This paper proposes a hierarchical motion generation model that integrates individual intent into dynamic locomotion control, aiming to reproduce transitions between walking and running within a unified physical framework. The model consists of a strategy layer that reflects high-level objectives such as speed and energy efficiency, and a tactics layer that adjusts physical parameters such as leg stiffness, touchdown angle, and applied force. These layers interact through dynamically modulated control gains, allowing motion transitions to emerge naturally without explicit switching mechanisms. Simulations based on an extended Spring-Loaded Inverted Pendulum (SLIP) model demonstrate that differences in acceleration duration and energy supply result in diverse locomotion patterns. Notably, a transition from walking to running occurs when both acceleration intensity and additional energy surpass specific thresholds. The results highlight the model’s capacity to capture hysteresis-like features in human gait transitions and emphasize the importance of coordinated tactical control. Future work will address integrating multiple tactical elements and experimental validation toward applications in assistive robotics and human movement analysis.

This study aims to construct a running simulator based on a motion generation and control system that enables the description of motion strategies using the spring-loaded inverted pendulum (SLIP) model. The problems of stability and robustness encountered in the running simulation with the SLIP model are elucidated, and stable running is achieved by controlling the stiffness and the attitude angle dynamically at touchdown, as well as human energy adjustment that is introduced to consider the active motion strategy. As a result, passive and active control by humans can be expressed, and a framework that can express the changes in running due to motion strategies is constructed. Finally, we discuss the possibility of describing and elucidating the motion strategies.

We introduce document retrieval and comment generation tasks for automating horizon scanning. This is an important task in the field of futurology that collects sufficient information for predicting drastic societal changes in the mid- or long-term future. The steps used are: 1) retrieving news articles that imply drastic changes, and 2) writing subjective comments on each article for others’ ease of understanding. As a first step in automating these tasks, we create a dataset that contains 2,266 manually collected news articles with comments written by experts. We analyze the collected documents and comments regarding characteristic words, the distance to general articles, and contents in the comments. Furthermore, we compare several methods for automating horizon scanning. Our experiments show that 1) manually collected articles are different from general articles regarding the words used and semantic distances, 2) the contents in the comment can be classified into several categories, and 3) a supervised model trained on our dataset achieves a better performance. The contributions are: 1) we propose document retrieval and comment generation tasks for horizon scanning, 2) create and analyze a new dataset, and 3) report the performance of several models and show that comment generation tasks are challenging.

Human running motion is a complex motion that not only based on the movement of legs but also needs the entire body to participate in. Therefore, we focused on the upper body, designed a trunk and pelvic rotation assist suit to apply an external force on the chest and pelvis for assisting the running motion, changing the energy flow between the upper and lower body to improve the running efficiency. However, human motion is very complicated and difficult to analyze, it is hard to explore the specific effect of the external force which is provided by the rotation assist suit on human motion. In this paper, we proposed an advanced spring-loaded inverted pendulum model for simulating the human running motion in natural running status and expanded running status in which the motion is expanded by wearing the trunk and pelvic rotation assist suit, parametrically represent the changes of human due to the intervention of the external force, and discuss the effect of the external assistant force on the human running motion.

Japanese society is ageing ever faster. One of the most critical issues here is the shortage of working population, which is both cause and effect of the `super-ageing' problem. We propose a new framework to `desterilize' and utilize the elderly population as a new social resource. To sustain and hopefully enhance cognitive and physical functions of the elderly, we integrate cognitive and physical interventions by using high-fidelity (Hi-Fi) virtual/augmented reality (Lucid Virtual/Augmented Reality, LVAR) and an endoskeletal robot suit (StillSuit), respectively. LVAR has a physics-fidelity (Phy-Fi) digital-self for each LVAR space, and provides real time dynamic feedbacks to the user through StillSuit. Physical interventions are governed with a biologically relevant musculoskeletal model tailored for each user. To realize social cognition, furthermore, LVAR provides a social networking service with high quality 3D immersive experiences, which is able to be shared by remote users. With the fine-tuned interventions based on biological data of human and non-human animals, we prolong the healthy life expectancy of the elderly population to be the social resource, by which we will overcome the negative spiral of the super-ageing society.

This study generates and analyzes unsafe human motions that cannot be measured experimentally in laboratories with dynamic consistency. Detailed whole-body motions are generated by a multilayered kinodynamics simulation (MLKD Sim) that uses a detailed digital whole-body human model and a simple motion-representation model that parametrically represents human motion mechanisms. First, we develop the simple motion-representation model that represents human motion and contact force data that are experimentally measured in a laboratory, and we identify this model's parameters based on these experimental data. Forward dynamics computation of this motion-representation model with changing model and/or environmental parameters simulates motion modification as well as a contact force with dynamic consistency. Finally, the mapping function from the motion-representation model's motion to the detailed motion identified from the experimental data is used to reconstruct the detailed whole-body motion. MLKD Sim reconstructs a vertical contact force with average error of 2.18E+02 N, center of mass trajectory with average error of 3.31E-02 m, ankle joint angle with average error of 1.11E-01 rad (2.95E+00%), and ankle joint torque with average error of 6.13E+01 Nm (1.93E+01%). Unsafe motion simulation results show that the physical load on the hip, knee, and ankle joints increases by 9.23E+01%, 5.42E+02%, and 1.45E+02% respectively with 0.5-m level difference in a running surface. These results imply that when sprinting in an unknown environment, we need to protect, in order, the knee ankle, and hip joints. This study conducts detailed dynamics and kinematics analysis of unsafe human motions that cannot be measured experimentally in laboratories to prevent injuries, falls, and fatigue, and these results should find applications in the fields of medicine and welfare.

The paper aims to develop a methodology of transferring the knowledge obtained from the experiments of laboratory animals to human musculoskeletal system. To achieve the goal, we propose a method for estimating the homologous posture of the mammalian skeletal system corresponding to the human body posture. We hypothesize the homology of bone geometry between mammalian species implies that of biomechanical functions. The method relies on this homology and determines the homologous postures according to the anatomical landmarks of bone geometry. This paper shows the results of the analysis on homologous postures between the human and mouse skeletal models to validate our hypothesis. A pilot study also introduces comparison of mechanical functions between the two models by using the homologous postures.

Motor functions of the biological system has been forged through 4 billion years evolution. From a neurorobotics view, it is important not only to know how well it works, but also how it fails. To quantitatively describe early onset symptoms of a neurodegenerative disease, we analyzed phenotypes of genetically engineered Huntington disease (HD) model mice, which reveal progressive impaired motor functions. We devised a simple yet sensitive paradigm called the crystalized motion profile (CMP), by which we successfully detected subtle difference between normal and abnormal mice in terms of whole-body level motor coordination. Our long-term objective is to remodel human mind and body to regain impaired motor and cognitive functions with ageing. To do so, we are developing a soft neurorobotic suit that provides integrated cognitive and physical interventions to users. Our analysis on the HD model mice is important as the first step to bridge between molecular mechanisms (altered genetic code) and the macroscopic neuro-musculoskeletal model. With this, we can extrapolate from knowledge of non-human mammals to human to derive the remodeling.

Deformation of skin and muscle is essential for bringing an animated character to life. This deformation is difficult to animate in a realistic fashion using traditional techniques because of the subtlety of the skin deformations that must move appropriately for the character design. In this paper, we present an algorithm that generates natural, dynamic, and detailed skin deformation (movement and jiggle) from joint angle data sequences. The algorithm has two steps: identification of parameters for a quasi-static muscle deformation model, and simulation of skin deformation. In the identification step, we identify the model parameters using a musculoskeletal model and a short sequence of skin deformation data captured via a dense marker set. The simulation step first uses the quasi-static muscle deformation model to obtain the quasi-static muscle shape at each frame of the given motion sequence (slow jump). Dynamic skin deformation is then computed by simulating the passive muscle and soft tissue dynamics modeled as a mass–spring–damper system. Having obtained the model parameters, we can simulate dynamic skin deformations for subjects with similar body types from new motion data. We demonstrate our method by creating skin deformations for muscle co-contraction and external impacts from four different behaviors captured as skeletal motion capture data. Experimental results show that the simulated skin deformations are quantitatively and qualitatively similar to measured actual skin deformations.

In this paper, we demonstrate that a musculoskeletal model with an anatomically-inspired abdomen model can estimate human-like abdominal muscle tension, in contrast to previous musculoskeletal models or models based on joint elasticity. We first model the abdominal structure based on anatomical knowledge. The anatomically-inspired abdomen model consists of a Tendinous Intersection model that connects the muscle bellies of Rectus Abdominis and represents its pathway, a Rectus Abdominis model that represents the muscle contraction part, and a balloon-type abdominal cavity model that represents the volumetric abdominal cavity. These models compute the intra-abdominal pressure and its effect on the lumbar vertebrae joint torques. We use these models with our musculoskeletal model to estimate the abdominal muscle tension during sitting up motion with optical motion capture, inverse kinematics, and inverse dynamics computation. Our computational results show that our abdomen model estimates the muscle tension with a waveform similar to the muscle activity measured by electromyography, while the previous musculoskeletal model and the joint elasticity model estimate obviously different muscle tension patterns. These results imply that the intra-abdominal pressure is critical in estimating the abdominal muscle tension rather than the inertial properties and the joint elasticity.

This study proposes a model that combines a realistically scaled neural network made up of pools of spiking neurons, with a musculoskeletal model of the human arm. We used evidence from literature to design topological pools of spinal neurons and their synaptic connections. The spiking output of the motor neuron pools were used as the command signals that generated motor unit forces, and drove joint motion. Feedback information from muscle spindles was relayed to the neural network via monosynaptic and disynaptic pathways. Participant-specific parameters of the combined neuromusculoskeletal (NMS) system were then identified from recorded experimental data. The identified NMS model was used to simulate the arm stretch reflex and the results were validated by comparison to an independent recorded dataset. The models and methodology proposed in this study show that large and complex neural systems can be identified in conjunction with the musculoskeletal systems that they control. This additional layer of detail in NMS models has important relevance to the research communities related to rehabilitation robotics and human movement analysis.

In this paper, we demonstrate that a neuromuscular controller built based on the human anatomical structure and motion data can realize human-like responses to unexpected disturbances during locomotion. This particular work concerns the response to trips due to obstacles and shows that the two strategies identified in biomechanics emerge from a single controller. We first identify the parameters of a neuromuscular network model using the muscle tension data during a human walking motion. The anatomically-correct network models the somatosensory reflex of the human neuromuscular system. We use this network as the controller for a musculoskeletal human model to simulate its response to disturbances. Simulation results show that our neuromuscular controller automatically results in the appropriate trip recovery strategy with a single set of parameters, although we do not explicitly model the trip response or the condition to invoke each strategy. This result implies that an appropriately designed locomotion controller can also provide rapid responses to trips without deliberate controller selection or planning.

Human motion coordination is a long-standing research issue in biomechanics, and it should also have some implications for humanoid robot control.We have built a whole-body somatosensory reflex model based on our neuromusculoskeletal model and identified its parameters through non-invasive measurements and statistical analysis. Such models are crucial for analyzing and estimating signals in the nervous system. In this paper, we focus on signal transmission delay of the somatosensory reflex loop and investigate its relationship with the generalization capability of the reflex model. We obtain some sets of model parameters assuming different time delays using the data obtained from a stepping motion, and perform cross validations against stepping motions with different cycles as well as entirely different behaviors such as squat and jump. Interestingly, time delays close to the value expected from physiological properties show better cross-validation results than others. This result suggests that relatively simple reflex control can be generalized to multiple behaviors if the parameters are appropriate, and that robust control is possible even with large feedback delay.

Contact-free estimation of the human somatosensory information is an essential skill for robots working in daily environments. The main objective of this paper is to develop a method for estimating muscle tensions without any sensors attached to the body. Muscle tension is an important information for evaluating physical load during motions. Existing approaches utilizing optimization techniques and/or electromyography (EMG) signals are not appropriate due to lack of physiological validity or usage of electrodes. In this paper, we propose to use a database of muscle tension distribution for obtaining physiologically realistic muscle tensions only from motion data. Using such database instead of direct EMG measurement is justified by the fact that muscle tension distribution is relatively highly correlated even among different subjects. For each new motion frame, we search for a similar entry in the database and use the corresponding muscle tension distribution to estimate the current muscle tensions. We demonstrate that the muscle tensions obtained by this approach is much closer to the result using the EMG data than that using pure numerical optimization, even when the database is constructed from other person's data.

This paper discusses the design issue of general closed kinematic chains focusing on analysis of constraints and elastic deformations. Closed kinematic chains are considered more advantageous in rigidity, power-output, and accuracy than open kinematic chains. However, it is not much stressed that closed kinematic chains are so sensitive to machining errors that a tenth of a millimeter of error might result in jamming and immobility. To avoid this critical problem, closed kinematic chains tend to be designed with play that reduces their native advantages. If 3D-CAD systems are equipped with a mathematical tool that evaluates machining accuracy, elasticity, and constraints, it will significantly assist the skill of designers and extend the field of applications of closed kinematic chains. In this paper, we clarify machining errors that are absorbable as errors in unactuated joints. These kind of errors are permissible. Mobility analysis in the presence of unabsorbable machining errors is discussed taking account of elastic deformations and strain energy. The mechanism can move smoothly even with machining errors if the strain energy remains less fluctuate along its motion locus. The established mathematical method of mobility analysis is applied to the design of a closed kinematic chain and used in practice to fabricate a medical robot system.