Abdominal Multi-Organ Segmentation Based on Feature Pyramid Network and Spatial Recurrent Neural Network

As recent advances in AI are causing the decline of conventional diagnostic methods, the realization of end-to-end diagnosis is fast approaching. Ultrasound image segmentation is an important step in the diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we take two inherent features of ultrasound images into consideration: (1) different organs and tissues vary in spatial sizes, (2) the anatomical structures inside human body form a relatively constant spatial relationship. Based on those two ideas, we propose a new image segmentation model combining Feature Pyramid Network (FPN) and Spatial Recurrent Neural Network (SRNN). We discuss why we use FPN to extract anatomical structures of different scales and how SRNN is implemented to extract the spatial context features in abdominal ultrasound images.Comment: IFAC World Congress 2023 pape

Support for ECHONET-based smart home environments in the universAAL ecosystem

With the advent of information and communication technology, many Ambient Assisted Living (AAL) solutions are being proposed to increase the quality of life of elderly people and reduce health and social care costs. Among these AAL solutions, universAAL seems to be the most promising platform for easy and economical development of AAL services. However, in its current state, the platform is incompatible with smart home systems which are based on the ECHONET standard. This paper presents the bridging between the universAAL and ECHONET standards through a technical point of view and thereby enables AAL services for ECHONET-based smart home environments

Adaptive Navigation Control for Swarms of Autonomous Mobile Robots

This paper was devoted to developing a new and general coordinated adaptive navigation scheme for large-scale mobile robot swarms adapting to geographically constrained environments. Our distributed solution approach was built on the following assumptions: anonymity, disagreement on common coordinate systems, no pre-selected leader, and no direct communication. The proposed adaptive navigation was largely composed of four functions, commonly relying on dynamic neighbor selection and local interaction. When each robot found itself what situation it was in, individual appropriate ranges for neighbor selection were defined within its limited sensing boundary and the robots properly selected their neighbors in the limited range. Through local interactions with the neighbors, each robot could maintain a uniform distance to its neighbors, and adapt their direction of heading and geometric shape. More specifically, under the proposed adaptive navigation, a group of robots could be trapped in a dead-end passage,but they merge with an adjacent group to emergently escape from the dead-end passage. Furthermore, we verified the effectiveness of the proposed strategy using our in-housesimulator. The simulation results clearly demonstrated that the proposed algorithm is a simple yet robust approach to autonomous navigation of robot swarms in highlyclutteredenvironments. Since our algorithm is local and completely scalable to any size, it is easily implementable on a wide variety of resource-constrained mobile robots andplatforms. Our adaptive navigation control for mobile robot swarms is expected to be used in many applications ranging from examination and assessment of hazardous environments to domestic applications

The CARESSES study protocol: testing and evaluating culturally competent socially assistive robots among older adults residing in long term care homes through a controlled experimental trial

Background : This article describes the design of an intervention study that focuses on whether and to what degree culturally competent social robots can improve health and well-being related outcomes among older adults residing long-term care homes. The trial forms the final stage of the international, multidisciplinary CARESSES project aimed at designing, developing and evaluating culturally competent robots that can assist older people according to the culture of the individual they are supporting. The importance of cultural competence has been demonstrated in previous nursing literature to be key towards improving health outcomes among patients. Method : This study employed a mixed-method, single-blind, parallel-group controlled before-and-after experimental trial design that took place in England and Japan. It aimed to recruit 45 residents of long-term care homes aged ≥65 years, possess sufficient cognitive and physical health and who self-identify with the English, Indian or Japanese culture (n = 15 each). Participants were allocated to either the experimental group, control group 1 or control group 2 (all n = 15). Those allocated to the experimental group or control group 1 received a Pepper robot programmed with the CARESSES culturally competent artificial intelligence (experimental group) or a limited version of this software (control group 1) for 18 h across 2 weeks. Participants in control group 2 did not receive a robot and continued to receive care as usual. Participants could also nominate their informal carer(s) to participate. Quantitative data collection occurred at baseline, after 1 week of use, and after 2 weeks of use with the latter time-point also including qualitative semi-structured interviews that explored their experience and perceptions further. Quantitative outcomes of interest included perceptions of robotic cultural competence, health-related quality of life, loneliness, user satisfaction, attitudes towards robots and caregiver burden. Discussion : This trial adds to the current preliminary and limited pool of evidence regarding the benefits of socially assistive robots for older adults which to date indicates considerable potential for improving outcomes. It is the first to assess whether and to what extent cultural competence carries importance in generating improvements to well-being

Pre-Engraftment Syndrome after Unrelated Cord Blood Transplantation: A Predictor of Engraftment and Acute Graft-versus-Host Disease

AbstractPre-engraftment syndrome (PES) is poorly characterized, and its clinical significance and the prognostic impact after unrelated cord blood transplantation (CBT) are unclear. To address these issues, we retrospectively analyzed the incidence, risk factors, and clinical outcomes of PES in unrelated CBT recipients. Data of 381 patients who received unrelated CBT from 18 medical centers in Korea were reviewed. PES was defined as unexplained fever >38.3°C not associated with infection, and/or unexplained skin rash with or without evidence of fluid retention before neutrophil recovery. PES developed in 102 patients (26.8%) at a median of 7 days after CBT. Of these patients, 74 patients (72.5%) received intravenous corticosteroid at a median dose of 1 mg/kg/day, and of these, 95% showed clinical improvement. Risk factors for developing PES included low risk disease, myeloablative conditioning, graft-versus-host disease (GVHD) prophylaxis without methotrexate or corticosteroid, and >5.43 x 107/kg infused nucleated cells. Absence of PES was one of the risk factors for graft failure in multivariate analysis. The cumulative incidence of grade II to grade IV acute GVHD by 100 days after CBT was higher in patients with PES than in those without PES (56.0% versus 34.4%, P < .01). PES was not associated with chronic GVHD, treatment-related mortality, relapse, or overall survival. PES seems to be common after CBT and may be associated with enhanced engraftment without significant morbidity

Distributed Autonomous Robotic Systems : the 12th International Symposium

This volume of proceedings includes 32 original contributions presented at the 12th International Symposium on Distributed Autonomous Robotic Systems (DARS 2014), held in November 2014. The selected papers in this volume are authored by leading researchers from Asia, Europe, and the Americas, thereby providing a broad coverage and perspective of the state-of-the-art technologies, algorithms, system architectures, and applications in distributed robotic systems.

Low-Cost Dual Rotating Infrared Sensor for Mobile Robot Swarm Applications

This paper presents a novel low-cost position detection prototype from practical design to implementation of its control schemes. This prototype is designed to provide mobile robot swarms with advanced sensing capabilities in an efficient, cost-effective way. From the observation of bats’ foraging behaviors, the prototype with a particular emphasis on variable rotation range and speed, as well as 360° observation capability has been developed. The prototype also aims at giving each robot reliable information about identification of neighboring robots from objects and their positions. For this purpose, an observation algorithm-based sensor is proposed. The implementation details are explained, and the effectiveness of the control schemes is verified through extensive experiments. The sensor provides real-time location of stationary targets positioned 100 cm away within an average error of 2.6 cm. Moreover, experimental results show that the prototype observation capability can be quite satisfactory for practical use of mobile robot swarms

RFID-based mobile robot guidance to a stationary target

Retrieving accurate location information about an object in real-time, as well as any general information pertinent to the object, is a key to enabling a robot to perform a task in cluttered, dynamically changing environment. In this paper, we address a novel technique for the guidance of mobile robots to help them identify, locate, and approach a target in our daily environments. To this end, we propose a standard for the use of radio-frequency identification (RFID) systems and develop a prototype that can be easily installed in existing mobile robots. Specifically, when an RF signal is transmitted from an RF transponder, the proposed RFID system reads the transponder-encoded data and simultaneously picks up the direction of the transponder using the received signal strength pattern. Based on the angle of signal arrival, we develop the guidance strategies that enable a robot to find its way to the transponder position. Moreover, to cope with multi-path reflection and unexpected distortions of the signals resulted from environmental effects, we present several algorithms for reconstructing the signals. We demonstrate that an off-the-self mobile robot equipped with the proposed system locates and approaches a stationary target object. Experimental results show that the accuracy of the proposed system operating at a frequency of 315 MHz falls within a reasonable range in our normal office environment