In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000–1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.

[abstFig src='/00280005/06.jpg' width='300' text='Developed device' ] According to a worldwide WHO survey, about one-third of people at the age of 65 or older experience at least one fall a year, which may result in a severe injury. Meanwhile, the population of the developed world is increasingly aging, and fall incidents can be therefore considered as a global problem. The causes of falls include the weakening of the tibialis anterior and gastrocnemius muscles that respectively play important roles in the dorsal and plantar flexion of the foot, and deterioration of the functions necessary to recover balance from perturbations during gait. Such dysfunctions are treated with rehabilitation provided by physical therapists and with special gait training in which the patient is subjected to perturbations. Although devices for rehabilitation and gait training have been developed, they are problematic since they only allow the ankle joint to move at a low number of degrees of freedom (DOF). In this study, we developed an ankle foot orthosis to provide six-DOF control of the ankle joint using a parallel link mechanism known as a Stewart platform. The Stewart platform construction makes it possible to provide six-DOF control. Since the ankle foot orthosis can be applied to walking, it can assist walking or gait training. In one of our prior studies, we proposed a force control method for the device, and verified its accuracy. In the present study, we improved the attachment method and introduced a pressure sensor to the previous version of the device to allow implementation of a new method that enables control adapted to the human gait. In addition, we conducted four experiments to verify whether it is possible to reproduce the physical therapist’s rehabilitation manipulations without limiting the ankle joint’s DOF, provide arbitrary walking assist action, and impart perturbations to the subject during gait. The first experiment verified the device’s accuracy in reproducing motion, the second confirmed the dispersion of the reproduced motion, the third assessed the walking-assist performance to prevent trips, and the fourth ascertained whether it is possible to make the subject lose balance by the imparted perturbation. The results demonstrated that the motions can be reproduced with high accuracy and with low dispersion and that the ankle joint motions can be controlled adaptively to fit the subject’s gait, suggesting the usefulness of the proposed device.

Currently, most rescue robots are mainly teleoperated and integrate some level of autonomy to reduce the operator’s workload, allowing them to focus on the primary mission tasks. One of the main causes of mission failure are human errors and increasing the robot’s autonomy can increase the probability of success. For this reason, in this work, a stair detection and characterization pipeline is presented. The pipeline is tested on a differential drive robot using the ROS middleware, YOLOv4-tiny and a region growing based clustering algorithm. The pipeline’s staircase detector was implemented using the Neural Compute Engines (NCEs) of the OpenCV AI Kit with Depth (OAK-D) RGB-D camera, which allowed the implementation using the robot’s computer without a GPU and, thus, could be implemented in similar robots to increase autonomy. Furthermore, by using this pipeline we were able to implement a Fuzzy controller that allows the robot to align itself, autonomously, with the staircase. Our work can be used in different robots running the ROS middleware and can increase autonomy, allowing the operator to focus on the primary mission tasks. Furthermore, due to the design of the pipeline, it can be used with different types of RGB-D cameras, including those that generate noisy point clouds from low disparity depth images.

Gastric cancer (GC) is a commonly diagnosed cancer worldwide and one of the leading causes of cancer-related mortality.¹ Gastrectomy is a curative treatment for GC.² The effect of gastric remnant volume on the postoperative quality of life and nutritional status has been recognized. Furthermore, organ-sparing surgery is increasingly preferred.^{3 – 5} Japanese GC treatment guidelines (5th edition) recommend a proximal resection margin of at least 3 cm for tumors $\ge \mathrm{T}2$ in the case of an expansive growth pattern, whereas a proximal resection margin of at least 5 cm is required in the case of an infiltrative growth pattern.⁶ The large margin in the infiltrative growth patterns is attributed to the extensive submucosal or deeper layer invasions. Hence, if the extent of the non-exposed cancer invasion is identified, the stomach can be resected more appropriately. However, assessing the extent of the tumor and determining the appropriate resection margins during surgery remains difficult. Hence, several methods, including intraoperative endoscopy and endoscopic tattooing or clipping, that can identify the tumor extent and determine the appropriate surgical margins exist.^{7 – 10} However, the endoscopic determination of the extent of the GC that is not exposed to the mucosal surface remains difficult (e.g., the infiltrative growth pattern¹⁰).

Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies

A recurrent-neural-network-based deep-learning model was developed to estimate the three-axis joint angles of an entire body with 17 bones during walking from three inertial measurement units (IMUs) — one each on the left wrist and heels. In this model, the acceleration and angular velocity of the previous 49 frames and current frame were considered as inputs. The architecture comprises two hidden layers (two long short-term memory layers) and a dense layer. The performance of the model was evaluated using the National Institute of Advanced Industrial Science and Technology (AIST) Gait Database 2019 public dataset. Consequently, the root mean squared error of each joint angle was less than 12.28°. A comparison of the estimation results of the same model with IMUs at the pelvis and shanks revealed that the proposed model is advantageous in terms of balanced measurement accuracy and ease of use in realizing whole-body motion capture. Although the accuracy of the model was better than those of previous models in estimating the general whole-body motion from six IMUs, it was worse than that of a previous model in estimating only the lower-limb motion from three IMUs attached to the pelvis and shanks during walking. In the proposed model, IMUs are attached to the left wrist and heels, and whole-body motion can be easily captured using a smartwatch and smart shoes.