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Accurately identifying user needs in terms of assist timing and magnitude presents challenges for wearable power‐assist limb devices. Traditional approaches to gait perception—such as estimating joint angles and walking conditions—often rely on electronic sensors and neural networks, which can compromise wearability and impose high computational demands. Physical reservoir computing (PRC), which utilizes the inherent nonlinearity of physical systems for data processing, offers a promising alternative. This study proposes a novel self‐estimated physical reservoir computing (SEPRC) model that improves traditional PRC models for gait perception using a wearable pneumatic physical reservoir. A core feature of the new model is the self‐estimation structure, wherein the outputs of the physical reservoir are mutually estimated. Experimental evaluations indicate that the SEPRC model outperforms traditional PRC in clustering time‐series reservoir output sequences with the same dimensionality. This enhanced clustering performance is subsequently leveraged in gait perception by incorporating Takagi–Sugeno fuzzy logic for joint angle estimation and a softmax activation function for walking condition recognition. The newly proposed time‐sequence processing approach facilitates the traditional PRC model to achieve higher accuracy in gait perception and greater robustness against the user's walking pattern variations while preserving PRC's hardware simplicity.

Abstract This paper tackles the challenge of accurate depth estimation from monocular laparoscopic images in dynamic surgical environments. The lack of reliable ground truth due to inconsistencies within these images makes this a complex task. Further complicating the learning process is the presence of noise elements like bleeding and smoke. We propose a model learning framework that uses a generic laparoscopic surgery video dataset for training, aimed at achieving precise monocular depth estimation in dynamic surgical settings. The architecture employs binocular disparity confidence information as a self-supervisory signal, along with the disparity information from a stereo laparoscope. Our method ensures robust learning amidst outliers, influenced by tissue deformation, smoke, and surgical instruments, by utilizing a unique loss function. This function adjusts the selection and weighting of depth data for learning based on their given confidence. We trained the model using the Hamlyn Dataset and verified it with Hamlyn Dataset test data and a static dataset. The results show exceptional generalization performance and efficacy for various scene dynamics, laparoscope types, and surgical sites.

Pneumatic artificial muscles (PAMs) are light and soft, and are expected to be applied to gait assistance robots with multiple actuators on the human body. The PAMs can be used as not only actuators, but also sensors to detect the gait phase by using their deformable bodies whose internal pressure changes in response to the wearer’s gait. However, conventional methods to detect the gait phase by PAMs have targeted single point detection in a gait phase and used for only ON/OFF control of the PAM actuators. In this study, we proposed an algorithm to estimate the postural state of the wearer, especially the state of both hip joints, from the internal pressure information of the PAMs with a small amount of calculation by using clustering method, and succeeded in controlling the PAMs’ pressure continuously based on this algorithm. The effectiveness of the proposed control method was verified through gait-assistive experiments using a treadmill. We measured the electromyogram of the adductor longus muscle under 3 subjects and a one-sided significant difference test was performed. As a result, we confirmed significant differences at the 1% significance level for 2 subjects and at the 10% significance level for the remaining subject, allowing us to evaluate the effectiveness of the proposed PAM control strategy.

In thoraco-laparoscopic surgery, the identification of a bleeding source is recognized as one of the most important issues with hemostasis during operation. However, previously proposed techniques are only capable of detecting an approximate bleeding region, not the precise location itself. To develop a system which can accurately localize a bleeding source, post-bleeding images and their corresponding bleeding source information may be required. However, to pinpoint bleeding sources from actual thoraco-laparoscopic surgery images is no easy task even for an experienced surgeon. In previous studies, a surgeon could only provide rectangular region information around a bleeding source. To address the problem, we have developed a mimicking device that simulates bleeding from a vessel on an artificial organ for obtaining bleeding images and precise bleeding source information at the same time. Using this information, we constructed a Generator that can associate a bleeding image with the corresponding bleeding source by using Pix2Pix and created a “bleeding alert map (BAM)” which concerns the Predicted intensity of bleeding source in the endoscopic view. The Generator successfully created BAMs from ex vivo lung bleeding images as well as actual organ bleeding images captured in thoracoscopic surgeries. The results showed that the BAM Generator constructed only by using the data from the mimicking device was effective in processing bleeding images from actual organs to identify bleeding sources. The proposed system may be utilized during endoscopic surgery to present a BAM which carries important information for hemostasis.

A gait training method that can adjust the target leg load is required to meet the varied demands of trainees. In this study, we propose a running training method using a soft gait-assistive suit—a power-assistive device that adds an external force to the leg of a trainee by actuating a pneumatic artificial muscle (PAM). Herein, the assisting force is applied to the leg of the trainee so that muscle activity is reduced throughout the gait cycle. We detected the terminal stance phase as an appropriate assist timing from the pressure information, and the detected timing was used for the PAM control. We conducted a verification experiment of the PAM pressure variation. In a running mode of 7.0 to 10.0 km/h, all data showed that the PAM at the calf front has better detectability than that at the calf back, which is used in conventional detection methods. The experimental results confirmed that the PAM for gait phase detection should be positioned at the calf front during running. The effectiveness of the proposed running training method was verified through experiments on four trainees using a treadmill. The leg electromyogram of the trainees was measured and changes observed. Accordingly, 14 out of 16 channels of the four trainees showed a significant difference, and the electromyograms decreased by the suit assistance in the range of −1.4 to − 80.3% maximum voluntary contraction. Thus, the experimental results quantitatively confirmed that the proposed running assistance method is effective in reducing muscular activity.

Considering the widespread use of surgical robots in recent years, computer-assisted surgery is becoming significantly popular. Because the automation of surgical tasks without human intervention remains complex owing to individual patient differences, a human cooperative control is proposed in this study. A system in which an operator manipulates one surgical instrument to insert a suture needle was developed, along with another surgical instrument that automatically pulls out the needle from the operated instrument. In the proposed method, YOLOv3 and a standard convolutional neural network (CNN) are used to estimate the penetration and pull state of the needle. An image-based state estimator classifies the state regardless of the stiffness of the object to which the suture needle is inserted. Furthermore, after the pull state is detected, despite a failure of the needle pulling, the position can be corrected and the automated surgical instrument can approach the needle again. Experiments on human cooperative control demonstrated the effectiveness of state estimation using the proposed method. In addition, the failure of grasping observed in a previous study caused by the needle angle error was reduced.

Various applications using pneumatic artificial muscles (PAMs) have been developed in recent years. When the pressure of the PAM is controlled, it is desirable to position a pressure sensor at the control port of a servo valve through a pipeline for improvement in usability, environment resistance, and elimination of mechanical complexity. However, the responsiveness of the pressure control worsens due to the volume and pressure losses through the pipeline. In this paper, we propose a pneumatic circuit model to compensate for such losses through the pipeline and a method to estimate the pressure at the PAM inlet. The proposed method improves the responsiveness of the pressure control by positioning a pressure sensor at the control port of the servo valve. We developed an experimental apparatus that simulates the mechanism of our power assist robot, and it was used for performance evaluation experiments of the conventional and proposed pressure control methods. When a sinusoidal pressure target value with a frequency of 1.5 Hz and load mass of 8 kg was fed as input, the errors between the measured and target values in the conventional and proposed control were approximately 45 and 20 kPa, respectively. The reduced error confirmed that the responsiveness of the pressure control was improved by the proposed method.

Three-dimensional (3D) shape acquisition has been widely introduced to enrich quantitative analysis with the combination of object shape and texture, for example, surface roughness evaluation in industry and gastrointestinal endoscopy in medicine. Shape from focus is a promising technique to measure substance surfaces in 3D space because no occlusion problem appears in principle, as does with stereo shape measurement, which is another commonly used option. We have been developing endoscopic shape measurement devices and shape reconstruction algorithms. In this paper, we propose a mechanism for driving an image sensor reciprocated for the shape from focus of 3D shape measurement in monocular endoscopy. It uses a stepping motor and a planar-end cam, which transforms the motor rotation to imaging sensor reciprocation, to implement the shape from focus of 3D shape measurement in endoscopy. We test and discuss the device in terms of its driving accuracy and application feasibility for endoscopic 3D shape measurement.

Tunable stiffness mechanisms can increase the noninvasiveness and stability of organ manipulation in laparoscopic liver resection. We have developed an organ-grasping device using beam-shaped tunable stiffness mechanism. Increasing the change ratio of stiffness will improve the performance of the device by offering high flexibility when adhering to the liver surface and high rigidity during the manipulation of the liver; however, optimal design of the beam has not been investigated. In this study, we investigate the wavy structure shape of the device that enhances the change in the ratio of stiffness. To increase the stiffness in a high-stiffness state, we used principal stress lines in the device to design the edge curve of the wavy shape material in the beams. We also investigated the arrangement of the wavy shape to decrease the stiffness in a low-stiffness state. Simulation using finite element method showed that the change ratio of stiffness was improved up to 13.0 by the new wavy shape arranged with the uniformly thick bottom of the waves.

Understanding how we consciously experience our bodies is a fundamental issue in cognitive neuroscience. Two fundamental components of this are the sense of body ownership (the experience of the body as one's own) and the sense of agency (the feeling of control over one's bodily actions). These constructs have been used to investigate the incorporation of prostheses. To date, however, no evidence has been provided showing whether representations of ownership and agency in amputees are altered when operating a robotic prosthesis. Here we investigated a robotic arm using myoelectric control, for which the user varied the joint position continuously, in a rubber hand illusion task. Fifteen able-bodied participants and three trans-radial amputees were instructed to contract their wrist flexors/extensors alternately, and to watch the robotic arm move. The sense of ownership in both groups was extended to the robotic arm when the wrists of the real and robotic arm were flexed/extended synchronously, with the effect being smaller when they moved in opposite directions. Both groups also experienced a sense of agency over the robotic arm. These results suggest that these experimental settings induced successful incorporation of the prosthesis, at least for the amputees who took part in the present study.

Gaze-independent brain computer interfaces (BCIs) are a potential communication tool for persons with paralysis. This study applies affective auditory stimuli to investigate their effects using a P300 BCI. Fifteen able-bodied participants operated the P300 BCI, with positive and negative affective sounds (PA: a meowing cat sound, NA: a screaming cat sound). Permuted stimuli of the positive and negative affective sounds (permuted-PA, permuted-NA) were also used for comparison. Electroencephalography data was collected, and offline classification accuracies were compared. We used a visual analog scale (VAS) to measure positive and negative affective feelings in the participants. The mean classification accuracies were 84.7% for PA and 67.3% for permuted-PA, while the VAS scores were 58.5 for PA and −12.1 for permuted-PA. The positive affective stimulus showed significantly higher accuracy and VAS scores than the negative affective stimulus. In contrast, mean classification accuracies were 77.3% for NA and 76.0% for permuted-NA, while the VAS scores were −50.0 for NA and −39.2 for permuted NA, which are not significantly different. We determined that a positive affective stimulus with accompanying positive affective feelings significantly improved BCI accuracy. Additionally, an ALS patient achieved 90% online classification accuracy. These results suggest that affective stimuli may be useful for preparing a practical auditory BCI system for patients with disabilities.

Power assistive devices have been developed in recent years. To detect the wearer’s motion, conventional devices require users to wear sensors. However, wearing many sensors increases the wearing time, and usability of the device will become worse. We developed a soft gait assistive suit actuated by pneumatic artificial rubber muscles (PARMs) and proposed its control method. The proposed suit is easy to wear because the attachment unit does not have any electrical sensors that need to be attached to the trainee’s body. A target application is forward walking exercise on a treadmill. The control unit detects the pre-swing phase in the gait cycle using the pressure information in the calf back PARMs. After the detection, the suit assists the trainee’s leg motion. The assist force is generated by the controlled PARM pressure, and the pressure input time is changed appropriately considering the gait cycle time. We conducted walking experiments; (1) verifies the proposed control method works correctly, and (2) verifies whether the gait assistive suit is effective for decreasing muscular activity. Finally, we confirmed that the accurate phase detection can be achieved by using the proposed control method, and the suit can reduce muscular activity of the trainee’s leg.

A cylindrical soft actuator is suitable for applications in which pneumatic or hydraulic cylinders are conventionally used. In this report, we discuss the force output model of a spring-reinforced-type cylindrical soft actuator. This type of actuator outputs a larger force than the air pressure multiplied by the pressure-receiving area. We construct a quasi-static model to explore the reason for this phenomenon, based on the strength of materials. A thick-walled cylinder model with three boundary conditions was defined and analyzed. The model indicates that the rubber cylinder itself transmits pneumatic pressure and contributes to the output force. We also modeled the relationship between the pressure and the elongation of the soft actuator. Experiments were conducted to evaluate the proposed models.