees with a myoelectric powered prosthetic hand, the bebionic hand. Compared to the healthy individuals, the prosthetic user group demonstrated poor isometric force control abilities in terms of higher control instability during the lateral direction task ( [Formula: see text]). Significantly higher variability in force-generating rates was also found in all task directions in the prosthetic user group ( [Formula: see text]). Compared to the healthy group, the prosthetic user group showed significant small peak biceps activities during the posterior task ( [Formula: see text]) and anterior task ( [Formula: see text]). Quantification of isometric upper limb force control abilities can potentially be beneficial to develop evaluation and research tools for investigating mechanisms underlying force control abilities of prosthetic users and provide guidelines for targeted isometric force control training and prosthesis development.

Developing an in-vivo physiological porcine model of inducing acute atraumatic compartment syndrome towards a non-invasive diagnosis using shear wave elastography.

Compartment syndrome (CS) is a pathological event caused by elevated intracompartmental pressure (ICP); however, changes from the onset of inducing atraumatic CS remained unclear. The study aimed to investigate the physiological changes in a newly developed in vivo porcine acute atraumatic CS model. CS was induced by ischemia-reperfusion injury in the left hind leg of fourteen pigs divided into an echogenicity group (EG) and a shear wave elastography group (SEG). Echogenicity was measured in EG, and shear elastic modulus (SEM) was measured in SEG seven times before, at the onset of inducing CS, and every 30 min after the onset over eight hours. Simultaneously, ICP, blood pressure, and muscle perfusion pressure (MPP) were also measured in both groups. Our results indicate that SEM of the experimental leg in SEG significantly increased as CS developed compared to the control leg (p = 0.027), but no statistical difference in the echogenicity in EG was found between the experimental leg and control leg. There were also significant correlations between SEM and ICP (p < 0.001) and ICP and MPP (p < 0.001). Our method and findings can be a basis to develop a non-invasive diagnostic tool using a shear wave elastography for atraumatic CS.

Multi-Domain Convolutional Neural Networks for Lower-Limb Motor Imagery Using Dry vs. Wet Electrodes.

Motor imagery (MI) brain-computer interfaces (BCIs) have been used for a wide variety of applications due to their intuitive matching between the user's intentions and the performance of tasks. Applying dry electroencephalography (EEG) electrodes to MI BCI applications can resolve many constraints and achieve practicality. In this study, we propose a multi-domain convolutional neural networks (MD-CNN) model that learns subject-specific and electrode-dependent EEG features using a multi-domain structure to improve the classification accuracy of dry electrode MI BCIs. The proposed MD-CNN model is composed of learning layers for three domain representations (time, spatial, and phase). We first evaluated the proposed MD-CNN model using a public dataset to confirm 78.96% classification accuracy for multi-class classification (chance level accuracy: 30%). After that, 10 healthy subjects participated and performed three classes of MI tasks related to lower-limb movement (gait, sitting down, and resting) over two sessions (dry and wet electrodes). Consequently, the proposed MD-CNN model achieved the highest classification accuracy (dry: 58.44%; wet: 58.66%; chance level accuracy: 43.33%) with a three-class classifier and the lowest difference in accuracy between the two electrode types (0.22%, d = 0.0292) compared with the conventional classifiers (FBCSP, EEGNet, ShallowConvNet, and DeepConvNet) that used only a single domain. We expect that the proposed MD-CNN model could be applied for developing robust MI BCI systems with dry electrodes.

Upper-Limb Electromyogram Classification of Reaching-to-Grasping Tasks Based on Convolutional Neural Networks for Control of a Prosthetic Hand.

In recent years, myoelectric interfaces using surface electromyogram (EMG) signals have been developed for assisting people with physical disabilities. Especially, in the myoelectric interfaces for robotic hands or arms, decoding the user's upper-limb movement intentions is cardinal to properly control the prosthesis. However, because previous experiments were implemented with only healthy subjects, the possibility of classifying reaching-to-grasping based on the EMG signals from the residual limb without the below-elbow muscles was not investigated yet. Therefore, we aimed to investigate the possibility of classifying reaching-to-grasping tasks using the EMG from the upper arm and upper body without considering wrist muscles for prosthetic users. In our study, seven healthy subjects, one trans-radial amputee, and one wrist amputee were participated and performed 10 repeatable 12 reaching-to-grasping tasks based on the Southampton Hand Assessment Procedure (SHAP) with 12 different weighted (light and heavy) objects. The acquired EMG was processed using the principal component analysis (PCA) and convolutional neural network (CNN) to decode the tasks. The PCA-CNN method showed that the average accuracies of the healthy subjects were 69.4 ± 11.4%, using only the EMG signals by the upper arm and upper body. The result with the PCA-CNN method showed 8% significantly higher accuracies than the result with the widely used time domain and auto-regressive-support vector machine (TDAR-SVM) method as 61.6 ± 13.7%. However, in the cases of the amputees, the PCA-CNN showed slightly lower performance. In addition, in the aspects of assistant daily living, because grip force is also important when grasping an object after reaching, the possibility of classifying the two light and heavy objects in each reaching-to-grasping task was also investigated. Consequently, the PCA-CNN method showed higher accuracy at 70.1 ± 9.8%. Based on our results, the PCA-CNN method can help to improve the performance of classifying reaching-to-grasping tasks without wrist EMG signals. Our findings and decoding method can be implemented to further develop a practical human-machine interface using EMG signals.

Developing a Quantifying Device for Soft Tissue Material Prop-Erties around Lumbar Spines.

Knowing the material properties of the musculoskeletal soft tissue could be important to develop rehabilitation therapy and surgical procedures. However, there is a lack of devices and information on the viscoelastic properties of soft tissues around the lumbar spine. The goal of this study was to develop a portable quantifying device for providing strain and stress curves of muscles and ligaments around the lumbar spine at various stretching speeds. Each sample was conditioned and applied for 20 repeatable cyclic 5 mm stretch-and-relax trials in the direction and perpendicular direction of the fiber at 2, 3 and 5 mm/s. Our device successfully provided the stress and strain curve of the samples and our results showed that there were significant effects of speed on the young's modulus of the samples (p < 0.05). Compared to the expensive commercial device, our lower-cost device provided comparable stress and strain curves of the sample. Based on our device and findings, various sizes of samples can be measured and viscoelastic properties of the soft tissues can be obtained. Our portable device and approach can help to investigate young's modulus of musculoskeletal soft tissues conveniently, and can be a basis for developing a material testing device in a surgical room or various lab environments.

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton.

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.

Plane Dependent Subject-Specific Neuromuscular Training for Knee Rehabilitation.

Knee injuries at risk of post-traumatic knee osteoarthritis (PTOA) and knee osteoarthritis (OA) are closely associated with knee transverse plane and/or frontal plane instability and excessive loading. However, most existing training and rehabilitation devices involve mainly movements in the sagittal plane. An offaxis elliptical training system was developed to train and evaluate neuromuscular control about the off-axes (knee varus/valgus and tibial rotation) as well as the main flexion/extension axis (sagittal movements). Effects of the offaxis elliptical training system in improving either transverse or frontal neuromuscular control depending on subjects' need (Pivoting group, Sliding group) were demonstrated through 6-week subject-specific neuromuscular training in subjects with knee injuries at risk of PTOA or medial knee osteoarthritis. The combined pivoting and sliding group, named as offxis group demonstrated significant reduction in pivoting instability, minimum pivoting angle, and sliding instability. The pivoting group showed more reduction in pivoting instability, maximum and minimum pivoting angle than the sliding group. On the other hand, the sliding group showed more reduction in sliding instability, maximum and minimum sliding distance than the pivoting group. Based on these findings, the offaxis elliptical trainer system can potentially be used as a therapeutic and research tool to train human subjects for plane-dependent improvements in their neuromuscular control during functional weight-bearing stepping movements.

Combined Ankle/Knee Stretching and Pivoting Stepping Training for Children With Cerebral Palsy.

Although various treatment methods have been investigated to reduce spasticity and intoeing gait in children with cerebral palsy (CP), methods to concurrently reduce an intoeing gait and associated ankle/knee stiffness and spasticity according to a child's specific needs are lacking. This study aimed to develop a training program to improve walking function and transverse-plane (pivoting) neuromuscular control and reduce spasticity and intoeing gait deviations. Eight children with diplegic CP and intoeing gait participated in this 6-week combined robotic ankle and/or knee intelligent stretching and pivoting neuromuscular control training program (Subject-specific Stretching and Pivoting Off-axis Neuromuscular control Training, [SS-POINT]). The effects of SS-POINT were evaluated using neuromechanical, functional, and clinical outcome measures and compared to those for eight children with CP and intoeing gait who participated in pivoting neuromuscular control training (POINT) alone in a previous study. RESULTS: After the SS-POINT program, subjects with CP showed reduced knee stiffness and intoeing angle, and improvements in both joint and leg functions in terms of ankle and knee active range of motion, ankle dorsiflexor strength, proprioception, walking speed, balance, and minimum pivoting angle. Furthermore, improvements in proprioceptive acuity and minimum pivoting angle after the SS-POINT were greater than those after the POINT. CONCLUSION: The SS-POINT approach can be used as a subject-specific training program for improving leg and walking functions and reducing intoeing during gait. SIGNIFICANCE: This approach can serve as an individualized intervention at the joint and walking levels to maximize intervention effects by adjusting training targets, sequences, and intensities to meet the individual needs of children with CP.

Real-Time Three-Dimensional Knee Moment Estimation in Knee Osteoarthritis: Toward Biodynamic Knee Osteoarthritis Evaluation and Training.

We investigated differences in knee kinetic variables (external knee adduction, flexion, internal rotation moments, and impulses) between patients with knee osteoarthritis (KOA) and healthy controls during stepping on a custom elliptical trainer; and searched knee kinetic variable candidates for real-time biofeedback and for complementing diagnosis/evaluation on the elliptical trainer based on the knee kinetic variables' associations with the knee injury and osteoarthritis outcome score (KOOS). Furthermore, we explored potential gait re-training strategies on the elliptical trainer by investigating the knee kinetic variables' associations with 3-D ankle angles. The knee kinetic variables and ankle angles were determined in real-time in a patient group of 10 patients with KOA and an age-and sex-matched control group of 10 healthy subjects. The mean peak external knee adduction moment of the patient group was 47% higher than that of the control group. The KOOS-Sports and Recreational Activities and KOOS-Pain scores were found to be significantly associated with the knee kinetic variables. All the ankle angles were associated with the knee kinetic variables. The findings support the use of the knee kinetic variables on the elliptical trainer to complement KOA diagnosis quantitatively and provide potential real-time KOA gait re-training strategies/guides.

Learning Patterns of Pivoting Neuromuscular Control Training-Toward a Learning Model for Therapy Scheduling.

OBJECTIVE: The goal of this study was to investigate the learning patterns in leg pivoting neuromuscular control performance over six-week pivoting neuromuscular control training (POINT) and to estimate how many sessions at beginning are needed to estimate the overall pivoting neuromuscular control learning curve. METHODS: Twenty subjects (ten females, ten males) participated in 18 sessions of POINT (three sessions per week for six weeks) program using an off-axis elliptical trainer. Performance measures including pivoting instability and stepping speed were quantified for each study session during a stepping task while subjects were asked to control pivoting movements under a slippery condition. Learning curve relating the pivoting instability to training sessions was quantified by the power law and by the exponential curve as a function of sessions or days with three parameters: The limit of learning, rate of learning, and learning capacity. RESULTS: The power and exponential learning models characterized the learning curves similarly with no differences in [Formula: see text]. No significant sex differences were found in the limit of learning, rate of learning, and learning capacity. Based on [Formula: see text] and RMSE, data from the first three study sessions might be enough to estimate the pivoting neuromuscular performance over the whole training period. CONCLUSION: The findings showed that subjects' motor skills to improve pivoting instability followed the learning curve models. SIGNIFICANCE: The findings and models can potentially be used to develop more effective subject-specific therapy scheduling.

EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors.

Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.

Improvement in Offaxis Neuromuscular Control Under Slippery Conditions Following Six-Week Pivoting Leg Neuromuscular Training.

Knee injuries are usually associated with offaxis loadings in the transverse and frontal planes. Thus, improvement of lower limb offaxis neuromuscular control is important in knee injury prevention and post-injury rehabilitation. The goal of this paper was to investigate the effects of six-week pivoting offaxis intensity adjustable neuromuscular control training (POINT) using a custom-made offaxis elliptical trainer on lower limb offaxis neuromuscular control performance in trained and untrained functional tasks under slippery conditions. Twenty-six subjects participated in 18 sessions of POINT (three sessions per week for six weeks) and 25 subjects served as controls who did a regular workout. Offaxis neuromuscular control performance measures in terms of pivoting instability, sliding instability, and time-to-peak offaxis EMG entropy were evaluated on both groups under slippery conditions including a trained free pivoting task and untrained free sliding task and free pivoting and sliding task. Compared with the control group, the training group significantly decreased pivoting instability and the time-to-peak offaxis EMG entropy in lower limb muscles, indicating improvement in offaxis neuromuscular control performance. Furthermore, the training group showed reduced pivoting instability and sliding instability during the untrained free pivoting and sliding task. This paper may help us develop more focused and effective offaxis training programs to reduce knee injuries associated with offaxis loadings.