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.

In off-pump coronary artery bypass grafting (OPCAB), the coronary arteries are joined as the heart beats. This procedure requires high skill and experience to be performed reliably and quickly. Although training kits are commonly used for technical training, the inability of trainees to always be with experienced surgeons for guidance and to receive immediate evaluation remains problematic. To address this problem, a system that allows a single trainee to observe and quantitatively evaluate the procedures performed by an experienced surgeon is being developed. In this study, to analyze the differences between the motions of experienced and novice surgeons, Leap Motion was used to measure the hand motion of the vascular anastomosis performed by both surgeons using a training kit. Using the measured data, the features of the vascular anastomosis surgical techniques performed by experienced and novices were tested using the Mann–Whitney U test. In addition, a binary classification was performed using machine learning with these features. The binary classification results show that trainees can be classified as experts or novices with high accuracy using the developed system.

Strokes are a widespread condition characterized by motor paralysis and sensory impairment, and they significantly hinder daily activities. Hence, rehabilitation aimed at improving motor function is crucial for post-stroke patients. Manual therapy, which involves the manual stimulation of the limbs to provide sensory input and enhance motor function, is commonly employed for rehabilitation. However, the limited duration of inpatient rehabilitation highlights the need for devices that can provide similar rehabilitation interventions at home. Here we elucidate the skills employed by physical therapists during intervention through interviews and measurements of the force applied to the plantar surface of the foot. Subsequently, we develop a device capable of replicating these intervention skills. The investigation of physical-therapist skills revealed the importance of stimulating the muscles involved in forming the longitudinal and transverse arches and the flexor muscles of the toes during manual therapy. The required force for stimulation was also noted. Using our device, which utilized pneumatic actuation to replicate therapist skills, intervention on the plantar surface of the feet of healthy adults demonstrated improved stability in their standing posture, particularly in the lateral direction. These devices can enhance the sensory-motor connection from the plantar surface, thereby improving motor abilities, such as preventing falls.

Pseudo-haptics convey force haptics without the use of complex mechanical devices and are being actively researched. However, a regression equation derivation method for estimating the perception produced by pseudo-haptics is yet to be established. In this study, we compared the model parameters (factors) for weightlifting movements, aiming to establish a method for deriving a regression equation to estimate the perception produced by pseudo-haptics. Weight perception produced by pseudo-haptics was determined by changing the control-display (CD) ratio, which is the amount of object movement in virtual reality (display) divided by the amount of object movement in reality (control). The regression equation estimating the perceived weight was derived using factors such as grasping force and electromyogram. The factors used in the derivation were determined based on quality engineering methods, and it was found that factors appropriate for human motor control strategies could be selected. In addition, the investigation provides insights for the realization of applications that can generate continuous pseudo-haptics.

Several power assist robots have been developed to reduce workloads, and address worker shortages and an aging workforce. Previously, the authors developed a power assist device to maintain shoulder posture using a braking mechanism; however, the device presented technical issues regarding the rigidity and weight of the mechanism. To address these issues, in this study, a flexible linear brake (FLB), a brake mechanism driven by negative pressure, was developed for power assist devices. Additionally, a braking force model for the FLB is proposed; the proposed model was verified by comparing the measured and modelled values. Finally, a shoulder assist device that uses the FLB and designed based on the proposed model is discussed in detail.

Understanding motion intentionality is important to augment motor skills. This can be explained by the anatomical structure and innate immune mechanisms of humans. This study aimed to analyze human motion using musculoskeletal information and estimate motion intentionality, motion direction, and muscle properties. The calculation method for the easy-to-move musculoskeletal direction in the task space was reversed to calculate the optimal muscle properties and matching rate for the motion direction. The analysis results of each walking and feint motion showed that the muscles were trained toward reduced muscular effort and improved skills for each motion. In addition, the results of the optimization of the matching rate and muscle properties indicate that the optimized muscle properties can express the motion better than the reference values, and the average matching rate increases by 2.41×10^-1, particularly for a feint motion. Therefore, skill acquisition and augmentation were achieved.

Extra or supernumerary robotic limbs are actively exploited in the field of body augmentation. The science of self-recognition of additional body parts is an interesting subject. Although the field is primarily led by psychological studies in virtual reality, which facilitate flexible experimental designs, we believe that assessments employing real robots are also essential. In this study, we investigated the sense of body ownership and agency of a dual-arm wearable robotic arm using an inexpensive and safe inflatable structure. We report the results of functional near-infrared spectroscopy (fNIRS) analysis of brain activity during the use of the robotic arm. The questionnaire results from the experiment, which involved a button-touching task, revealed that both the sense of ownership and sense of agency were significantly higher in the goal-oriented collaborative experience compared to the non-goal-oriented condition. This indicates that humans feel ownership of and agency toward an autonomous robot or a remote-controlled robotic arm operated by another person. The analysis of the fNIRS data included a two-factor analysis of variance for the learning and trial phases. While there was no main effect for the conditions within each phase, a significant interaction was observed between the two brain regions of the right angular gyrus and right postcentral gyrus.

Curling is a two-player, zero-sum, finite, complete-information, uncertain game in which stones are delivered to a point approximately 37 m (120 ft). Despite its popularity, the dynamics of curling stones are unclear, and significant variations exist in the conditions of curling stones and curling sheets. It is essential to have a highly reproducible delivery apparatus that can perform tests in various environments to clarify stone dynamics and evaluate curling stones and sheets more accurately. In this study, we developed a delivery robot capable of delivering stones as quickly and accurately as, if not better than, a human using a large-diameter pneumatic cylinder. The delivery robot could deliver stones with high repeatability at 2.0 to 4.0 m/s by controlling the velocity of the pneumatic cylinder using multiple solenoid valves. In addition, we verified the accuracy of the robot’s velocity meter using motion capture to ensure that the throwing velocity measured by the delivery robot was sufficiently accurate. Finally, we conducted repeated delivery experiments using the delivery robot and demonstrated that the robot could deliver stones with high repeatability.

Owing to the increase in the population of elderly people, the number of people suffering from mild cognitive impairment is increasing in several countries. Furthermore, family members of people with mild cognitive impairment, such as reduced function of memory, reasoning, and decision-making, undergo severe stress owing to difficulties in communicating with them. There are, however, few existing technologies to support learning this type of communication to reduce this stress. This study proposes a tool wherein an animated character resembles the behavior of elderly people with mild cognitive impairment in different scenarios to teach how to communicate with them. Fundamental experiments were conducted to confirm the validity of the proposal tool, and results showed that the tool successfully helped users learn how to communicate with elderly people with mild cognitive impairment. Furthermore, we demonstrated the advantages of using an animated character to depict the behavior of people with mild cognitive impairment. We believe the proposed tool enables the imagination of difficult situations and the understanding of communication with the elderly.

Real driving emissions (RDE) test procedures for type approval were initiated in Japan in 2022. In Japan, the test vehicle is driven on a test road (test course) by tracing the speed pattern prepared for each RDE test. The vehicle needs to trace the patterns in the same manner as in the conventional chassis dynamometer test. The use of a driving robot is effective for reproducibility in precisely tracing the target vehicle speed during these tests. In this study, we developed a driving robot for electrified vehicles (HEV, plug-in HEV, BEV, and FCV), which have regenerative brake-control systems. We applied a new logic to our robot that was developed in-house for conventional engine vehicles. We focused on braking deceleration of the regenerative brake control to change the operation timing from the accelerator pedal to the brake pedal. We verified the improvement of speed traceability of the driving robot installed with the proposed logic on the chassis dynamometer and test road.

The purpose of this paper is to apply an intelligent algorithm to conduct the torque tracking control for lower limb exoskeleton robot driven by an electro-hydraulic servo system (EHSS). The dynamics of EHSS actuating the robot are mathematically modeled with two degrees of freedom joints, and the torque control strategy is made to realize the minimization of human-machine forces. The PI controller is selected to implement this strategy, and the selection of PI gains is important for system control. Therefore, the ameliorative moth flame optimization (AMFO) algorithm is chosen to optimize the PI gains. The main idea of moth flame optimization is to mimic the evolution of a moth’s lateral positioning mechanism over time. The AMFO algorithm is capable of achieving enhanced better global and local search capabilities by adding the inertia weights to the position update formula. Moreover, the particle swarm optimization (PSO) and whale optimization algorithm (WOA) are selected to obtain comparative results. The results indicate that, when compared to the WOA-PI and PSO-PI controllers, the AMFO-PI controller gains the least amount in terms of rise time, overshoot, steady error, mean absolute error, and root mean square error.

To enable quantitative palpation in vivo, we previously developed a catheter-type tactile sensor with an outer diameter of 2 mm composed of a polyvinylidene fluoride (PVDF) film for minimally invasive surgery. However, our previous studies did not evaluate the effect of the PVDF film shape on the sensor output. In this study, we fabricated three types of prototype sensors with different PVDF film shapes and sizes using a simple cutting method. One of the films had the same shape as that used in one of our previous studies. We also prepared two types of PVDF film with a wide base and a narrow tip because we assumed that the deformation of the sensor gradually decreases from the root to the tip, similar to the first mode of the natural frequency. We evaluated the frequency response of the proposed sensors by vibrating the sensor tip and compared the results with the theoretical values. It was confirmed that the sensor output increased with PVDF film size. Although this tendency was observed for both the measured and theoretical values, the measured values were smaller than the theoretical values. Moreover, the effect of film size was larger than that of film shape. Improvements in the sensor structure and the theoretical equation and better evaluation methods are required in order to optimize the film shape and size.

Model-based development (MBD), which utilizes system models to design complex products, has received increasing attention. However, an advanced control system design scheme is required to accurately control the developed products under harsh conditions for practical usage. This paper proposes a control system that integrates a data-driven compensator (DDC) with a model-based control (MBC) system design. The proposed method considers a hierarchical control structure comprising an upstream control system based on the MBC design approach and a downstream control system that includes a plant control loop with a DDC. The proposed system can always maintain the desired performance by driving the MBC based on an ideal model because the downstream control system, DDC, maintains the characteristics of the ideal model even if the plant properties change owing to changes in operational and/or environmental conditions. In this study, the design scheme for unifying models and data in the MBD process is called smart MBD (S-MBD), and the proposed control system design scheme is based on the S-MBD approach. The effectiveness of the proposed hierarchical control system is verified by applying it to a hydraulic excavator.

Autonomous personal mobility vehicles (PMVs), such as electric wheelchairs, are meant to drive through pedestrian spaces. Cooperative pedestrian avoidance by PMVs is necessary in these spaces to maintain smooth traffic. Previous studies suggested that PMVs can avoid pedestrians on a shorter path without compromising each other’s acceptability. This avoidance can be realized by understanding how pedestrians react to the behavior of PMVs and considering those characteristics in the autonomous navigation of PMVs. In this study, the characteristics of pedestrian’s avoidance behavior were investigated. Experiments were conducted to understand the influence of the parameters of the PMV’s avoidance behavior on pedestrians. Results showed that the angular velocity of the PMV during avoidance affects the pedestrian’s avoidance width and tolerance against the PMV’s behavior. These results suggest that it is possible to avoid pedestrians in smaller avoidance spaces by controlling the angular velocity of the PMV and maintaining smooth traffic.

For small-sized mobile machines, moving on snow without sinking is challenging. Snowmobiles are often used to move on snow. However, in smaller-sized machines, the skis attached to the machine become small, so small snow bumps or soft powdered snow quickly cause the entire machine to be buried under the snow. Therefore, it is necessary for small robots moving on snow to give appropriate driving mechanisms for producing propulsive force and snow-floating force. To this end, we propose new driving mechanisms, a “passive wing wheel” and a “spiral screw,” for small mobility services that move on snow without sinking or getting stuck. The passive wing wheel has transformable wing-like paddles, which extend to behave like animal feet and shrink to form a wheel if necessary. The spiral screw is designed to produce propulsive force by pushing snow particles with the screw. Additionally, by combining two sets of screws having opposite threads, they gather snow underneath the body attached to them, which causes floating force. This report also proposes ways to prepare pseudo-snow, which resembles natural snow in hardness, viscosity, and property transformation due to deformation. We conducted experiments to measure the performance of the proposed design in both the pseudo-snow and natural snow. From experiments, we confirmed that the mechanism performs well in terms of propulsion and floating on snow.

A method for working six-legged robots to recover from an overturned state on a flat slope is proposed. The robot rotates around its roll axis, which is parallel to the slope, from an overturned state to a normal state. During this process, the robot supports its body using six legs and maintains as much static balance as possible. This enables a stable overturn recovery. Before recovery, the robot may rotate in the overturned state around its yaw axis, which is vertical to the slope, until the recovery direction becomes lateral to the slope. This reduces the risk of tumbling down the slope. Consequently, the robot can recover from the overturned state on the flat slope of 40° when it recovers almost in the lateral direction.

Generally, deformable objects have large and nonlinear deformations. Because of these characteristics, recognition and estimation of their movement are difficult. Many studies have been conducted aimed at manipulating deformable objects at will. However, they have been focused on situations wherein a rope’s properties are already known from prior experiments. In our previous work, we proposed a motion planning algorithm to manipulate unknown ropes using a robot arm. Our approach considered three steps: motion generation, manipulation, and parameter estimation. By repeating these three steps, a parameterized flexible linear object model that can express the actual rope movements was estimated, and manipulation was realized. However, our previous work was limited to 2D space manipulation. In this paper, we extend our previously proposed method to address casting manipulation in a 3D space. Casting manipulation involves targeting the flexible linear object tips at the desired object. While our previous studies focused solely on two-dimensional manipulation, this work examines the applicability of the same approach in 3D space. Moreover, 3D manipulation using an unknown flexible linear object has never been reported for dynamic manipulation with flexible linear objects. In this work, we show that our proposed method can be used for 3D manipulation.

In this study, we propose an automated excavation system for pneumatic caisson construction using either a single excavator or multiple excavators. The system divides the work area among the excavators and manages the transition of occupied states in shared areas to reduce the risk of collisions. Additionally, the Laplace potential method is employed for path planning to avoid collisions with equipment inside the caisson. The system also includes a mechanism for disposing of soil outside the caisson by dumping it into an earth bucket. To confirm the effectiveness of the proposed method, verification tests were conducted: one using a single excavator in a narrower-than-usual caisson, and another using two excavators in a test field. These tests demonstrated the method’s effectiveness.

Developing a learning model that adapts to changes in the body is critical for improving the flexibility of machine intelligence. During recovery from a controller malfunction, humans use the information obtained from previous experiences. One possible explanation for the recovery process is that information from the remaining controller was transformed and used. Modeling this mechanism will aid in the development of an adaptive motor-learning model capable of quickly recovering from controller malfunctions. We proposed a learning model for explaining the reused information of the remaining controllers in a pair of controllers. Simulations of a pair of upper limbs validated that the learning model could find a simple transformation, such as a reflection between the left and right arms, using optimization.

In the Bonabeau model, chemotaxis, which is observed in social insects, such as ants, was introduced into the movement rules of agents to control the collision frequency between agents, and its effect on the mechanism of hierarchical structure formation was investigated. Like an ant, this chemotactic agent makes stochastic decisions regarding its direction of movement depending on the intensity of its released chemicals. Because of this mechanism, the agent depends on its past location history. It can perform different motions from a random walk (RW) and asymmetric attractive or repulsive interactions with other agents via the diffusion of chemotactic substances. When there is an attractive interaction between these agents, they are more likely to aggregate, which increases the effective density; thus, the disparity in the agent winning ratio is more likely to form than in a conventional model with a RW. However, in the case of repulsive interactions, the agents became more distant from each other, the effective density decreased, and a disparity in the winning ratio was less likely to form. This indicates that the disparity in the winning ratio is tunable owing to the interactions between the introduced chemotactic agents.