Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements

BACKGROUND AND OBJECTIVE

For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training.

METHODS

This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern.

RESULTS

Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent.

CONCLUSION

The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Feasibility Evaluation of Online Classification-based Control for Gross Movement in a 2-DoF Prosthetic Arm

Regression and classification models have been extensively studied to exploit the myoelectric and kinematic input information from the residual limb for the control of multiple degree-of-freedom (DoF) powered prostheses. The gross movement control of above-elbow prostheses is mainly based on regression models which map the available inputs to continuous prosthetic poses. However, the regression output is sensitive to the variation in the input signal. The myoelectric signal variation is usually large due to unintentional muscle contractions, which can deteriorate the user-in-the-loop performance with respect to the offline analysis. Alternatively, the classification models offer the advantage of being more robust to the input signal variation, but they were predominantly used for fine motor functions such as grasping. For gross motor functions, the discrete output may cause issues. Therefore, this work attempts to investigate the feasibility of utilising the classification model to control a 2-DoF transhumeral prosthesis for gross movement. The performance of 6 able-bodied subjects was evaluated in performing reaching and orientation matching tasks with a prosthetic arm in a virtual reality environment. The results were compared with the case of using their intact arms and existing results using the regression model. Our findings indicate that the classification-based method provides comparable performance to the regression model, making it a potential alternative for gross arm movement in multi-DoF prosthetic arms.

A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion

Wearable sensing solutions have emerged as a promising paradigm for monitoring human musculoskeletal state in an unobtrusive way. To increase the deployability of these systems, considerations related to cost reduction and enhanced form factor and wearability tend to discourage the number of sensors in use. In our previous work, we provided a theoretical solution to the problem of jointly reconstructing the entire muscular-kinematic state of the upper limb, when only a limited amount of optimally retrieved sensory data are available. However, the effective implementation of these methods in a physical, under-sensorized wearable has never been attempted before. In this work, we propose to bridge this gap by presenting an under-sensorized system based on inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes for the reconstruction of the upper limb musculoskeletal state, focusing on the minimization of the sensors' number. We found that, relying on two IMUs only and eight sEMG sensors, we can conjointly reconstruct all 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state, yielding a median normalized RMS error of 8.5% on the non-measured joints and 2.5% on the non-measured muscles.

Evaluating the effect of non-invasive force feedback on prosthetic grasp force modulation in participants with and without limb loss

Grasping an object is one of the most common and complex actions performed by humans. The human brain can adapt and update the grasp dynamics through information received from sensory feedback. Prosthetic hands can assist with the mechanical performance of grasping, however currently commercially available prostheses do not address the disruption of the sensory feedback loop. Providing feedback about a prosthetic hand's grasp force magnitude is a top priority for those with limb loss. This study tested a wearable haptic system, i.e., the Clenching Upper-Limb Force Feedback device (CUFF), which was integrated with a novel robotic hand (The SoftHand Pro). The SoftHand Pro was controlled with myoelectrics of the forearm muscles. Five participants with limb loss and nineteen able-bodied participants completed a constrained grasping task (with and without feedback) which required modulation of the grasp to reach a target force. This task was performed while depriving participants of incidental sensory sources (vision and hearing were significantly limited with glasses and headphones). The data were analyzed with Functional Principal Component Analysis (fPCA). CUFF feedback improved grasp precision for participants with limb loss who typically use body-powered prostheses as well as a sub-set of able-bodied participants. Further testing, that is more functional and allows participants to use all sensory sources, is needed to determine if CUFF feedback can accelerate mastery of myoelectric control or would benefit specific patient sub-groups.

Separability of Input Features and the Resulting Accuracy in Classifying Target Poses for Active Transhumeral Prosthetic Interfaces

In active prostheses, it is desired to achieve target poses for a given family of tasks, for example, in the task of forward reaching using a transhumeral prosthesis with coordinated joint movements. To do so, it is necessary to distinguish these target poses accurately using the input features (e.g. kinematic and sEMG) obtained from the human users. However, the input features have conventionally been selected through human observations and influenced heavily by the availability of sensors in this context, which may not always yield the most relevant information to differentiate the target poses in the given task. In order to better select from a pool of available input features, those most appropriate for a given set of target poses, a measure that correlates well with the resulting classification accuracy is required so that it can inform the interface design process. In this paper, a scatter-matrix based class separability measure is adopted to quantitatively evaluate the separability of the target poses from their corresponding input features. A human experiment was performed on ten able-bodied subjects. Subjects were asked to perform forward-reaching movements with their arms on nine target poses in a virtual reality (VR) platform and the corresponding kinematic information of their arm movement and muscle activities were recorded. The accuracy of the prosthetic interface in determining the intended target poses of the human user during forward reaching is evaluated for different combinations of input features, selected from the kinematic and sEMG sensors worn by the users. The results demonstrate that employing input features that yield a high separability measure between target poses results in a high accuracy in identifying the intended target poses in the execution of the task.

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning

Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit decomposition-based method, this work presents a novel neural interface for human gait tracking based on muscle synergy, the high-level neural control information to collaborate muscle groups for performing movements. Three classical synergy extraction approaches include Principle Component Analysis (PCA), Factor Analysis (FA), and Nonnegative Matrix Factorization (NMF), are employed for muscle synergy extraction. A deep regression neural network based on the bidirectional gated recurrent unit (BGRU) is used to extract temporal information from the synergy matrix to estimate joint angles of the lower limb. Eight subjects participated in the experiment while walking at four types of speed: 0.5km/h, 1.0km/h, 2.0km/h, and 3.0km/h. Two machine learning methods based on linear regression (LR) and multilayer perceptron (MLP) are set as the contrast group. The result shows that the synergy-based approach's performance outperforms two contrast methods with R_var² scores of 0.83~0.88. PCA reaches the highest performance of 0.871±0.029, corresponding to RMSE of 3.836°, 6.278°, 2.197° for hip, knee, and ankle, respectively. The effect of walking speed, synergy number, and joint location will be analyzed. The performance shows that muscle synergy has a good correlation will joint angles which can be unearthed by deep learning. The proposed method explores a new way for gait analysis and contributes to building a novel neural interface with muscle synergy and deep learning.

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

BACKGROUND

Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization.

CONTRIBUTION

This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.

Toward brain-heart computer interfaces: a study on the classification of upper limb movements using multisystem directional estimates

OBJECTIVE

Brain-computer interfaces (BCI) exploit computational features from brain signals to perform a given task. Despite recent neurophysiology and clinical findings indicating the crucial role of functional interplay between brain and cardiovascular dynamics in locomotion, heartbeat information remains to be included in common BCI systems. In this study, we exploit the multidimensional features of directional and functional interplay between electroencephalographic and heartbeat spectra to classify upper limb movements into three classes.

APPROACH

We gathered data from 26 healthy volunteers that performed 90 movements; the data were processed using a recently proposed framework for brain-heart interplay (BHI) assessment based on synthetic physiological data generation. Extracted BHI features were employed to classify, through sequential forward selection scheme and k-nearest neighbors algorithm, among resting state and three classes of movements according to the kind of interaction with objects.

MAIN RESULTS

The results demonstrated that the proposed brain-heart computer interface (BHCI) system could distinguish between rest and movement classes automatically with an average 90% of accuracy.

SIGNIFICANCE

Further, this study provides neurophysiology insights indicating the crucial role of functional interplay originating at the cortical level onto the heart in the upper limb neural control. The inclusion of functional BHI insights might substantially improve the neuroscientific knowledge about motor control, and this may lead to advanced BHCI systems performances.

Hand Kinematics Characterization While Performing Activities of Daily Living Through Kinematics Reduction

Improving the understanding of hand kinematics during the performance of activities of daily living may help improve the control of hand prostheses and hand function assessment. This work identifies sparse synergies (each degree of freedom is present mainly in only one synergy), representative of the global population, with emphasis in unveiling the coordination of joints with small range of motion (palmar arching and fingers abduction). The study is the most complete study described in the literature till now, involving 22 healthy subjects and 26 representative day-to-day life activities. Principal component analysis was used to reduce the original 16 angles recorded with an instrumented glove. Five synergies explained 75% of total variance: closeness (coordinated flexion and abduction of metacarpophalangeal finger joints), digit arching (flexion of proximal interphalangeal joints), palmar-thumb coordination (coordination of palmar arching and thumb carpometacarpal flexion), thumb opposition, and thumb arch. The temporal evolution of these synergies is provided during reaching per intended grasp and during manipulation per specific task, which could be used as normative patterns for the global population. Reaching has been observed to require the modulation of closeness, digit arch and thumb opposition synergies, with different control patterns per grasp. All the synergies are very important during manipulation and need to be modulated for all the tasks. Finally, groups of tasks with similar kinematic requirements in terms of synergies have been identified, which could benefit the selection of tasks for rehabilitation and hand function assessments.