The concepts of muscle activity generation driven by upper limb kinematics

BACKGROUND

The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.

METHODS

We develop a novel approach utilizing recurrent neural networks that are able to predict muscle activity of the upper limbs associated with complex 3D human arm motions. Therefore, motion parameters such as joint angle, velocity, acceleration, hand position, and orientation, serve as input for the models. In addition, these models are trained on multiple subjects (n=5 including , 3 male in the age of 26±2 years) and thus can generalize across individuals. In particular, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific fine-tuned model using a transfer learning approach to adapt the model to a new subject. Estimators such as mean square error MSE, correlation coefficient r, and coefficient of determination R² are used to evaluate the goodness of fit. We additionally assess performance by developing a new score called the zero-line score. The present approach was compared with multiple other architectures.

RESULTS

The presented approach predicts the muscle activity for previously through different subjects with remarkable high precision and generalizing nicely for new motions that have not been trained before. In an exhausting comparison, our recurrent network outperformed all other architectures. In addition, the high inter-subject variation of the recorded muscle activity was successfully handled using a transfer learning approach, resulting in a good fit for the muscle activity for a new subject.

CONCLUSIONS

The ability of this approach to efficiently predict muscle activity contributes to the fundamental understanding of motion control. Furthermore, this approach has great potential for use in rehabilitation contexts, both as a therapeutic approach and as an assistive device. The predicted muscle activity can be utilized to guide functional electrical stimulation, allowing specific muscles to be targeted and potentially improving overall rehabilitation outcomes.

Smart Sports Outward Bound Training Assistant System Based on WSNs

The outward-bound training has been a popular manner to exercise in daily life. However, there lacks an intelligent assistant system to monitor the real-time status of users to avoid accidents during training. In order to fill this gap, this paper established an intelligent system to monitor fatigue status during outward-bound training by using surface electromyography (sEMG) signals. The system consists of three parts: a wearable device, edge node, and cloud server. First, the wearable device collects sEMG signals. Second, the edge node processes the collected sEMG signals and sends the sEMG signal features to the cloud server. Finally, the cloud server returns the results to edge node according to a stored classification model that learnt from massive historical sEMG signals. The experimental results show the effectiveness of the proposed system.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

Towards understanding and synthesis of contact-rich anthropomorphic motions through interactive cyber-physical human

This article presents perspective on the research challenge of understanding and synthesizing anthropomorphic whole-body contact motions through a platform called “interactive cyber-physical human (iCPH)” for data collection and augmentation. The iCPH platform combines humanoid robots as “physical twins” of human and “digital twins” that simulates humans and robots in cyber-space. Several critical research topics are introduced to address this challenge by leveraging the advanced model-based analysis together with data-driven learning to exploit collected data from the integrated platform of iCPH. Definition of general description is identified as the first topic as a common basis of contact motions compatible to both humans and humanoids. Then, we set continual learning of a feasible contact motion network as the second challenge by benefiting from model-based approach and machine learning bridged by the efficient analytical gradient computation developed by the author and his collaborators. The final target is to establish a high-level symbolic system allowing automatic understanding and generation of contact motions in unexperienced environments. The proposed approaches are still under investigation, and the author expects that this article triggers discussions and further collaborations from different research communities, including robotics, artificial intelligence, neuroscience, and biomechanics.

Muscle Activity Estimation at Drop Vertical Jump Landing Using Passive Muscle Mechanical Model

Among the various elements that facilitate the movement of the lower limbs, the anterior cruciate ligament (ACL) is prone to injury. An adequate joint control of the lower limb can prevent ACL injury. Balancing activities between the agonist and the antagonist muscles is vital for joint control. However, prior studies on muscle activities were limited since they could not determine passive muscle activities. In this study, we develop a muscle model considering the passive properties to analyze the movement mechanism of the ACL under heavy loads, such as those produced during jump landing. We estimated the muscle activities occurring during a drop vertical jump (DVJ) by applying to the proposed method the physiological constraint that muscle activities are constant during a short time around landing. In addition, the knee joint torque and muscle forces were calculated from the estimated muscle activities, which were thereafter compared with those obtained using the conventional method. The results revealed that this passive muscle model appropriately represented the knee joint torque at DVJ landing by decreasing the passive muscle strain and increasing the isometric maximum muscle force. Moreover, the estimated muscle activities were larger than those obtained using the conventional method, which may be caused by the co-contraction between agonist and antagonist muscles that cannot be represented by the conventional method. This muscle co-contraction estimation algorithm would estimate the muscle load under heavy loads, and applying this knowledge to training would help to prevent ACL injuries.

DATSURYOKU Sensor-A Capacitive-Sensor-Based Belt for Predicting Muscle Tension: Preliminary Results

Excessive muscle tension is implicitly caused by inactivity or tension in daily activities, and it results in increased joint stiffness and vibration, and thus, poor performance, failure, and injury in sports. Therefore, the routine measurement of muscle tension is important. However, a co-contraction observed in excessive muscle tension cannot be easily detected because it does not appear in motion owing to the counteracting muscle tension, and it cannot be measured by conventional motion capture systems. Therefore, we focused on the physiological characteristics of muscle, that is, the increase in muscle belly cross-sectional area during activity and softening during relaxation. Furthermore, we measured muscle tension, especially co-contraction and relaxation, using a DATSURYOKU sensor, which measures the circumference of the applied part. The experiments showed high interclass correlation between muscle activities and circumference across maximal voluntary co-contractions of the thigh muscles and squats. Moreover, the circumference sensor can measure passive muscle deformation that does not appear in muscle activities. Therefore, the DATSURYOKU sensor showed the potential to routinely measure muscle tension and relaxation, thus avoiding the risk of failure and injury owing to excessive muscle tension and can contribute to the realization of preemptive medicine by measuring daily changes.

Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire

In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat's head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE's ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis.

Muscles Cooperation Analysis Using Akaike Information Criteria for Anterior Cruciate Ligament Injury Prevention

In this paper, we propose the analysis method for finding out the similarity of the muscle force patterns to mine the risk factor of the anterior cruciate ligament (ACL) injury. Akaike information criteria (AIC) under the assumption of the auto-regression model is adapted to analyze the similarities of muscle force patterns in time-series. The difference of AIC values between 2 muscles is considered to be the distance between 2 muscle force patterns and the dexterity of the maneuver is expected to be discussed. We measured drop vertical jump (DVJ) and use the data around the contact timing of whom hadn't had ACL injury experiments. The results showed that we could successfully calculate AIC distance according to the similarity of the time-series data pattern and it can be useful to discuss one's dexterity of controlling body maneuvers soon after contact timing of DVJ motion.

Estimation of Muscle Activity Change under Different Bolus Conditions using Musculoskeletal Model of Swallowing

Swallowing, deglutition, is realized by highly coordinated activities of many nerves and muscles, but it is hard to observe directly due to intracorporal movement, and there is a limitation to the number of muscles that can be percutaneously measured. In addition, since there are few studies on the mechanical analysis of the swallowing movement, the detailed muscle activity pattern during swallowing has not yet been clarified. To tackle this problem from the viewpoint of biomechanics, we have been developing the musculoskeletal model of swallowing which can estimate the activities of swallowing-related muscles based on the movements of hyoid bone and thyroid cartilage. In this paper, we analyzed the activities of swallowing-related muscles under two different bolus conditions: bolus of water and nectar thickened liquid to investigate the effect of physical property of bolus in the activities of swallowing-related muscles.

Bicycle exercise training improves ambulation in patients with peripheral artery disease

OBJECTIVE

Exercise training has multiple beneficial effects in patients with arteriosclerotic diseases; however, the exact underlying mechanisms of the effects are not completely understood. This study aimed to evaluate the effectiveness of a supervised exercise program in improving gait parameters, including the variability and walking performance of lower limb movements, in patients with peripheral artery disease (PAD) and intermittent claudication (IC).

METHODS

Sixteen patients with a history of PAD and IC were recruited for this study, and they completed a 3-month supervised bicycle exercise program. The ankle-brachial index and responses to quality of life (QOL) questionnaires were evaluated. Near-infrared spectroscopy was also performed to determine the hemoglobin oxygen saturation in the calf. Patients' kinematics and dynamics, including joint range of motion and muscle tension, were evaluated using an optical motion capture system. Computed tomography images of each muscle were assessed by manual outlining. Data were collected before and after the supervised bicycle exercise program, and differences were analyzed.

RESULTS

Significant differences were not found in step length, ankle-brachial index, and hemoglobin oxygen saturation before and after the supervised bicycle exercise program; however, IC distance (P = .034), maximum walking distance (P = .006), and all QOL questionnaire scores (P < .001) showed significant improvement. Hip range of motion (P = .035), maximum hip joint torque (right, P = .031; left, P = .044), maximum tension of the gluteus maximus muscle (right, P = .044; left, P = .042), and maximum hip joint work (right, P = .048; left, P = .043) also significantly decreased bilaterally. Computed tomography images showed a significant increase in the cross-sectional area of the abdominal, trunk, and thigh muscles but not in that of the lower leg muscles after the supervised exercise program intervention.

CONCLUSIONS

In this study, bicycle exercise training improved the QOL and walking distance and decreased hip movement. The results showed that bicycling might be as useful as walking in patients with PAD.

Musculoskeletal design, control, and application of human mimetic humanoid Kenshiro

We have been developing a human mimetic musculoskeletal humanoid called Kenshiro, whose design concept is to thoroughly pursue an unprecedented anatomical fidelity to the human musculoskeletal structure. We believe that research on human mimetic musculoskeletal humanoids advances our understanding of humans and expands the applications of humanoids-such as a human body simulator that can quantitatively analyze internal human motion data. This paper describes Kenshiro's musculoskeletal body characteristics, software system, and preliminary experiments explaining the concept of potential application.

Linear Logistic Regression for Estimation of Lower Limb Muscle Activations

This paper addresses a technique to estimate the muscle activity from the movement data. Statistical models, such as linear regression (LR) models and artificial neural networks (ANNs), are good candidate estimation techniques. Although an ANN has a high estimation capability, it is frequently in the clinical application that a very small amount of data leads to performance deterioration. Conversely, an LR model needs fewer data, while its generalization performance is limited. In this paper, therefore, a muscle activity estimation method is proposed that uses a linear logistic regression model to improve the generalization performance. The proposed method was compared with an LR model and an ANN in verification experiments with several different conditions. The results suggest that the proposed method has a higher generalization performance than the conventional methods.

Comprehensive theory of differential kinematics and dynamics towards extensive motion optimization framework

This paper presents a novel unified theoretical framework for differential kinematics and dynamics for the optimization of complex robot motion. By introducing an 18×18 comprehensive motion transformation matrix, the forward differential kinematics and dynamics, including velocity and acceleration, can be written in a simple chain product similar to an ordinary rotational matrix. This formulation enables the analytical computation of derivatives of various physical quantities (e.g. link velocities, link accelerations, or joint torques) with respect to joint coordinates, velocities and accelerations for a robot trajectory in an efficient manner ([Formula: see text], where [Formula: see text] is the number of the robot’s degree of freedom), which is useful for motion optimization. Practical implementation of gradient computation is demonstrated together with simulation results of robot motion optimization to validate the effectiveness of the proposed framework.

Evaluation of Compensatory Movement by Shoulder Joint Torque during Gain Adjustment of a Powered Prosthetic Wrist Joint

Powered prostheses with low degree of freedom (DoF) have been developed for people with disabilities to assist daily tasks. These prostheses neglect the user's compensatory movements caused by the low degree of freedom. We assume that the movements can be reduced by well-designed controller of the devices. This paper explores an optimal control gain of the powered prosthesis to prevent the user from compensatory movements through experiments. In the experiments, we developed 1-DoF hand prosthesis with a position-controlled servo, which includes the constant gain as a feed-forward term. The compensatory movements are regarded as a joint torque at a shoulder (abduction/adduction). 4 intact subjects performed a pick-and-place task, using the prosthesis with several control gains. The empirical results show that there was the optimal gain for each subject, which reduces their compensatory movement.

Estimating Lumbar Load During Motion with an Unknown External Load Based on Back Muscle Activity Measured with a Muscle Stiffness Sensor

A forward bending motion is essential in everyday tasks, such as carrying objects, shoveling snow, and performing farm work. However, many people suffer from lumbar pain resulting from forward bending motion, which causes a lumbar disc load owing to the changing of the lumbar shape. We have developed a wearable lumbar load estimation system, which measures the skin shape on the back using a curvature sensor. Because the lumbar load varies with the external load, the lumbar load should be estimated based on the external load. Therefore, we have developed a method for estimating an unknown external force using a muscle stiffness sensor. Muscle strength can be estimated by measuring the muscle hardness from the surface, and the relationship between the external force and the muscle force can be modeled. Using this method, we estimate the dependence of the lumbar load on external forces in real time. In addition, we simplify the calculation by converting the external load into a load resulting from a person’s own weight. We incorporate the proposed method into our wearable sensor system, estimate the lumbar load, and compare this with the results of a musculoskeletal dynamics simulation.

Evaluation Framework for Passive Assistive Device Based on Humanoid Experiments

This study presents an enhanced framework for evaluating an assistive effect generated by a passive assistive device using a humanoid robot. The humanoid robotic experiments can evaluate wearable devices by measuring the joint torque, which cannot be measured directly from the human body. In this paper, we introduce an “assistive torque estimation map” as an efficient means for estimating the supportive torque within the range of motions by interpolating the measured joint torques and joint angles of the robot. This map aims to estimate the supportive torques for complex motions without conducting humanoid experiments or human-subject experiments with these motions. We generated an estimation map for an actual assistive suit that decreases the load on the lumbar region and we verified the validity of the proposed method by experimentation. In addition, the geometric simulation model of the assistive suit was validated based on the proposed experiments by using the humanoid robot HRP-4. The proposed framework is expected to lead to an efficient design of such assistive devices so that fewer human-subject experiments need to be conducted.

Risk estimation for intervertebral disc pressure through musculoskeletal joint reaction force simulation

This research proposes a novel method that evaluates joint reaction forces by motion analysis using a musculoskeletal model. While general muscle tension estimations minimize the sum of the muscle tensions, the proposed method utilizes the joint reaction forces themselves in the objective function of the optimization problem in addition to conventional method. This method can estimate a pattern of the muscle tensions that maximizes or minimizes a specific joint force. As a typical outcome, the proposed method allows evaluating intervertebral disc compressive force caused by co-contraction of muscles while avoiding risk underestimation. We analyzed the actual lifting motion as an example and confirmed that the method can estimate the muscle tension distribution under different tension conditions.

Simulation-based design for robotic care device: Optimizing trajectory of transfer support robot

This paper presents a framework of simulation-based design for robotic care devices developed to reduce the burden of caregiver and care receivers. First, physical interaction between the user and device is quantitatively estimated by using a digital human simulator. Then we introduce a method for optimizing the design parameters according to given evaluation criteria. An example of trajectory optimization of transfer support robot is provided to demonstrate the effectiveness of the proposed method.

Gait training assist system of a lower limb prosthetic visualizing muscle activation pattern using a color-depth sensor

Some unilateral lower-limb amputees load the intact limb more than the prosthetic limb. This can cause chronic pains, fatigue, lumbago, and joint diseases, including knee osteoarthritis. To avoid and counteract these symptoms it is necessary to improve their asymmetric gait. Increasing the function of the hip abductor muscle is important to maintaining symmetrical weight distribution. Therefore, the purpose of this study is to develop a training assist system, which estimates and visualizes an abductor muscle by using a color-depth sensor. To estimate the muscle activation, first, the floor reaction force is calculated using a simple dynamic model. Then, the hip torque is calculated using joint angles. The floor reaction force and, the muscle length are calculated based on a human musculoskeletal model. Muscle activity is estimated by these parameters. Evaluation experiments of this proposed method were performed on healthy persons and unilateral trans femoral amputees, and the effectiveness of this proposed algorithm has been confirmed.

Inverse estimation of multiple muscle activations based on linear logistic regression

This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.

Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model

Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem (OCP) is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution toward bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation.

Forward dynamics simulation of human figures on assistive devices using geometric skin deformation model

We present a forward dynamics (FD) simulation technique for human figures when they are supported by assistive devices. By incorporating a geometric skin deformation model, called linear blend skinning (skinning), into rigid-body skeleton dynamics, we can model a time-varying geometry of body surface plausibly and efficiently. Based on the skinning model, we also derive a Jacobian (a linear mapping) that maps contact forces exerted on the skin to joint torques, which is the main technical contribution of this paper. This algorithm allows us to efficiently simulate dynamics of human body that interacts with assistive devices. Experimental results showed that the proposed approach can generate plausible motions and can estimate pressure distribution that is roughly comparable to the tactile sensor data.

How does the brain solve muscle redundancy? Filling the gap between optimization and muscle synergy hypotheses

The question of how the central nervous system coordinates redundant muscles has been a long-standing problem in motor neuroscience. The optimization hypothesis posits that the brain can select the muscle activation pattern that minimizes the motor effort cost from among many solutions that satisfy the requirements of the task. On the other hand, the muscle-synergy hypothesis proposes that neurally established functional groupings of muscles alleviate the computational burden associated with motor control and learning. Although the two hypotheses are not mutually exclusive, the relationship between them has not been well analyzed. This is probably because both hypotheses are formulated mathematically without a clear concept of their neural implementation. Here, we introduce a biologically plausible hypothesis ("the forgetting hypothesis") for how optimization is realized by a population of neurons. We further demonstrate that low-dimensional structure can be detected in an optimal network even if no muscle-synergies are explicitly assumed. Finally, we briefly discuss an inherent difficulty in testing the muscle-synergy hypothesis, which arises when population level optimization is assumed.

Modeling and Identification of a Realistic Spiking Neural Network and Musculoskeletal Model of the Human Arm, and an Application to the Stretch Reflex

This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. The proposed model was able to track the recorded data and produce dynamically consistent neural spiking patterns, muscle forces and movement kinematics under varying conditions of external forces and co-contraction levels. This additional layer of detail in neuromuscular models has important relevance to the research communities of rehabilitation and clinical movement analysis by providing a mathematical approach to studying neuromuscular pathology.

Relationship between the peak time of hamstring stretch and activation during sprinting

The purpose of this study was to investigate the time series relationships between the peak musculotendon length and electromyography (EMG) activation during overground sprinting to clarify the risk of muscle strain injury incidence in each hamstring muscle. Full-body kinematics and EMG of the right biceps femoris long head (BFlh) and semitendinosus (ST) muscles were recorded in 13 male sprinters during overground sprinting at maximum effort. The hamstring musculotendon lengths during sprinting were computed using a three-dimensional musculoskeletal model. The time of the peak musculotendon length, in terms of the percentage of the running gait cycle, was measured and compared with that of the peak EMG activity. The maximum length of the hamstring muscles was noted during the late swing phase of sprinting. The peak musculotendon length was synchronous with the peak EMG activation in the BFlh muscle, while the time of peak musculotendon length in the ST muscle occurred significantly later than the peak level of EMG activation (p < 0.05). These results suggest that the BFlh muscle is exposed to an instantaneous high tensile force during the late swing phase of sprinting, indicating a higher risk for muscle strain injury.

Mechanics of the muscles crossing the hip joint during sprint running

We aimed to demonstrate the changes over time in the lengths and forces of the muscles crossing the hip joint during overground sprinting and investigate the relationships between muscle lengths and muscle-tendon unit forces - particularly peak biceps femoris force. We obtained three-dimensional kinematics during 1 running cycle from 8 healthy sprinters sprinting at maximum speed. Muscle lengths and muscle-tendon unit forces were calculated for the iliacus, rectus femoris, gluteus maximus, and biceps femoris muscles of the target leg as well as the contralateral iliacus and rectus femoris. Our results showed that during sprinting, the muscles crossing the hip joint demonstrate a stretch-shortening cycle and 1 or 2 peak forces. The timing of peak biceps femoris force, expressed as a percentage of the running cycle (mean [SD], 80.5 [2.9]%), was synchronous with those of the maximum biceps femoris length (82.8 [1.9]%) and peak forces of the gluteus maximus (83.8 [9.1]%), iliacus (81.1 [5.2]%), and contralateral iliacus (78.5 [5.8]%) and also that of the peak pelvic anterior tilt. The force of the biceps femoris appeared to be influenced by the actions of the muscles crossing the hip joint as well as by the pelvic anterior tilt.

Identifiability and identification of inertial parameters using the underactuated base-link dynamics for legged multibody systems

In this paper we study the dynamics of multibody systems with the base not permanently fixed to the inertial frame, or more specifically legged systems such as humanoid robots and humans. The issue is to be approached in terms of the identification theory developed in the field of robotics. The under-actuated base-link which characterizes the dynamics of legged systems is the focus of this work. The useful mechanical feature to analyze the dynamics of legged system is proven: the set of inertial parameters appearing in the equation of motion of the under-actuated base is equivalent to the set in the equations of the whole body. In particular, when no external force acts on the system, all of the parameters in the set except the total mass are generally identifiable only from the observation of the free-flying motion. We also propose a method to identify the inertial parameters based on the dynamics of the under-actuated base. The method does not require the measurement of the joint torques. Neither the joint frictions nor the actuator dynamics need to be considered. Even when the system has no external reaction force, the method is still applicable. The method has been tested on both a humanoid robot and a human, and the experimental results are shown.

A neuromusculoskeletal model of the human lower limb: towards EMG-driven actuation of multiple joints in powered orthoses

This paper presents a novel neuromusculoskeletal (NMS) model of the human lower limb that uses the electromyo-graphic (EMG) signals from 16 muscles to estimate forces generated by 34 musculotendon actuators and the resulting joint moments at the hip, knee and ankle joints during varied contractile conditions. Our proposed methodology allows overcoming limitations on force computation shown by currently available NMS models, which constrain the operation of muscles to satisfy joint moments about one single degree of freedom (DOF) only (i.e. knee flexion-extension). The design of advanced human machine interfaces can benefit from the application of our proposed multi-DOF NMS model. The better estimates of the human internal state it provides with respect to single-DOF NMS models, will allow designing more intuitive human-machine interfaces for the simultaneous EMG-driven actuation of multiple joints in lower limb powered orthoses.

Learning with slight forgetting optimizes sensorimotor transformation in redundant motor systems

Recent theoretical studies have proposed that the redundant motor system in humans achieves well-organized stereotypical movements by minimizing motor effort cost and motor error. However, it is unclear how this optimization process is implemented in the brain, presumably because conventional schemes have assumed a priori that the brain somehow constructs the optimal motor command, and largely ignored the underlying trial-by-trial learning process. In contrast, recent studies focusing on the trial-by-trial modification of motor commands based on error information suggested that forgetting (i.e., memory decay), which is usually considered as an inconvenient factor in motor learning, plays an important role in minimizing the motor effort cost. Here, we examine whether trial-by-trial error-feedback learning with slight forgetting could minimize the motor effort and error in a highly redundant neural network for sensorimotor transformation and whether it could predict the stereotypical activation patterns observed in primary motor cortex (M1) neurons. First, using a simple linear neural network model, we theoretically demonstrated that: 1) this algorithm consistently leads the neural network to converge at a unique optimal state; 2) the biomechanical properties of the musculoskeletal system necessarily determine the distribution of the preferred directions (PD; the direction in which the neuron is maximally active) of M1 neurons; and 3) the bias of the PDs is steadily formed during the minimization of the motor effort. Furthermore, using a non-linear network model with realistic musculoskeletal data, we demonstrated numerically that this algorithm could consistently reproduce the PD distribution observed in various motor tasks, including two-dimensional isometric torque production, two-dimensional reaching, and even three-dimensional reaching tasks. These results may suggest that slight forgetting in the sensorimotor transformation network is responsible for solving the redundancy problem in motor control.

It is thought that the brain can optimize motor commands to produce efficient movements; however, it is unknown how this optimization process is implemented in the brain. Here we examine a biologically plausible hypothesis in which slight forgetting in the motor learning process plays an important role in the optimization process. Using a neural network model for motor learning, we initially theoretically demonstrated that motor learning with a slight forgetting factor consistently led the network to converge at an optimal state. In addition, by applying the forgetting scheme to a more sophisticated neural network model with realistic musculoskeletal data, we showed that the model could account for the reported stereotypical activity patterns of muscles and motor cortex neurons in various motor tasks. Our results support the hypothesis that slight forgetting, which is conventionally considered to diminish motor learning performance, plays a crucial role in the optimization process of the redundant motor system.

EMG-based neuromuscular modeling with full physiological dynamics and its comparison with modified Hill model

EMG-based muscle model has many applications in human-machine interface and rehabilitation robotics. For the muscular force estimation, so-called Hill-type model has been used in most of the cases. It has already shown its promising performance, however it is known as a phenomenological model considering only macroscopic physiology. In this paper, we discuss EMG-force estimation with the full physiology based muscle model in voluntary contraction. In addition to Hill macroscopic representation, a microscopic physiology description as stated by Huxley and Zahalak is integrated. It has significant meaning to realize the same kind of EMG-force estimation with multiscale physiology based model not with a phenomenological Hill model, because it brings the understanding of the internal biophysical dynamics and new insights about neuromuscular activations.

Computationally fast estimation of muscle tension for realtime bio-feedback

In this paper, we propose a method for realtime estimation of whole-body muscle tensions. The main problem of muscle tension estimation is that there are infinite number of solutions to realize a particular joint torque due to the actuation redundancy. Numerical optimization techniques, e.g. quadratic programming, are often employed to obtain a unique solution, but they are usually computationally expensive. For example, our implementation of quadratic programming takes about 0.17 sec per frame on the musculoskeletal model with 274 elements, which is far from realtime computation. Here, we propose to reduce the computational cost by using EMG data and by reducing the number of unknowns in the optimization. First, we compute the tensions of muscles with surface EMG data based on a biological muscle data, which is a very efficient process. We also assume that their synergists have the same activity levels and compute their tensions with the same model. Tensions of the remaining muscles are then computed using quadratic programming, but the number of unknowns is significantly reduced by assuming that the muscles in the same heteronymous group have the same activity level. The proposed method realizes realtime estimation and visualization of the whole-body muscle tensions that can be applied to sports training and rehabilitation.

Identification of Human Limb Viscoelasticity using Robotics Methods to Support the Diagnosis of Neuromuscular Diseases

In this paper we present an original method to estimate in vivo the joint dynamics of the human limbs. The method is based on a non-invasive and painless technology making use of an optical motion capture system and an associated skeletal model to record the human motion and compute its kinematics and its dynamics. The formalism that is used for the identification is commonly used in robotics. The passive limb joints properties are modeled by enhanced spring-damper systems. The inverse dynamics is sampled along a movement to give an over-determined system. The obtained system is solved by the linear least-squares method. To perform the estimation, we place emphasis on giving indicators and requirements to interpret the obtained results, and on using painless, passive constraint-free movements that are usually performed during the clinical diagnosis of neuromuscular diseases. Finally the method is experimentally applied to two healthy subjects and five patients of neuromuscular diseases in order to estimate the upper-limb viscoelastic properties. The obtained results are discussed.

Modeling and identification of human neuromusculoskeletal network based on biomechanical property of muscle

In this paper, we build a whole-body neuromusculoskeletal network model including somatic reflex, and identify its parameters through non-invasive measurements and statistical analysis. Such models are crucial for analyzing and estimating signals in the nervous system. Our neuromuscular model consists of two parts. The first part models the neuromuscular network that represents the relationships between the spinal nerve signals and muscle activities, which are then converted to muscle tensions using a physiological muscle dynamics model. The second part includes the feedback loops from muscle spindles and Golgi tendon organs to the spinal nerve that represent the somatic reflex using muscle length, velocity, and tension information. We demonstrate the consistency of the model by showing that a forward dynamics simulation of somatic reflex yields a motion similar to actual human response.

Characterization of motor skill based on musculoskeletal model

In this paper, we propose two methods to quantitatively analyze the motor skill in sports. The first method is the dimensionality reduction using the principal component analysis (PCA). The motion data, e.g. the joint angles (143-dimensional vector) or the muscle tensions (989-dimensional vector), are projected to a lower dimensional space that well represents the characteristics of original data. The similarities and differences become clear by observing the data in the low-dimensional space. The second method utilizes the joint stiffness obtained from joint kinematics and a biological muscle model. Though muscle tension data contain richer information than joint angle data, the dimension is so high that simply applying PCA does not give useful insights. Here we calculate the joint stiffness using the muscle tension data and a biological muscle model. This information represents the muscle usage skill which can not be observed only from motion data, and reflects the redundancy of the muscle tensions. We demonstrate the two methods by analyzing skilled performers' motions.

Macroscopic modeling and identification of the human neuromuscular network

In this paper, we build a mathematical model of the whole-body neuromuscular network and identify its parameters by optical motion capture, inverse dynamics computation, and statistical analysis. The model includes a skeleton, a musculotendon network, and a neuromuscular network. The skeleton is composed of 155 joints representing the inertial property and mobility of the human body. The musculotendon network includes more than 1000 muscles, tendons, and ligaments modeled as ideal wires with any number of via points. We also develop an inverse dynamics algorithm to estimate the muscle tensions required to perform a given motion sequence. Finally, we model the relationship between the spinal nerve signals and muscle tensions by a neural network. The resulting parameters match well with the agonist-antagonist relationships of muscles. We also demonstrate that we can simulate the patellar tendon reflex using the neuromuscular model. This is the first attempt to build and identify a macroscopic model of the human neuromuscular network based only on non-invasive motion measurements, and the result implies that the activation commands from the motor neurons can be considerably simple compared with the number of muscles to be controlled.

Modeling and identifying the somatic reflex network of the human neuromuscular system

In this paper, we build a mathematical model of the whole-body neuromuscular network and identify its parameters by optical motion capture, inverse kinematics, inverse dynamics computation, and statistical analysis. The model includes a skeleton, a musculotendon network, and a neuromuscular network. The skeleton is composed of 155 joints representing the inertial property and mobility of the human body. The musculotendon network includes more than 1000 muscles, tendons, and ligaments modeled as ideal wires with any number of via points. We also develop an inverse dynamics algorithm to estimate the muscle tensions required to perform a given motion sequence. Finally, we model the somatic reflex network based on the relationship between the spinal nerves and the muscle tensions by a neural network. The resulting parameters match well with the agonist-antagonist relationship of the muscles. We also demonstrate that the model inherently includes low-level somatic reflexes such as the patellar tendon reflex using the neuromuscular model. This is the attempt to build and identify the neuromuscular network based only on non-invasive motion measurements, and the result shows that the whole-body muscles can be controlled by the command signals as few as the number of spinal nerve rami.

Estimation of bio-signal based on human motion for integrated visualization of daily-life

This paper describes a method for the estimation of bio-signals based on human motion in daily life for an integrated visualization system. The recent advancement of computers and measurement technology has facilitated the integrated visualization of bio-signals and human motion data. It is desirable to obtain a method to understand the activities of muscles based on human motion data and evaluate the change in physiological parameters according to human motion for visualization applications. We suppose that human motion is generated by the activities of muscles reflected from the brain to bio-signals such as electromyograms. This paper introduces a method for the estimation of bio-signals based on neural networks. This method can estimate the other physiological parameters based on the same procedure. The experimental results show the feasibility of the proposed method.

A new non-orthogonal decomposition method to determine effective torques for three-dimensional joint rotation

This paper describes a new non-orthogonal decomposition method to determine effective torques for three-dimensional (3D) joint rotation. A rotation about a joint coordinate axis (e.g. shoulder internal/external rotation) cannot be explained only by the torque about the joint coordinate axis because the joint coordinate axes usually deviate from the principal axes of inertia of the entire kinematic chain distal to the joint. Instead of decomposing torques into three orthogonal joint coordinate axes, our new method decomposes torques into three "non-orthogonal effective axes" that are determined in such a way that a torque about each effective axis produces a joint rotation only about one of the joint coordinate axes. To demonstrate the validity of this new method, a simple internal/external rotation of the upper arm with the elbow flexed at 90 degrees was analyzed by both orthogonal and non-orthogonal decomposition methods. The results showed that only the non-orthogonal decomposition method could explain the cause-effect mechanism whereby three angular accelerations at the shoulder joint are produced by the gravity torque, resultant joint torque, and interaction torque. The proposed method would be helpful for biomechanics and motor control researchers to investigate the manner in which the central nervous system coordinates the gravity torque, resultant joint torque, and interaction torque to control 3D joint rotations.