The coimagination method (Otake, 2009) is designed to prevent dementia in individuals with mild cognitive impairment (MCI) by utilizing the brain’s natural processes. This method involves participants sharing their thoughts and feelings through group conversations centered around shared photos. The coimagination method contains two phases: (1) each participant talk about their memories and experiences related to the photos they bring, and (2) other participants ask questions about the photos. Automating the MCI estimation could be helpful for assisting individuals with MCI during coimagination. However, previous MCI estimation methods rarely focused on group conversation scenarios, despite the potential of multimodal features observed in these scenarios in revealing cognitive states. This study focuses MCI individuals defined by cognitive test scores (e.g., Mini-Mental State Examination (MMSE)). We explore MCI estimation from three aspects. First, we clarify whether MCI can be effectively estimated by constructing estimation models using linguistic and acoustic features from coimagination sessions. Second, we evaluate the impact of using data from the two distinct phases, as they may activate participants’ cognitive functions differently. Finally, we analyze the effects of incorporating subtasks including participants’ conversational customary and engagement level during coimagination via multitask learning. The experimental results demonstrated that individuals with MCI can be effectively estimated from group conversations from coimagination, with the highest macro F1 score of 0.693. The results also demonstrated that the performance improved when using data from the phase that highly activates cognitive functions and when considering conversation customary as a subtask.

The sense of smell, which is also known as olfaction, can improve brain-computer interfaces (BCIs). It provides a natural and non-invasive way for users to interact with technology by assigning different commands to various scents delivered one after the other in a classical oddball paradigm setting. Olfactory BCIs detect changes in brain activity patterns in response to odors, which can be used to control devices, communicate, or provide information about the user’s mental state in the passive BCI modality. Olfactory stimulants can be processed quickly without causing attention overload, making them a promising direction for future BCI research and development. However, some challenges need to be overcome, such as the need for more accurate and reliable odor delivery systems and the development of robust algorithms for detecting and interpreting brain activity patterns. In a pilot study, we have shown the possibility of using a common spatial pattern (CSP) filtration and subsequent clustering of attended versus ignored scent stimuli in a novel BCI modality. Our preliminary results are promising, with accurate EEG response classification observed in four of eight experimental subjects.

Japan faces many challenges related to its aging society, including increasing rates of cognitive decline in the population and a shortage of caregivers. Efforts have begun to explore solutions using artificial intelligence (AI), especially socially embodied intelligent agents and robots that can communicate with people. Yet, there has been little research on the compatibility of these agents with older adults in various everyday situations. To this end, we conducted a user study to evaluate a robot that functions as a facilitator for a group conversation protocol designed to prevent cognitive decline. We modified the robot to use backchannelling, a natural human way of speaking, to increase receptiveness of the robot and enjoyment of the group conversation experience. We conducted a cross-generational study with young adults and older adults. Qualitative analyses indicated that younger adults perceived the backchannelling version of the robot as kinder, more trustworthy, and more acceptable than the non-backchannelling robot. Finally, we found that the robot’s backchannelling elicited nonverbal backchanneling in older participants.

Brain-computer interface (BCI) and efficient machine learning (ML) algorithms belonging to the so-called ‘AI for social good’ domain contribute to the well-being improvement of patients with limited mobility or communication skills. We report preliminary results from a project focusing on developing a dementia digital neuro–biomarker for early-onset prognosis of a possible cognitive decline utilizing a passive BCI approach. We also report findings from two elderly volunteer pilot study groups in oddball paradigm EEG responses to attended (target) and inhibited (ignored) images in a classical short-term-memory evaluating oddball paradigm. We propose applying an information geometry approach employing Riemannian geometry tools for EEG covariance matrix-derived features used in subsequent shallow machine learning classification. The reported pilot study showcases the vital application of artificial intelligence (AI) for an early-onset mild cognitive impairment (MCI) prediction in the elderly.

In a super-aged society, dementia is a severe problem in which older adults suffer from symptoms to maintain daily life. A wide variety of systems have been developed for older adults; however, to our knowledge, few of them encourage communication aiming at the training of cognitive functions. This paper reports the results of home-based experiments using a novel dialogue system. Our proposed system has consisting of subsystem such as an experiment scheduling system, mobile application for older adults, dialogue supportive system, and robots. We explain how subsystems communicate with each other, data format to be designed to follow the experimental situation remotely easily. Besides, we describe how to select communication protocols to avoid system errors. Through the experiment, we identify the characteristics of users’ statements and gaps in the duration of users’ utterances. We also address the current system’s issues and clarify improvements to our strategy for further experiments.

Dementia in the elderly has recently become the most usual cause of cognitive decline. The proliferation of dementia cases in aging societies creates a remarkable economic as well as medical problems in many communities worldwide. A recently published report by The World Health Organization (WHO) estimates that about 47 million people are suffering from dementia-related neurocognitive declines worldwide. The number of dementia cases is predicted by 2050 to triple, which requires the creation of an AI-based technology application to support interventions with early screening for subsequent mental wellbeing checking as well as preservation with digital-pharma (the so-called beyond a pill) therapeutical approaches. We present an attempt and exploratory results of brain signal (EEG) classification to establish digital biomarkers for dementia stage elucidation. We discuss a comparison of various machine learning approaches for automatic event-related potentials (ERPs) classification of a high and low task-load sound stimulus recognition. These ERPs are similar to those in dementia. The proposed winning method using tensor-based machine learning in a deep fully connected neural network setting is a step forward to develop AI-based approaches for a subsequent application for subjective- and mild-cognitive impairment (SCI and MCI) diagnostics.

In super aged society, system that assists social activities of older adults is needed for cognitive enhancement. Group conversation is one of the social activities. One of the largest problems for older adults is the imbalance of participation to conversation. Our approach is to develop a system that assists group conversation of older adults, by evaluating speech duration and facial expression of each participant during conversation. It enables all participants evenly to take part in the conversation, which tends to be difficult for older adults. We analyzed 15 group conversations for calculating the parameters used for evaluating the conversation. The effectiveness of the system using the obtained parameters was validated through the experiment. The system which evaluates only speech duration of each participant and the system which evaluates both speech duration and facial expression of each participant during group conversation were compared by analyzing 8 group conversations for each. The results demonstrated that the system which evaluates both speech duration and facial expression of each participant can make all participants to take part in the conversation evenly and actively. We successfully developed the system that can support group conversation regardless of the contents.

Near-infrared spectroscopy (NIRS) is a non-invasive technology for measuring brain activity. Recently, the number of research papers on brain machine interface (BMI) based on NIRS technology is increasing. NIRS is a safe and convenient technique but its measurement results are unstable. To improve reliability of NIRS-based BMI, methods to extract stable data from NIRS signals are necessary. This paper describes a reliable NIRS-based BMI system we have developed. The feasibility of the method was demonstrated through generating motion of a humanoid robot.

Prevention of dementia is a crucial issue in this aged society. We propose coimagination method for prevention of dementia through supporting interactive communication with images. Coimagination method aims to activate three cognitive functions: episode memory, division of attention, and planning function, which decline at mild cognitive impairment (MCI). Participants of the coimagination program bring images according to the theme and communicate with them. The objective of the program is to make participants to focus on present and future rather than past, which is major difference between coimagination and reminiscence. We measure frequency of comments by others for each participant in order to evaluate interactivity of conversation. They take memory task whether they remember the owner or theme of images after the series of sessions. We held coimagination program successfully at the welfare institution for elderly people in Kashiwa city, Japan. Each session was held one hour per week for five times. The result of the task indicates that the participants showed empathy with each other. The effectiveness of the proposed method was validated through the experiment.

This paper presents open brain simulator, which estimates the neural state of human through external measurement for the purpose of improving motor and social skills. Macroscopic anatomical nervous systems model was built which can be connected to the musculoskeletal model. Microscopic anatomical and physiological neural models were interfaced to the macroscopic model. Neural activities of somatosensory area and Purkinje cell were calculated from motion capture data. The simulator provides technical infrastructure for human biomechatronics, which is promising for the novel diagnosis of neurological disorders and their treatments through medication and movement therapy, and for motor learning support system supporting acquisition of motor skill considering neural mechanism.

The motivation of this research is to compute internal perspective of humans through external observation. In this paper, we propose method for analyzing neural information through motion measurement. The method is on the bases of the anatomy and physiology of somatic and spinal nervous system. Muscles are classified by the innervated nerves originate from the spinal cord. The somatotopic organization inside the ventral horn of the spinal cord is utilized for topological structure of the spinal neural information. Time series of images which represent distribution of somatic information inside the spinal cord were successfully obtained through measurement and computation for sword swinging 'kesagiri' motion. The coordination of the motion at spinal level was analyzed. The proposed method provides fundamental for motor learning support system.

This paper proposes the mathematical model of deformation for gel robots and develops the pattern formation theory. The robots are made of surfactant-driven ionic polymer gel in constant electric fields, which is a typical electroactive polymer gel containing poly 2-acrylamido-2-methylpropane sulfonic acid (PAMPS). A beam of gel in uniform electric fields develops wave forms through penetration of the surfactant solution. The model is to be built on the hypothesis of adsorption-induced deformation. The mechanism of wave-shape pattern formation is then analyzed utilizing the model. The results of this study provide the foundation to develop deformable machines with virtually infinite degrees of freedom.

This paper formulates and solves the inverse dynamics problem of deformable robots made entirely of electro-active polymer gel. One of the primary difficulties with deformable robots is that they have conceptually infinite degrees of freedom. We solve this problem through the selection of an essential point to generate a desired motion. The problem is then reduced to trajectory control of a point on the robot. We have proposed dynamic models of electro-active polymers system and derived a variety of motions by applying either spatially or time varying electric fields. However, the motion control problem has not yet been investigated. We show a procedure to realize an inversion (turning over) motion of a starfish-shaped gel robots by applying both spatially varying and time alternating electric fields. This work takes the first step towards motion control of deformable robots.

This paper proposes an inverse kinematic model for deformable robots made entirely of electro-active polymer gel. The required method is to control a higher degrees of freedom than numbers of input. We (2000, 2001) have been proposed a kinematic and dynamic model of electro-active polymer system and derived a variety of motions of gel robots by applying spatially varying electric fields. However, inverse kinematic model and the method of applying time alternating electric fields have not been investigated. We challenge the tip control of gel manipulator by applying spatially uniform but time varying electric field. We show the procedure to control the tip position of a gel manipulator by dynamically and slightly changing its whole configuration. Our work is a first step towards the shape control of gel robots.

This paper proposes a model for predicting the dynamic motions of elastic robots made entirely of electro-active polymer gel. The requirement is that both the active and passive deformations be described simultaneously. Active deformations arise from a generated stress, while passive deformations are due to external forces, such as gravity and friction. We represent both in the same framework, considering them as the result of either electrochemical or mechanical interaction between the gel robot and the environment. We evaluated the model by comparing the results obtained through simulations and experiments using prototype gel robots. One experiment involved hitting an external object, and the other involved bending while resting upon a surface. We present an overview of the model, with a discussion of the experimental results and future work.

Introduces and describes a kinematic model based on a chemical reaction to control robots made of soft materials, poly (2-acrylamido-2-methylpropane sulfonic acid) gel (PAMPS gel). Experiments were conducted using a prototype mechanism whose energy is supplied by applying an electric field in a surfactant solution. We have verified the validity of our model by comparing the experimental and simulation results obtained by bending a flexible strip of PAMPS gel under a uniform electric field.