Developmental Changes of Muscle Synergies in an Infant's Walking.

Primitive stepping is one of the primitive reflexes in newborns in response to external stimuli. It is known that primitive stepping disappears about two months after birth, but its role and the relationship with voluntary gait acquired later are still unknown. In this study, we extracted muscle synergies, spatiotemporal coordination patterns of muscle activities, from EMG measured in one infant during growth (4-18 weeks of age) using non-negative matrix factorization (NMF).

Behavioral and neuro-cognitive bases for emergence of norms and socially shared realities via dynamic interaction.

In the digital era, new socially shared realities and norms emerge rapidly, whether they are beneficial or harmful to our societies. Although these are emerging properties from dynamic interaction, most research has centered on static situations where isolated individuals face extant norms. We investigated how perceptual norms emerge endogenously as shared realities through interaction, using behavioral and fMRI experiments coupled with computational modeling.

Characterizing Strategic Contributions of Physical Therapy to Natural Standing Motion in the Muscle Synergy Space.

Understanding the contributions of therapist skill during intervention is essential for improving existing rehabilitation methodologies. This study aims to characterize therapist intervention on an important activity of daily living, the sit-to-stand motion. Using the concept of muscle synergy, we quantify and compare naturally-occurring standing strategies with those induced by a physical therapist.

Modeling dynamic high-DOF finger postures from surface EMG using nonlinear synergies in latent space representation.

Accurate proportional myoelectric control of the hand is important in replicating dexterous manipulation in robot prostheses and orthoses. However, this is still difficult to achieve due to the complex and high degree-of-freedom (DOF) nature present in the governing musculoskeletal system. To address this problem, we suggest using a low dimensional encoding based on nonlinear synergies to represent both the high-DOF finger joint kinematics and the coordination of muscle activities taken from surface electromyographic (EMG) signals.

Estimation of continuous multi-DOF finger joint kinematics from surface EMG using a multi-output Gaussian Process.

Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint kinematics from EMG signals using a multi-output convolved Gaussian Process (Multi-output Full GP) that considers dependencies between outputs.

Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model.

Background: Surface electromyography (EMG) signals are often used in many robot and rehabilitation applications because these reflect motor intentions of users very well. However, very few studies have focused on the accurate and proportional control of the human hand using EMG signals. Many have focused on discrete gesture classification and some have encountered inherent problems such as electro-mechanical delays (EMD).

Assist-as-needed robotic trainer based on reinforcement learning and its application to dart-throwing.

This paper proposes a novel robotic trainer for motor skill learning. It is user-adaptive inspired by the assist-as-needed principle well known in the field of physical therapy. Most previous studies in the field of the robotic assistance of motor skill learning have used predetermined desired trajectories, and it has not been examined intensively whether these trajectories were optimal for each user.

Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals.

Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries have driven a surge in the development of wearable assistive devices in recent years. In this paper, we present a system made up of a low-profile, optimally designed finger exoskeleton continuously controlled by a user's surface electromyographic (sEMG) signals. The mechanical design is based on an optimal four-bar linkage that can model the finger's irregular trajectory due to the finger's varying lengths and changing instantaneous center.

Continuous estimation of finger joint angles using muscle activation inputs from surface EMG signals.

Prediction of dynamic hand finger movements has many clinical and engineering applications in the control of human interface devices such as those used in virtual reality control, robot prosthesis and rehabilitation aids. Surface electromyography (sEMG) signals have often been used in the mentioned applications because these reflect the motor intention of users very well. In this study, we present a method to estimate the finger joint angles of a hand from sEMG signals that considers electromechanical delay (EMD), which is inherent when EMG signals are captured alongside motion data.

Throwing darts utilizes the interaction torque of the elbow joint.

Acquiring the skillful movements of experts is a difficult task in many fields. If we find quantitative indices of skillful movement, we can develop an adaptive training system using the indices. We focused on throwing darts in our previous study.

Comparison of experts and non-experts in throwing darts based on optimization criteria.

Acquiring skillful movements of experts is a difficult task in many fields. Since non-experts often fail to find out how to improve their skill, it is desirable to find quantitative indices of skillful movements that clarify the difference between experts and non-experts. If we find quantitative indices, we can develop an adaptive training system using the indices.