Many medical accidents and incidents occurred due to communication errors. To avoid such incidents, in this paper, we propose a system for determining communication errors. Especially, we propose a model that can be applied to multi-layered or chained situations. First, we provide an overview of communication errors in nursing activities. Then we describe the warp and woof model for nursing task that was proposed by Harada and considers multi-layered or chained situations. Next we describe a system for determining communication errors based on the warp and woof model for nursing task. The system is capable of generating nursing activity diagrams semi-automatically and compiles necessary nursing activities. We also propose a prototype tagging of the nursing corpus for an effective generation of the diagrams. Then we combine the diagram generation with the Kamishibai KeyGraph to determine possible points of the hidden or potential factors of communication errors.

We propose a noise suppression method based on multi-model compositions using particle filtering. In real environments, input speech for speech recognition includes many kinds of noise signals. For such noisy speech, we previously proposed Multi-model Noise Suppression (MM-NS) that uses many kinds of noise models and their compositions obtained from training data. However, since MM-NS only uses the static property of noise models, handling unknown noise distributions is difficult. We introduce a particle filter into MM-NS. The distributions of noise models are used as prior distributions of particle filtering to increase the accuracy of the estimation of noise signals for input data. We evaluated this method using the E-Nightingale task, which contains voice memoranda spoken by nurses during actual work at hospitals. The proposed method outperformed the original MM-NS.

Nurses are with the primary mission to provide quality services to patients up to 24 hours a day. In general, different nurses (especially between nurse experts and novices) handle the pre-assigned nursing cares in different ways, which directly affect the quality of nursing cares. In this respect, it is necessary to quantitatively elucidate the nursespsila action rules for effectively nursing instruction and quantitative evaluation of staffing levels. In this paper, we will analyze the daily nursing cares to quantitatively illustrate the implicit action rules of nurses on their provision of cares from the viewpoint of scheduling. Unlike the conventional methods mainly based on the interviews/dialogues, we hypothetically model the nursespsila action rules as some traditional dispatching rules, and elucidate the nursespsila action rules by evaluating the similarities of the actual nursing care schedules with planned nursing care ones. By analyzing several actual observed nursing cares, we quantitatively elucidate the action rules of veterans and novices, and furthermore, we compared their differences on the rules.

In most Japanese hospitals, different nurses handle the pre-assigned nursing cares in different ways, which directly affect the quality of nursing cares. To our knowledge, there has been less attention on ensuring that nurses provide nursing cares in a timely and accurate fashion. Consequently, in this paper, considering the similarities to the traditional job shop scheduling problems, we will model the daily nursing care scheduling problems and propose an efficient scheduling method based on simulated annealing algorithm. By iteratively local searching based on simulated annealing: (1) permutating the tasks from one nurse to another and (2) permutating the sub tasks handled by a nurse from its original position to another new one, the proposed method is evaluated to be applicable to the nursing care scheduling problems (providing comprehensive, coordinated and cost effective nursing cares to patients).

This paper presents robust speech recognition using a noise suppression method based on multi-model compositions and multi-pass search. In real environments, many kinds of noise signals exists, and input speech for speech recognition systems include them. Our task in the E-Nightingale project is speech recognition of voice memoranda spoken by nurses during actual work at hospitals. To obtain good recognized candidates, suppressing many kinds of noise signals at once to find target speech is important. First, before noise suppression, to find speech and noise label sequences, we introduce multi-pass search with acoustic models including many kinds of noise models and their compositions, their n-gram models, and their lexicon. Second, noise suppression based on models is performed using the multiple composite models selected by recognized label sequences with time alignments. We evaluated this approach using the E-Nightingale task, and the proposed method outperformed the conventional method.

We present a practical 3D reconstruction method that guarantees robust visual hull construction in real environments where segmentation errors and occlusion exist. The proposed method consists of foreground extraction and reliability-based shape-from-silhouette, and they are connected by the intra-/inter-silhouette reliabilities. In foreground extraction, all regions are classified into four categories based on their intra-reliabilities. Then the reliability-based shape-from-silhouette technique reconstructs a visual hull by carving a 3D space based on the intra-/inter-silhouette reliabilities. The proposed method provides a reliable visual hull in real environments without much increment of the system complexity compared with conventional systems.

This paper investigates human behavior around an interactive robot at a science museum. To develop a communication robot that works in daily environments, it is important to investigate the available information from a robot about people's behavior. Such information will enable the robot to predict people's behavior so that the robot can optimize its interactive behavior. We analyzed visitor behavior toward a simple interactive robot exhibited at a science museum in relation to information from sound level and range sensors. We discovered factors that influence the way people approach, maintain distance, and interact both physically and verbally with the robot. This enabled us to extract meaningful information from the sensory information and apply it to communication robots.

The aim of this paper is to clarify the relationship between the expressive strengths of gestures and voice for embodied and personified interfaces. We conduct perceptual tests using a puppet interface, while controlling singing-voice expressions, to empirically determine the naturalness and strength of various combinations of gesture and voice. The results show that (1) the strength of cross-modal perception is affected more by gestural expression than by the expressions of a singing voice, and (2) the appropriateness of cross-modal perception is affected by expressive combinations between singing voice and gestures in personified expressions. As a promising solution, we propose balancing a singing voice and gestural expressions by expanding and correcting the width and shape of the curve of expressive strength in the singing voice.

We believe that robots will take an active part in our daily lives in the near future. Once this time arrives, robots will be expected to have the social skills necessary for interacting with people in addition to the ability to carry out their own tasks. We call an interaction that increases familiarity and makes communication smoother a "social interaction." We have recently developed a human-size humanoid, called "Robovie-IV," which will have the ability to interact with people in their daily lives. This paper discusses the design requirements for humanrobot interaction by a communication robot, then introduces an overview of Robovie-IV's hardware and software architectures. There are also preliminary results of a daily experiment that we are currently conducting with Robovie-IV in our office.

We have constructed a sensor network in a real hospital environment to develop a system that prevents medical accidents by monitoring nursing activities. The network has been carefully designed to be dependable for practical purposes even in a network-unready environment with considerations of safety and stability as well as the consistency of the obtained sensor data. This paper describes the design and implementation of our sensor network. Through an experiment of about one week, we confirmed that our design performs well in a practical environment and obtains consistent data among the sensors worn by nurses and installed in the environment. Since a hospital has strict limitations on the use of sensor network equipment, the design described in this paper provides a practical solution for the construction of sensor networks in many indoor applications

We have developed a method for synthesizing a singing voice by gradually changing the musical expression based on speech morphing. This paper shows the advantages of this method in comparison with the approach of binary discrete transformation between two expressions, confirmed by statistical analyses of perception tests. In order to synthesize different expressional strengths of a singing voice, a "normal" (without expression) voice of a particular singer is used as the base of morphing, and three different expressions, "dark," "whispery" and "wet," are used as the target. Through our experiments, we confirmed i) the proposed morphing algorithm effectively interpolates the expressional strength of a singing voice, ii) an approximate equation of the perceptual sense can be used to calculate the morph ratio at a perceptually linear interval, and iii) our gradual transformation method can generate a natural singing voice from the interpolation of two different expressions.

An interactive exhibition guide robot has been developed, that is integrated with ubiquitous sensors. The robot utilizes its human-like body properties to perform human-like gestures such as eye-contact and pointing. The ubiquitous sensors are embedded in an exhibition room or attached to people in the room, and they provide information not only for the robot's interactive guide but also for the development of the robot. The results of a two-days exhibition experiment show that the robot attracted visitors and successfully guided them in the environment. Moreover, the obtained interaction statistics, including video from the ubiquitous sensors, prove to be useful for analyzing the interaction between the robot and visitors, and this implies that the use of such interaction data is a promising approach for developing interactive robots.

This study clarified that a robot tracking a user can influence user decision-making. Two types of robot were used and the robots tracked or did not track the user. A laboratory experiment (n = 118) tested differences in selection ratios corresponding to a color name recommended by the robots. Subjects selected the recommended color name significantly more often when the robot tracked subjects' faces than in the no-recommendation situation, however, when the robot did not track subjects' faces with recommendation, there was no significant difference. The physiological data also showed that the tracking condition generated higher levels of arousal than the non-tracking condition. This suggests that tracking the user's face influences user decision-making.

We propose an intelligent environment for "co-experience communication" consisting of stationary and wearable sensors, and communication robots. The environment captures many events collaboratively from multiple viewpoints using a combination of stationary sensors that track people's behaviors and interactions, and wearable sensors that observe the viewpoints and areas of interest of individual persons. Humanoid communication robots also play a role as assistants to help visitors pay attention to significant events. First of all, the basic framework of the environment is described. Next, our developing exhibition room is introduced as an example of an intelligent environment.

This paper proposes a concept of robots called ubiquitous intelligent robot and introduces our research efforts toward establishing its fundamental technology. A ubiquitous intelligent robot is a kind of embodied intelligence that encompasses the sphere of people's activities, records these activities, and supports people. Under this concept, we have developed an intelligent environment for capturing our experiences and a wearable event recording system for medical care. These developments mainly focus on recording people's activities along with their environments.

This paper presents a unification-based approach to Japanese honorifics based on a version of HPSG (Head-driven Phrase Structure Grammar). Utterance parsing is based on lexical specifications of each lexical item, including honorifics, and a few general PSG rules using a parser capable of unifying cyclic feature structures. It is shown that the possible word orders of Japanese honorific predicate constituents can be automatically deduced in the proposed framework without independently specifying them. Discourse Information Change Rules (DICRs) that allow resolving a class of anaphors in honorific contexts are also formulated.