As the primary choice for treating insomnia, Cognitive Behavioral Therapy for Insomnia (CBT-I) is recommended. However, the availability of therapists trained in CBT-I is limited worldwide, and particularly in Japan, patients often face challenges in accessing CBT-I even when they express a desire for it.
We are currently investigating the effectiveness and feasibility of providing CBT-I remotely to address this issue. This involves connecting medical institutions with trained therapists offering CBT-I via teleconferencing, allowing patients to attend sleep clinics remotely. Additionally, we aim to elucidate the mechanisms of CBT-I by capturing factors such as gaze, facial expressions, voice, and physiological indicators during therapy sessions.

Providing Cognitive Behavioral Therapy for Insomnia (CBT-I) remotely through teleconferencing systems or telephone is a crucial study to enhance access to CBT-I, especially considering the challenges faced by those who may find digital technology daunting.
In this research, we target insomnia patients who have consulted their primary care physicians, where in-person CBT-I was difficult. Patients can choose between CBT-I via teleconferencing or CBT-I via telephone, based on their preferences. The study aims to verify the feasibility of implementing remote CBT-I and address the potential challenges associated with the digital delivery of CBT-I for individuals less comfortable with technology.

In recent years, there has been a growing demand for on-demand training in acquiring medical communication skills, as opposed to traditional face-to-face training. This research aims to investigate the effectiveness of on-demand medical communication training in enhancing the communication skills of interpersonal support professionals.
Additionally, the study seeks to develop a program that can automatically assess the empathetic capabilities of these professionals by extracting empathy from their interactions with simulated cases.

It has become evident that the support humans seek from each other and the support they desire from robots differ. In this study, we are conducting research focused on children who have sought treatment in child psychiatry to explore the differences in support needs between interactions with humans and robots.
Simultaneously, we are developing autonomous agent robots designed to provide empathetic support to individuals.

The characteristics of children's sleep vary significantly based on developmental stages. Moreover, children's sleep is closely intertwined with caregivers' sleep patterns and mental health.
This study investigates the relationship between sleep issues in children, parenting behaviors, and mental health problems in caregivers. We are also exploring effective support methods to address and improve sleep and mental health issues for children and their caregivers.

Cognitive Behavioral Therapy for Insomnia (CBT-I) delivered by human therapists is recommended as the most evidence-based treatment for insomnia. However, human support has various limitations.
Therefore, in this study, we are developing a Therapist Avatar using generative AI to address these limitations.

We will examine linking therapeutic effects with the body's movements, skin conductance, and linguistic features observed during psychotherapy. Research utilizing multiple types of information during psychotherapy is still limited, and methods to process different kinds of information into analyzable forms have yet to be firmly established.
Therefore, in this study, we will concurrently build a database called a registry to store processed data while also exploring processing methods for various types of information during psychotherapy.

Insomnia, characterized by the distress of inability to sleep, and Sleep Apnea Syndrome, characterized by daytime sleepiness, may appear contradictory at first glance. Still, it has been revealed that they often coexist at a high rate.
However, there is currently no established research or treatment approach for the coexistence of insomnia and sleep apnea syndrome. In this study, we aim to aggregate data from patients who have sought medical attention, utilizing artificial intelligence technology to investigate the pathophysiology and validate the treatment responsiveness of interventions for the coexistence of these two conditions.