The long-term goal of this study is a training system that can simulate medical cases and advise physicians based on quantitative evaluation of neonatal resuscitation. In this paper, we designed and manufactured a neonatal airway management simulator for quantitative evaluation of tracheal intubation. This robotic simulator is equipped with 25 sensors of 6 types, which detect motions that lead to complications, inside the manikin replicated a neonate. A performance experiment of the developed sensor and an evaluation experiment with physicians were conducted. We observed that an erroneous operation in the laryngoscopy can be detected by the sensors in our simulator.

In recent years, awareness of improving the quality of medical care has increased. We focused on neonatal resuscitation, which is difficult to train clinically, and our long-term goal is a neonatal resuscitation training system that can simulate cases and can feedback interactively based on quantitative evaluation of the procedures. In this study, we developed the neonatal simulator that has a respiratory movement simulating mechanism and a chest compression measurement mechanism. Through an interview with neonatologists, the fidelity of respiratory movements and the possibility of evaluating chest compression with the developed simulator were assessed.

In this study, we focused on the automatic scoring of medical clinical abilities. The objective clinical ability tests that all undergraduate students take before starting clinical practice were considered. As these tests evaluate practical skills, there is a problem that the learning method is poor compared to the examination of other lectures. Therefore, in this study, we recorded the voice of a student examining a simulated patient using a microphone. We constructed a system comprising a speech recognition module and a scoring system that performed automatic scoring by checking against a prepared example answer. This system was evaluated by medical doctors.

This study aims to develop a state transition model and speech recognition module for application to a whole-body patient simulator for scenario based training (SBT) of neurological examination procedures. These procedures are very important for the early identification of neurological system disorders. In neurological examinations, the doctor selects procedures by situation of patients, interacts with the patient and performs a series of medical procedures to judge the site of nerve disorders and lesions. SBT is one type of training that doctor performs to be based on scenario. Scenario is patient situation and examination procedure. SBT is used for the training of such examinations. SBT is often performed using simulated patients (SPs); however, the number of SPs is not sufficient and SPs cannot adequately reproduce diseases. Therefore, we integrated a state transition model with a commercial speech recognition software and developed a dialogue system for SBT. First, we customized the grammatical rules and the different words that the software can recognize. By doing so, the recognition rate improved to about 90%. Second, we developed a probability model for the state transition model. In neurological examinations, patient pause is limited and an order of procedures is generally decided. We developed a state transition model including probability model with the customized speech recognition module.

Neurologic examination takes an important role in the physical examination. By now, several methods have been carried out for medical training which bring the benefits for trainee to master the skills and accumulate experiences. However, because of the limits of these methods, the training effectiveness is limited. With the developments of technology, more and more simulators have been launched to improve medical training effectiveness. However, most of these simulators only focus on mimicking the symptoms, not simulating the pathology of diseases. In this paper, we propose an improved motor nerve model which is used in the elbow robot named WKE-2(Waseda Kyotokagaku Elbow Robot No.2). This robot is designed to simulate the disorders of motor nerves to give the trainee a full training on the elbow examination. The motor nerve model mimics the real motor nerve structure. And the functions of each part are designed from analysis of the real functions of each organ. In this robot, the effects of motor nerve system, and various symptoms related to the examination of elbow force, biceps tendon reflex, involuntary action are simulated. Making use of this robot, a systematic training on the skills as well as the understanding of knowledge is provided. Finally, several experiments are performed to verify our proposed system. The experimental results lead to the consideration that the approach is worth following in further research.