Lower extremities adhering to the spring mechanics proposed by the spring-loaded inverted pendulum (SLIP) model during running are regarded as a form of running-gait optimization among runners. We examined the degree of adherence between experimental and SLIP model-predicted vertical ground reaction force (vGRF) in individuals with unilateral transfemoral amputation. Nine individuals with unilateral transfemoral amputation performed running trials at 6 different speeds on an instrument treadmill. The trials were set at 30% to 80% of their maximum speeds with a 10% increment. The experimental vGRF collected was compared with SLIP model-predicted vGRF. The degree of adherence was calculated using the R2 goodness-of-fit statistics. The experimental vGRF of the affected limbs exhibited a significantly higher degree of adherence than unaffected limbs. There were no significant differences in the degree of adherence between different speed trials for either limb. This finding indicates that running-specific prostheses exhibit mechanical behavior that is consistent with SLIP mechanics, whereas the unaffected limb relies on compensatory, non-SLIP-like strategies. Furthermore, the results suggest that the interaction of running-specific prostheses with other prosthetic components and the residual limb enables the preservation of running mechanics across a range of speeds, thereby supporting the application of SLIP-based modeling to the affected limb.

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 proposes a novel approach to assess muscle fiber type distributions using electrical stimulation and surface electromyography (sEMG). By applying electrical impulses to muscles with different fiber dominance, we examine the temporal and frequency characteristics of the resulting responses. Our results demonstrate that non-selective and synchronized muscle fiber recruitment induced by electrical stimulation can provide valuable insights into muscle composition, particularly in the context of fiber type dominance. The methodology presented here could be utilized for muscle monitoring during processes such as the decay of fast fibers in sarcopenia, enabling early detection of this condition and potentially enhancing interventions for muscle health in aging populations.

This paper presents a simple mechanical model capable of evaluating a running effectiveness index ϵ _EI based on mechanical energy. We extended a spring-loaded inverted pendulum model, considering the biomechanical determinants of running economy (RE), to develop a simplified mechanical model that can accurately represent the ϵ _EI calculated by a detailed running model. To assess the accuracy of the proposed model in estimating RE, we computed the ϵ _EI using both the proposed and detailed models, based on data obtained from running experiments. A linear regression analysis using the least squares method was performed to analyze the relationship between the ϵ _EI values calculated by the two models. The regression analysis results of the ϵ _EI values obtained from the two models yielded significant F-statistics ( p < 0.01 ) for all four participants, demonstrating that the proposed model can sufficiently represent the running economy index calculated by the detailed model.

We aimed to investigate whether a linear relationship exists between swimming velocity and vertical body position for each stroke phase in front crawl, and to determine whether there are differences in the velocity effect among the stroke phases. Eleven male swimmers performed a 15 m front crawl at various swimming velocities. The whole-body centre of mass (CoM) was estimated from individual digital human models using inverse kinematics. The horizontal CoM velocity and vertical CoM position from the water surface were calculated for one stroke cycle and divided into five stroke phases: entry, pull, push, release, and recovery. Linear mixed-effects model analysis revealed a positive trend between the mean swimming velocity and the mean vertical CoM position for each stroke phase (p < 0.001 for all phases). The interaction term between stroke phase and swimming velocity was significant (p < 0.001), and the slopes of the propulsive phases (pull and push) were larger than those of the non-propulsive phases (entry, release, and recovery) (p < 0.001). These findings provide practical implications that vertical body position can be evaluated independently of the stroke phase while considering velocity effects, and that focusing on propulsive phases allows easier detection of vertical body position changes.

PURPOSE:This study aimed to investigate whether a lung volume-dependent decrease in the projected frontal area (PFA) contributes to reducing active drag in the front crawl. METHODS:Twelve competitive male swimmers performed a 15-m front crawl at 1.20 m·s -1 while sustaining one of three lung volume levels: maximal inspiration (INSP), maximal expiration (EXP), or intermediate (MID). The three-dimensional positions of the reflective markers attached to the swimmers' bodies were measured using an underwater motion capture system. Based on the body shape obtained from the photogenic body scanner, an individual digital human model was created using model vertices color coded into eight body segments. The time series of the volumetric swimming motion was reconstructed using the individual digital human model and motion capture data. The PFA of each body segment was calculated using image processing. The pressure drag index (PDI), defined as the value excluding the drag coefficient while simultaneously considering the PFA and horizontal velocity, was calculated for each body segment. RESULTS:There were significant interactions between lung volume and body segment on PFA and PDI (both P < 0.001). Specifically, the PFA and the PDI of the head segment were smaller in INSP than in EXP ( P ≤ 0.046); in addition, the PFA and the PDI of the trunk and femur segments were smaller in INSP and MID than in EXP ( P ≤ 0.003). CONCLUSIONS:These findings suggest that the decrease in PFA caused by the increase in lung volume directly contributes to reducing active drag.

Abstract We are currently developing a suit that assists human running motion based on the Smart Suit (SS). The SS is a wearable assistive device that intervenes in trunk kinetic chain movements by linking trunk rotation to hip flexion and extension through elastic belts. By intervening in the trunk kinetic chain, our goal is to enhance stiffness, thereby improving running economy and speed. The purpose of this study is to acquire fundamental insights into the changes in leg and trunk rotational stiffness induced by the SS, as well as the underlying mechanisms. We defined the SS torque intervention rate $$p_{ss}$$ p ss , which focuses on the magnitude of force, and the peak time difference index, $$e_{lag}$$ e lag , between SS and humans, which focuses on the timing of force, and analyzed the relationship between the stiffness change rate due to SS wearing. The results of the analysis suggest that SS affects human running sensitively rather than mechanically. We also confirmed that the degree of adaptation to SS, assessed by the timing gap between SS exertion and human exertion, contributes significantly to changes in trunk rotation stiffness.

This study analyzed upper torso rotation, which is crucial for improving pitch velocity and minimizing upper limb load during pitching. A simplified model was developed to facilitate the analysis which focused on torso torsion. In this model, the instantaneous torque transfer ratio and torso mobility were defined as key indices. We experimentally verified how these indices vary under three conditions: no torsion, static torsion, and dynamic torsion. The results demonstrate that torso mobility increases in conjunction with the instantaneous torque transfer ratio. Both static and dynamic torsion conditions resulted in higher instantaneous torque transfer ratios compared to the no-torsion condition. In static torsion, the maximum torso mobility was, on average, 26% greater than that observed under no torsion. In dynamic torsion, the maximum torso mobility was, on average, 69% greater than that observed under no torsion. These results indicate that torsion is effective in increasing both the instantaneous torque transfer ratio and torso mobility and that this effect is stronger in dynamic torsion. Therefore, torsion increased the angular velocity of the upper torso in response to torque input from the lower limb, potentially resulting in higher ball velocity and a reduction in upper limb strain. Additionally, the findings imply that the input required from the lower limbs to achieve a specified pitch velocity may be reduced. The simplified model and indices proposed in this study provide a foundation for designing exercise intervention techniques, evaluating athletic performance, and assessing injury risk related to torso rotation.

Surface electromyographic (sEMG) signals result from the interaction between motor unit action potentials (MUAPs) and neural spike trains, yet how specific features of spike timing shape the sEMG spectrum is not fully understood. Using a simplified convolutional model, we simulated sEMG by combining synthetic spike trains with MUAP templates, varying firing rate, temporal jitter, and motor unit synchronization to examine their effects on spectral characteristics. Rather than addressing a particular experimental condition such as fatigue or workload, the main goal of this study is to provide a framework that clarifies how variability in neural timing and muscle properties affects the observed sEMG spectrum. We introduce extractability indices to measure how clearly neural activity appears in the spectrum. Results show that MUAPs act as spectral filters, reducing components outside their bandwidth and limiting the detection of high firing rates. Temporal jitter spreads spectral energy and blunts frequency peaks, while moderate synchronization improves spectral visibility, partially countering jitter effects. These findings offer a reference for interpreting how neural and muscular factors shape sEMG signals, supporting a more informed use of spectral analysis in both experimental and applied neuromuscular studies.

Human running is a simple, periodic motion; however, the underlying mechanisms that generate this motion are complex, resulting in individual variations even when the same locomotion goal is pursued. Current generalized locomotion studies have not adequately explained these variations. To address this gap, we focus on the concept of motion strategy, which involves selecting different approaches to accomplish a task. Our objective is to develop a simulation that integrates these motion strategies. Achieving this requires both a deep understanding of the mechanisms behind human motion generation and the development of a system that can reproduce these mechanisms within a simulation. In this study, we propose a motion tactics model and develop a simulation system to replicate motions guided by these strategies. The simulation results were compared with experimental data to assess the effectiveness of the proposed model in replicating individual differences in motion. The model generated multiple motion states and showed it could select unique outcomes by combining different tactics, confirming its validity and feasibility. This approach demonstrated the potential for representing the characteristics in individual motion.

The projected frontal area (PFA) is a useful indicator of swimming drag. However, it is inherently limited because it only considers observable frontal areas from a frontal view. To address this limitation, we determined a new indicator, the projected and occluded frontal area (POFA), which includes occluded frontal areas relative to the swimming direction. This study aimed to examine the difference between the PFA and POFA, focusing on the tibial and femoral segments during front crawl. Twelve competitive male swimmers performed a 15-meter front crawl at 1.20 m·s-1. The three-dimensional positions of the reflective markers attached to the swimmers' bodies were collected using an underwater motion-capture system. The body shape of each swimmer was obtained using a body scanner. Two types of digital human models were created: a whole-body model with vertex colors divided into eight body segments and a segment-specific model extracted from the whole-body model. To reconstruct identical motions in both models, the joint angle data obtained through inverse kinematics computations using motion-capture data and the whole-body model were applied to the segment-specific models. The PFA and POFA were determined through image processing of a series of parallel frontal images from whole-body and segment-specific models, respectively. The PFA of the tibial and femoral segments was substantially smaller than the corresponding POFA (p < 0.001), with underestimation ratios of 86.1 % and 42.3 %, respectively. These results suggest that PFA is not a fully reliable indicator for evaluating swimming drag, at least in the tibial and femoral segments.

Psychological safety is pivotal for co-creation to build an open environment where innovative ideas can flourish. Traditionally, psychological safety has been evaluated from a stable and long-term perspective by implementing psychological scales. Consequently, existing interventions often focus on steadily enhancing psychological safety, which is less suitable for dynamic short-term co-creation settings. The purpose of this study is to introduce the use of emojis as a novel and intuitive interaction during co-creations and assess their effectiveness in evaluating and influencing psychological safety. We performed two experiments with 140 participants in total to test emojis as evaluations and interventions, respectively. The participants watched videos and annotated them with emojis based on their perceptions of emotions. This process allowed us to explore the relationship between perceived emotions and psychological safety. In the next phase, we embedded emojis directly into the videos to observe whether the participants’ emotional perceptions—and, consequently, their psychological safety—could be influenced by visual cues. Our findings demonstrate that positive emojis are positively correlated with psychological safety and negative emojis are negatively correlated with psychological safety. We also revealed that negative emojis significantly decreased psychological safety scores, whereas positive emojis did not lead to a corresponding increase.

This study introduces an advanced computational model for simulating surface electromyography (sEMG) signals during muscle contractions. The model integrates five elements that simulate the chain of processes from motor intention to voltage variations over the skin. These elements include the motor control system, motor neurons, muscle fibers, biological tissues, and electrodes. sEMG signals were simulated for isotonic and isometric contractions under two force conditions and compared with real data obtained from elbow flexion experiments. The results demonstrate a high level of similarity between simulated and real signals, encompassing both temporal and spectral features. Additionally, the study reveals a correlation between muscle fiber type distribution and changes in the spectral distribution of the simulated signals. Potential applications of this research include the development of comprehensive sEMG databases and elucidating the relationship between sEMG signal characteristics and internal neuromuscular parameters. Future research aims to further explore these applications and enhance the model's performance by leveraging emerging technologies such as machine learning. This approach establishes a framework for simulating sEMG signals under tailored neuromuscular conditions and holds promise for advancing our understanding of muscular physiology and human motor control mechanisms.

Respiration is a crucial metabolic process that converts macronutrients, carbohydrates and fats, and oxygen into energy and carbon dioxide to support motor actions. Moreover, the brain is a significant energy consumer, accounting for large portions of the body's total energy expenditure and relying primarily on carbohydrates for neural activity and plasticity. However, it is not known whether gas composition in breathing can serve as an indicator of neural activity and plasticity as they can for movement intensity. In human reaching movement tasks, we evaluated time-constants of sensorimotor learning during the recording of gas exchange. We computed the respiratory exchange ratio (RER), indicating whether carbohydrate or fat is used preferentially, and found that the RER was unaffected by the execution and learning of reaching movements and that it was stable within but varied across individuals. Interestingly, using computational modelling to identify short and long-time constants of sensorimotor learning, individual RER levels correlated with the estimated slow component of learning dynamics, suggesting a link between metabolic state and processes underlying long-term retention. To probe this further, we used glucose administration, known to increase RER by promoting carbohydrate utilisation, before training. Regression analysis indicated that glucose-induced RER increases during training were associated with enhanced estimated 24 h retention at the intra-individual level. Together, RER is associated with processes underlying long-term memory acquisition and retention, and glucose administration shifted the physiological idling state for the processes. Unravelling the specific neurobiological pathway from these intriguing breathing metrics to brain function emerges as a compelling new research direction. KEY POINTS: The brain is a major energy consumer (20% of total energy from only 2% of body weight), primarily using carbohydrates for neural activity and plasticity. The respiratory exchange ratio (RER) in breath signals the body's balance of fat-carbohydrate fuel use; this study explored whether RER reflects neural processes in motor memory acquisition and retention. Individual RER, stable during reaching tasks but varying across participants, correlated with the computationally estimated slow component of learning dynamics, which is linked to long-term retention. Glucose administration, known to increase RER, was associated with improved estimated 24 h motor memory retention at an individual level. The results suggest that RER indicates long-term motor memory processes and that manipulating RER via glucose may enhance motor memory, offering a new neurobiological pathway from these intriguing breathing metrics to memory function and potential practical implications for a simple but plausible intervention.

Robotics, mechatronics, and digital technologies are advancing significantly in sports, exercise, and healthcare, and offer innovative solutions that shape the future of these fields. In Part 2 of this special issue, we examine independent research and cutting-edge developments that address new challenges and provide unique contributions to these domains. Technological advancements in assistive devices and rehabilitation offer new solutions for improving the quality of life of individuals with physical challenges. Researchers of wearable assistive devices and prosthetics continue to devise methods to enhance mobility and support physical functions, thereby benefiting both users and healthcare providers. Sports and motion analysis remains a key focus area, where innovative technologies are employed to analyze and improve athletic performance. By capturing and evaluating detailed movement data, these technologies can help athletes refine their techniques and achieve greater precision in their sports, thus ultimately resulting in enhanced performance and injury prevention. As in Part 1, the studies presented in this issue demonstrate the practical applications of cutting-edge technologies and offer insights into their future potential. We hope that these contributions will inspire researchers and practitioners alike and provide a foundation for continued advancements in these exciting and impactful fields.

This study generates and analyzes human torso rotation and torsion unisng a simple model (TRM sim). As most injuries happen in the upper limb during pitching related to torso rotation and torsion, TRM sim is effective in baseball First, we designed TRM sim and generated the motion by forward dynamics. For validation, we measured three different human torso rotations with varying degrees of torsion. By aligning the peak values of input and their timings with human motions, TRM sim represented human motion, such as the relationship between torsion and the angular impulse of trunk muscle torque. These results suggest that TRM sim can represent torso rotation with different torsion levels. This study aims to generate torso rotations with various torsion to reduce upper limb load during pitching, with potential applications in designing intervention techniques.

Respiration is a crucial metabolic process that converts macronutrients and oxygen (O2) into energy and carbon dioxide (CO2), supporting motor actions. In addition to the energy demands for movements, the brain is a significant energy consumer for neural activity and plasticity. However, it is not known whether breathing patterns can serve as an indicator for them as they can for movement intensity. According to computational theory, motor memory updating involves fast and slow timescales, which may correspond to neural activity and plasticity. To investigate whether breathing patterns reflect these time constants, human experiments assessed short- and long-term memories while recording the O2-CO2 gas exchange. We found that the respiratory exchange ratio (RER), an indicator of metabolic mode, was not influenced by the execution and learning of the reaching movement and was stable within individuals but diverse across individuals. Interestingly, the individual differences in the RER reflect individual variation in long-term memory rather than short-term memory. Furthermore, to manipulate the RER, we provided 200 kcal of glucose immediately before the task. Surprisingly, 24-hour retention increased by 21%. Together, the RER would serve as a remarkable proxy for long-term motor memory and ingesting glucose would shift the neurophysiological “idling state” for learning. ### Competing Interest Statement The authors have declared no competing interest.

Robotics, mechatronics, and digital technologies are rapidly advancing in the fields of sports, exercise, and healthcare, and their applications are expanding daily. In part 1 of our special issue, we focus on the innovative contributions of cutting-edge technologies in various domains. Robotics technology is crucial in sports and exercise training as it monitors individual movements and forms, provides user feedback, and enables effective training. The development of robots to enhance the accuracy and reproducibility of sports techniques is a key element directly linked to performance improvements. Robotics and mechatronics provide new possibilities for treatment and rehabilitation in healthcare. Developing beneficial systems for medical professionals and patients contributes to increased precision in surgery and productive support in rehabilitation. Power assistance and motion augmentation are witnessing advancements that prolong individual capabilities and achieve better performance. These studies may reduce the physical burden of daily activities and specific tasks, thereby enhancing efficiency. Therefore, studies on haptics and sensory feedback are important. Efforts to improve user interfaces through virtual haptic experiences represent a promising new application of digital technology. The studies included in part 1 of this special issue present recent technological trends and practical examples of sports, exercise, and healthcare and aim to provide valuable information and inspire the readers. We hope that these studies will serve as significant steps toward future technological advancement and social implementation.

This study aimed to investigate the essential role of the kicking action in front crawl. To achieve this objective, we examined the relationships of the hand propulsive force and trunk inclination with swimming velocity over a wide range of velocities from 0.75 ms(-1) to maximum effort, including the experimental conditions of arm stroke without a pull buoy. Seven male swimmers performed a 25 m front crawl at various speeds under three swimming conditions: arm stroke with a pull buoy, arm stroke without a pull buoy (AWOB) and arm stroke with a six-beat kick (SWIM). Swimming velocity, hand propulsive force and trunk inclination were calculated using an underwater motion-capture system and pressure sensors. Most notably, AWOB consistently exhibited greater values than SWIM for hand propulsive force across the range of observed velocities (p < 0.05) and for trunk inclination below the severe velocity (p < 0.05), and these differences increased with decreasing velocity. These results indicate that 1) the kicking action in front crawl has a positive effect on reducing the pressure drag acting on the trunk, thereby allowing swimmers to achieve a given velocity with less hand propulsive force, and 2) this phenomenon is significant in low-velocity ranges.