Utilising redundancy in musculoskeletal systems for adaptive stiffness and muscle failure compensation: a model-free inverse statics approach.

Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control. This difficulty arises from the need for comprehensive knowledge of the musculoskeletal system, including details such as muscle arrangement, and fully accessible muscle and joint states. Whilst existing model-free methods do not need explicit formulations, they also underutilise the benefits of muscle redundancy. Consequently, they necessitate retraining in the event of muscle failure and require manual tuning of parameters to control joint stiffness limiting their applications under unknown payloads. Presented here is a model-free local inverse statics controller for musculoskeletal systems, employing a feedforward neural network trained on motor babbling data. Experiments with a musculoskeletal leg model showcase the controller's adaptability to complex structures, including mono and bi-articulate muscles. The controller can compensate for changes such as weight variations, muscle failures, and environmental interactions, retaining reasonable accuracy without the need for any additional retraining. .

Integrated Quantitative Evaluation of Spatial Cognition and Motor Function with HoloLens Mixed Reality.

The steady increase in the aging population worldwide is expected to cause a shortage of doctors and therapists for older people. This demographic shift requires more efficient and automated systems for rehabilitation and physical ability evaluations. Rehabilitation using mixed reality (MR) technology has attracted much attention in recent years. MR displays virtual objects on a head-mounted see-through display that overlies the user's field of vision and allows users to manipulate them as if they exist in reality. However, tasks in previous studies applying MR to rehabilitation have been limited to tasks in which the virtual objects are static and do not interact dynamically with the surrounding environment. Therefore, in this study, we developed an application to evaluate cognitive and motor functions with the aim of realizing a rehabilitation system that is dynamic and has interaction with the surrounding environment using MR technology. The developed application enabled effective evaluation of the user's spatial cognitive ability, task skillfulness, motor function, and decision-making ability. The results indicate the usefulness and feasibility of MR technology to quantify motor function and spatial cognition both for static and dynamic tasks in rehabilitation.

Identifying essential factors for energy-efficient walking control across a wide range of velocities in reflex-based musculoskeletal systems.

Humans can generate and sustain a wide range of walking velocities while optimizing their energy efficiency. Understanding the intricate mechanisms governing human walking will contribute to the engineering applications such as energy-efficient biped robots and walking assistive devices. Reflex-based control mechanisms, which generate motor patterns in response to sensory feedback, have shown promise in generating human-like walking in musculoskeletal models. However, the precise regulation of velocity remains a major challenge. This limitation makes it difficult to identify the essential reflex circuits for energy-efficient walking. To explore the reflex control mechanism and gain a better understanding of its energy-efficient maintenance mechanism, we extend the reflex-based control system to enable controlled walking velocities based on target speeds. We developed a novel performance-weighted least squares (PWLS) method to design a parameter modulator that optimizes walking efficiency while maintaining target velocity for the reflex-based bipedal system. We have successfully generated walking gaits from 0.7 to 1.6 m/s in a two-dimensional musculoskeletal model based on an input target velocity in the simulation environment. Our detailed analysis of the parameter modulator in a reflex-based system revealed two key reflex circuits that have a significant impact on energy efficiency. Furthermore, this finding was confirmed to be not influenced by setting parameters, i.e., leg length, sensory time delay, and weight coefficients in the objective cost function. These findings provide a powerful tool for exploring the neural bases of locomotion control while shedding light on the intricate mechanisms underlying human walking and hold significant potential for practical engineering applications.

Latent Representation-Based Learning Controller for Pneumatic and Hydraulic Dual Actuation of Pressure-Driven Soft Actuators.

The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.

Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice.

The use of neurofeedback is an important aspect of effective motor rehabilitation as it offers real-time sensory information to promote neuroplasticity. However, there is still limited knowledge about how the brain's functional networks reorganize in response to such feedback. To address this gap, this study investigates the reorganization of the brain network during motor imagery tasks when subject to visual stimulation or visual-electrotactile stimulation feedback. This study can provide healthcare professionals with a deeper understanding of the changes in the brain network and help develop successful treatment approaches for brain-computer interface-based motor rehabilitation applications. We examine individual edges, nodes, and the entire network, and use the minimum spanning tree algorithm to construct a brain network representation using a functional connectivity matrix. Furthermore, graph analysis is used to detect significant features in the brain network that might arise in response to the feedback. Additionally, we investigate the power distribution of brain activation patterns using power spectral analysis and evaluate the motor imagery performance based on the classification accuracy. The results showed that the visual and visual-electrotactile stimulation feedback induced subject-specific changes in brain activation patterns and network reorganization in the [Formula: see text] band. Thus, the visual-electrotactile stimulation feedback significantly improved the integration of information flow between brain regions associated with motor-related commands and higher-level cognitive functions, while reducing cognitive workload in the sensory areas of the brain and promoting positive emotions. Despite these promising results, neither neurofeedback modality resulted in a significant improvement in classification accuracy, compared with the absence of feedback. These findings indicate that multimodal neurofeedback can modulate imagery-mediated rehabilitation by enhancing motor-cognitive communication and reducing cognitive effort. In future interventions, incorporating this technique to ease cognitive demands for participants could be crucial for maintaining their motivation to engage in rehabilitation.

Transhumeral Arm Reaching Motion Prediction through Deep Reinforcement Learning-Based Synthetic Motion Cloning.

The lack of intuitive controllability remains a primary challenge in enabling transhumeral amputees to control a prosthesis for arm reaching with residual limb kinematics. Recent advancements in prosthetic arm control have focused on leveraging the predictive capabilities of artificial neural networks (ANNs) to automate elbow joint motion and wrist pronation-supination during target reaching tasks. However, large quantities of human motion data collected from different subjects for various activities of daily living (ADL) tasks are required to train these ANNs. For example, the reaching motion can be altered when the height of the desk is changed; however, it is cumbersome to conduct human experiments for all conditions. This paper proposes a framework for cloning motion datasets using deep reinforcement learning (DRL) to cater to training data requirements. DRL algorithms have been demonstrated to create human-like synergistic motion in humanoid agents to handle redundancy and optimize movements. In our study, we collected real motion data from six individuals performing multi-directional arm reaching tasks in the horizontal plane. We generated synthetic motion data that mimicked similar arm reaching tasks by utilizing a physics simulation and DRL-based arm manipulation. We then trained a CNN-LSTM network with different configurations of training motion data, including DRL, real, and hybrid datasets, to test the efficacy of the cloned motion data. The results of our evaluation showcase the effectiveness of the cloned motion data in training the ANN to predict natural elbow motion accurately across multiple subjects. Furthermore, motion data augmentation through combining real and cloned motion datasets has demonstrated the enhanced robustness of the ANN by supplementing and diversifying the limited training data. These findings have significant implications for creating synthetic dataset resources for various arm movements and fostering strategies for automatized prosthetic elbow motion.

Self-organizing neural network for reproducing human postural mode alternation through deep reinforcement learning.

A self-organized phenomenon in postural coordination is essential for understanding the auto-switching mechanism of in-phase and anti-phase postural coordination modes during standing and related supra-postural activities. Previously, a model-based approach was proposed to reproduce such self-organized phenomenon. However, if we set this problem including the process of how we establish the internal predictive model in our central nervous system, the learning process is critical to be considered for establishing a neural network for managing adaptive postural control. Particularly when body characteristics may change due to growth or aging or are initially unknown for infants, a learning capability can improve the hyper-adaptivity of human motor control for maintaining postural stability and saving energy in daily living. This study attempted to generate a self-organizing neural network that can adaptively coordinate the postural mode without assuming a prior body model regarding body dynamics and kinematics. Postural coordination modes are reproduced in head-target tracking tasks through a deep reinforcement learning algorithm. The transitions between the postural coordination types, i.e. in-phase and anti-phase coordination modes, could be reproduced by changing the task condition of the head tracking target, by changing the frequencies of the moving target. These modes are considered emergent phenomena existing in human head tracking tasks. Various evaluation indices, such as correlation, and relative phase of hip and ankle joint, are analyzed to verify the self-organizing neural network performance to produce the postural coordination transition between the in-phase and anti-phase modes. In addition, after learning, the neural network can also adapt to continuous task condition changes and even to unlearned body mass conditions keeping consistent in-phase and anti-phase mode alternation.

Sparse identification of Lagrangian for nonlinear dynamical systems via proximal gradient method.

The autonomous distillation of physical laws only from data is of great interest in many scientific fields. Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. However, SINDy faces certain difficulties when the dynamics contain rational functions. The Lagrangian is substantially more concise than the actual equations of motion, especially for complex systems, and it does not usually contain rational functions for mechanical systems. Few proposed methods proposed to date, such as Lagrangian-SINDy we have proposed recently, can extract the true form of the Lagrangian of dynamical systems from data; however, these methods are easily affected by noise as a fact. In this study, we developed an extended version of Lagrangian-SINDy (xL-SINDy) to obtain the Lagrangian of dynamical systems from noisy measurement data. We incorporated the concept of SINDy and used the proximal gradient method to obtain sparse Lagrangian expressions. Further, we demonstrated the effectiveness of xL-SINDy against different noise levels using four mechanical systems. In addition, we compared its performance with SINDy-PI (parallel, implicit) which is a latest robust variant of SINDy that can handle implicit dynamics and rational nonlinearities. The experimental results reveal that xL-SINDy is much more robust than the existing methods for extracting the governing equations of nonlinear mechanical systems from data with noise. We believe this contribution is significant toward noise-tolerant computational method for explicit dynamics law extraction from data.

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.

EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network.

One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average R2 scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained.

Ground Reaction Force and Moment Estimation through EMG Sensing Using Long Short-Term Memory Network during Posture Coordination.

Motion prediction based on kinematic information such as body segment displacement and joint angle has been widely studied. Because motions originate from forces, it is beneficial to estimate dynamic information, such as the ground reaction force (GRF), in addition to kinematic information for advanced motion prediction. In this study, we proposed a method to estimate GRF and ground reaction moment (GRM) from electromyography (EMG) in combination with and without an inertial measurement unit (IMU) sensor using a machine learning technique. A long short-term memory network, which is suitable for processing long time-span data, was constructed with EMG and IMU as input data to estimate GRF during posture control and stepping motion. The results demonstrate that the proposed method can provide the GRF estimation with a root mean square error (RMSE) of 8.22 ± 0.97% (mean ± SE) for the posture control motion and 11.17 ± 2.16% (mean ± SE) for the stepping motion. We could confirm that EMG input is essential especially when we need to predict both GRF and GRM with limited numbers of sensors attached under knees. In addition, we developed a GRF visualization system integrated with ongoing motion in a Unity environment. This system enabled the visualization of the GRF vector in 3-dimensional space and provides predictive motion direction based on the estimated GRF, which can be useful for human motion prediction with portable sensors.

Soft-body dynamics induces energy efficiency in undulatory swimming: A deep learning study.

Recently, soft robotics has gained considerable attention as it promises numerous applications thanks to unique features originating from the physical compliance of the robots. Biomimetic underwater robots are a promising application in soft robotics and are expected to achieve efficient swimming comparable to the real aquatic life in nature. However, the energy efficiency of soft robots of this type has not gained much attention and has been fully investigated previously. This paper presents a comparative study to verify the effect of soft-body dynamics on energy efficiency in underwater locomotion by comparing the swimming of soft and rigid snake robots. These robots have the same motor capacity, mass, and body dimensions while maintaining the same actuation degrees of freedom. Different gait patterns are explored using a controller based on grid search and the deep reinforcement learning controller to cover the large solution space for the actuation space. The quantitative analysis of the energy consumption of these gaits indicates that the soft snake robot consumed less energy to reach the same velocity as the rigid snake robot. When the robots swim at the same average velocity of 0.024 m/s, the required power for the soft-body robot is reduced by 80.4% compared to the rigid counterpart. The present study is expected to contribute to promoting a new research direction to emphasize the energy efficiency advantage of soft-body dynamics in robot design.

A Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots.

The state-of-the-art reinforcement learning (RL) techniques have made innumerable advancements in robot control, especially in combination with deep neural networks (DNNs), known as deep reinforcement learning (DRL). In this article, instead of reviewing the theoretical studies on RL, which were almost fully completed several decades ago, we summarize some state-of-the-art techniques added to commonly used RL frameworks for robot control. We mainly review bioinspired robots (BIRs) because they can learn to locomote or produce natural behaviors similar to animals and humans. With the ultimate goal of practical applications in real world, we further narrow our review scope to techniques that could aid in sim-to-real transfer. We categorized these techniques into four groups: 1) use of accurate simulators; 2) use of kinematic and dynamic models; 3) use of hierarchical and distributed controllers; and 4) use of demonstrations. The purposes of these four groups of techniques are to supply general and accurate environments for RL training, improve sampling efficiency, divide and conquer complex motion tasks and redundant robot structures, and acquire natural skills. We found that, by synthetically using these techniques, it is possible to deploy RL on physical BIRs in actuality.

Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks.

Introduction: Emerging deep learning approaches to decode motor imagery (MI) tasks have significantly boosted the performance of brain-computer interfaces. Although recent studies have produced satisfactory results in decoding MI tasks of different body parts, the classification of such tasks within the same limb remains challenging due to the activation of overlapping brain regions. A single deep learning model may be insufficient to effectively learn discriminative features among tasks. Methods: The present study proposes a framework to enhance the decoding of multiple hand-MI tasks from the same limb using a multi-branch convolutional neural network. The CNN framework utilizes feature extractors from established deep learning models, as well as contrastive representation learning, to derive meaningful feature representations for classification. Results: The experimental results suggest that the proposed method outperforms several state-of-the-art methods by obtaining a classification accuracy of 62.98% with six MI classes and 76.15 % with four MI classes on the Tohoku University MI-BCI and BCI Competition IV datasets IIa, respectively. Discussion: Despite requiring heavy data augmentation and multiple optimization steps, resulting in a relatively long training time, this scheme is still suitable for online use. However, the trade-of between the number of base learners, training time, prediction time, and system performance should be carefully considered.

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.

Systematic Motion Integration with Multiple Depth Cameras Allowing Sensor Movement for Stable Skeleton Tracking.

In recent years, markerless motion capture using a depth camera or RGB camera without any restriction on the subject has been attracting attention. Especially, depth cameras such as Kinect and RealSense allow instantaneous motion capture even at home outside lab environment, which is attractive for rehabilitation usage. However, single depth camera can capture steadily skeleton only when the subject stands facing to camera for the limited range, thus it is hard to apply to track skeletons while walking. Multiple depth cameras setting may allow to expand the range, but it can involve non-practical calibration process and can affect instantaneous capture advantage of depth camera. In this study, we propose a systematic method to integrate the motion information of skeletal models obtained from multiple depth cameras. The proposed method can perform a quick calibration using skeletal models instead of external reference objects, and estimate the spatial relationship of the sensors that allows the depth camera to move. The result demonstrates stable skeleton tracking free from occlusion problem keeping instantaneous capture capability of depth cameras.

Game-based Evaluation of Whole-body Movement Functions with CoM Stability and Motion Smoothness.

In the field of rehabilitation, there is a great demand for an automatic and quantitative evaluation system. The balance ability is an essential factor for motor function evaluation related to posture control. Although balance ability is assessed using various indices in current clinical situations, most of previous studies developing an automatic evaluation system have used only a single particular index for balance evaluation. In this study, we developed a system that evaluates whole-body motor function using multiple indices based on the trajectory of the center of mass (CoM) and the motion smoothness. The system is inexpensive and little physical burden because the evaluation indices are calculated from the skeleton tracked by Kinect in a game environment. We attempt to capture the differences in individual motor functions which are difficult to be detected by qualitative visual observation.

Classification of Human Balance Recovery Strategies Through Kinematic Motor Synergy Analysis.

A key problem in human balance recovery lies in understanding the mechanism of balance behavior with redundant bio-mechanical motors. Motor synergy has been known as an efficient tool to analyze characteristics of motion behavior and reconstruct control command. In this paper, motor synergy analysis for different control strategies is proposed to analyze different balance motion coordination for various levels of pushing force, and understand the coordination of human multiple joints regarding balance recovery. The spatial synergy of specific joint angles for different pushing force levels exerted on the subject's back is computed with the principal component analysis (PCA) to evaluate the adaptive balance motion response patterns and illustrate the improvement of balance robustness through the switch of joint coordination. Therefore, the switch of postural coordination over multiple joints in balance recovery movements was analyzed to better understand the mechanism of balance strategy generation in this study.

Temporal Variation Quantification During Cognitive Dual-Task Gait Using Two IMU Sensors.

Multiple tasks are simultaneously performed during walking in our daily life. Distracted walk by smartphone usage is recently getting a social problem. The term dual-task gait refers to the secondary task added to the walking. Attention demanding tasks may influence how a person walks. Since in-lab measurement may not accurately reflect the daily living gait, wearable sensors approach have been proposed for gait analysis in an out-of-lab setting. This study addresses the potential of using only two inertial measurement units (IMUs) attached to the shoes for the assessment of cognitive dual-task gait and how it differs from single-task gait. We found that the proposed system is sensitive to recognizing a tiny change in gait features such as on the double support time and gait indices when subject performing dual-task gait compared to the single-task gait experiment.

Quantifying Motor and Cognitive Function of the Upper Limb Using Mixed Reality Smartglasses.

In our aging world, the need to measure and evaluate motor and cognitive functions and to automate physical and occupational therapy will increase in the future. Many studies on VR-based rehabilitation systems are already underway. However, there are some issues such as the risk of falling or crashing due to the complete blockage of visual information, VR sickness, and lack of reality. The purpose of this research is to develop a system that simultaneously measures and evaluates multiple abilities and functions, such as motor function, cognitive function, and prediction ability, by using mixed reality (MR) smartglasses technology that enables interaction with spatially arranged objects while maintaining real-world information. In this study, we focused on the motor function of the upper limbs and cognitive function, and measured finger and gaze movements during a reaching task. In addition, we developed a game-based task for occupational therapy in a MR environment and reported the results.