Yairi Ikuko

Background Imagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus. Objective In this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCID method, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ. Results In the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities.

Objective and accurate classification of fear levels is a socially important task that contributes to developing treatments for Anxiety Disorder, Obsessive–compulsive Disorder, Post-Traumatic Stress Disorder (PTSD), and Phobia. This study examines a deep learning model to automatically estimate human fear levels with high accuracy using multichannel EEG signals and multimodal peripheral physiological signals in the DEAP dataset. The Multi-Input CNN-LSTM classification model combining Convolutional Neural Network (CNN) and Long Sort-Term Memory (LSTM) estimated four fear levels with an accuracy of 98.79% and an F1 score of 99.01% in a 10-fold cross-validation. This study contributes to the following; (1) to present the possibility of recognizing fear emotion with high accuracy using a deep learning model from physiological signals without arbitrary feature extraction or feature selection, (2) to investigate effective deep learning model structures for high-accuracy fear recognition and to propose Multi-Input CNN-LSTM, and (3) to examine the model’s tolerance to individual differences in physiological signals and the possibility of improving accuracy through additional learning.

Path integration is one of the functions that support the self-localization ability of animals. Path integration outputs position information after an animal’s movement when initial-position and movement information is input. The core region responsible for this function has been identified as the medial entorhinal cortex (MEC), which is part of the hippocampal formation that constitutes the limbic system. However, a more specific core region has not yet been identified. This research aims to clarify the detailed structure at the cell-firing level in the core region responsible for path integration from fragmentarily accumulated experimental and theoretical findings by reviewing 77 papers. This research draws a novel diagram that describes the MEC, the hippocampus, and their surrounding regions by focusing on the MEC’s input/output (I/O) information. The diagram was created by summarizing the results of exhaustively scrutinizing the papers that are relative to the I/O relationship, the connection relationship, and cell position and firing pattern. From additional investigations, we show function information related to path integration, such as I/O information and the relationship between multiple functions. Furthermore, we constructed an algorithmic hypothesis on I/O information and path-integration calculation method from the diagram and the information of functions related to path integration. The algorithmic hypothesis is composed of regions related to path integration, the I/O relations between them, the calculation performed there, and the information representations (cell-firing pattern) in them. Results of examining the hypothesis confirmed that the core region responsible for path integration was either stellate cells in layer II or pyramidal cells in layer III of the MEC.

Providing accessibility information about sidewalks for people with difficulties with moving is an important social issue. We previously proposed a fully supervised machine learning approach for providing accessibility information by estimating road surface conditions using wheelchair accelerometer data with manually annotated road surface condition labels. However, manually annotating road surface condition labels is expensive and impractical for extensive data. This paper proposes and evaluates a novel method for estimating road surface conditions without human annotation by applying weakly supervised learning. The proposed method only relies on positional information while driving for weak supervision to learn road surface conditions. Our results demonstrate that the proposed method learns detailed and subtle features of road surface conditions, such as the difference in ascending and descending of a slope, the angle of slopes, the exact locations of curbs, and the slight differences of similar pavements. The results demonstrate that the proposed method learns feature representations that are discriminative for a road surface classification task. When the amount of labeled data is 10% or less in a semi-supervised setting, the proposed method outperforms a fully supervised method that uses manually annotated labels to learn feature representations of road surface conditions.

Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization.

The recent increase in the use of intelligent devices such as smartphones has enhanced the relationship between daily human behavior sensing and useful applications in ubiquitous computing. This paper proposes a novel method inspired by personal sensing technologies for collecting and visualizing road accessibility at lower cost than traditional data collection methods. To evaluate the methodology, we recorded outdoor activities of nine wheelchair users for approximately one hour each by using an accelerometer on an iPod touch and a camcorder, gathered the supervised data from the video by hand, and estimated the wheelchair actions as a measure of street level accessibility in Tokyo. The system detected curb climbing, moving on tactile indicators, moving on slopes, and stopping, with F-scores of 0.63, 0.65, 0.50, and 0.91, respectively. In addition, we conducted experiments with an artificially limited number of training data to investigate the number of samples required to estimate the target.

This paper introduces an automatic road accessibility information collecting system inspired by human action sensing technologies of wheelchair users. The system aims to estimate a road accessibility caused by environmental factors, e.g. curbs and gaps, which directly influence wheelchair bodies, and also physiological factors, e.g. The wheelchair user's fatigue resulted by the environmental factors. We report that wheelchair user's fatigue influences wheelchair's action data sensed by accelerometer mounted on iPod touch. This paper contributes to discovering patterns of accelerations each of wheelchair user's fatigue and non-fatigue by clustering pushing wheel action data.

This paper proposes a methodology for developing large scale accessibility map with personal sensing by using smart phone and machine learning technologies. The strength of the proposed method is its low cost data collection, which is a key to break through stagnations of accessibility map that currently applied to limited areas. This paper developed and evaluated a prototype system that estimates types of ground surfaces by applying supervised learning techniques to activity sensing data of wheelchair users recorded by a three-axis accelerometer, focusing on knowledge extraction and visualization. As a result of evaluation using nine wheelchair users' data with Support Vector Machine, three ground surface types, curb, tactile indicator, and slope, were detected with f-score (and accuracy) of 0.63 (0.92), 0.65 (0.85), and 0.54 (0.97) respectively.

This research proposes a methodology for digitizing street level accessibility with human sensing of wheelchair users. The digitization of street level accessibility is essential to develop accessibility maps or to personalize a route considering accessibility. However, current digitization methodologies are not sufficient because it requires a lot of manpower and therefore money and time cost. The proposed method makes it possible to digitize the accessibility semi-automatically. In this research, a three-axis accelerometer embedded on iPod touch sensed actions of nine wheelchair users across the range of disabilities and aged groups, in Tokyo, approximately 9 hours. This paper reports out attempts to estimate both environmental factors: the status of street and subjective factors: driver's fatigue from human sensing data using machine learning. (C) 2015 The Authors. Published by Elsevier B.V.

This paper presents a deadlock-free fault-tolerant routing algorithm for irregular mesh network-on-chips based on a region-based approach. In this approach, a set of rectangular faulty regions called faulty blocks is formed for faulty nodes and a detour path is defined for each faulty block to indicate how packets must detour thefaulty block. The most recent routing algorithm on this approach is Message-Route (Holsmark and Kumar J Inf Sci Eng 23:1649-1662, 2007) which does not have restrictions on the number of tolerable faulty nodes and its distribution. However, this algorithm has three crucial problems; (1) this algorithm fails to provide complete and deadlock-free routing, (2) many nonfaulty nodes are contained in faulty blocks and thus deactivated, and (3) complex routing functions are not feasible for hardware implementation. In this paper, we give a solution for each of the above three problems. We correct the errors of Message-Route to make it complete and deadlock-free. Then, we propose a deadlock-free fault-tolerant routing algorithm which can work under small-sized faulty blocks with a simple routing control. Experimental results show that the proposed algorithm significantly reduces the size of faulty blocks and improves communication latency for both random and cluster faults. Moreover, an FPGA implementation of the proposed algorithm is also discussed.

The collaborative work of visually impaired people and sighted people on equal ground plays a significant role for visually impaired people's social advance in society. We developed a collaborative application of music composition to achieve the goal mentioned above. This application has a beautiful tangible interface that would attract the attention of both visually impaired and sighted people, and multiple functions that are likely to induce collaborative communication among users. We demonstrated the experiment with six visually impaired people and six sighted people. In the experiment, the visually impaired people could lead the collaborative work without hesitating even in front of the sighted people whom they did not know very well. Then we focused our attention on the moment in which the visually impaired were having fun, and discussed the factor of the excitement.

A Visually impaired people's life is changing significantly in recent information society even the visually impaired person who masters a various functions of a smart device exists. However, the diversity of their lifestyle has not been thoroughly investigated yet, compared to that of sighted people's. Therefore, the lifestyles of visually impaired people tend to be misunderstood by others and even wrong stereotypes of them could be spread in society. This situation has been making it hard for us to find actually useful living assistants for them. The biggest factor that prevents investigations of bare lifestyles of visually impaired people is the privacy issue caused by video recording. This study's purpose is to propose and evaluate a new sensing system that substitutes the video ethnography and raises degree of abstraction so that it would not specify personal information and invade their privacy.

This paper introduces a novel system for computational estimation and visualization of a possibility of accidents and incidents using wheelchair users' driving life-logs with three-axis accelerometers mounted on smart devices such as smartphones. Three wheelchair users participated in outdoor driving experiments held in urban center of Tokyo and all their actions were recorded in time-series of acceleration values and filmed on a digital video camera. In total, four units of iPods, which attached on left and right wheels, seats, and user's body, recorded approximately 1,800,000 accelerations signals during 10,000 seconds. In this paper, we analyze and classify life-logs of three wheelchair users' driving as the first step of computational estimation. We employed Support Vector Machine for classification, and created the supervised data from the video by human judgments. The life-logs were classified into moving/resting state and rough/flat state of the ground surface with finding optimal window size from 0.5 sec to 10 sec. As the result of classifications, estimation of moving/resting was achieved 99.8% accuracy rate and estimation of rough/flat surface was achieved 88.3% accuracy rate. Also estimations of driving difficulty were visualized on Google Map, and were evaluated by comparing with actual states of roads about wheelchair driving difficulty. © 2013 IEEE.

This paper introduces our research project about the universal designed interactive contents for visually impaired people's touch panel interaction. As an instance, we proposed "One Octave Scale Interface (abbr. OOSI) as the graphical representation. Several touch panel contents with OOSI, such as a guide maps, a concentration game, and shape recognition puzzle of symbols, have been implemented. This paper reports the evaluation of shape recognition puzzle by visually impaired people.

Music applications, like a GarageBand, have become more popular today because they afford intuitive comfortable visual interface for users to play, remix, and compose music. But visually impaired people have difficulties to use such a software application. Therefore, this paper introduces a novel music interface for visually impaired people using daily goods and stationery on the table. An experimental system was developed with Kinect for AR marker and gesture recognition, and with sounds of three instruments, the piano, the guitar and the percussion. Five blind young people participated in the evaluation of right combination of the goods. The results shows that the proposed interface is effective both single use and collaborative work.

Life-logging has attracted rising attention as the most fundamental elements for developing every rich software today. This paper presents computational estimation and mapping of potential accidents and incidents of wheelchairs from life-logs with a single cheap and mini-sized three-axis accelerometer mounted on a wheelchair. Wheelchair driving data was obtained by real wheelchair users driving with their wheelchair on real roads, but has the sampling time delay and noises. As a first step of computational estimation, wheelchair driving behavior was classified into moving and static action, and the moving action was divided into tough and smooth status of the ground surface. We employed Support Vector Machine for classification, and made the precise supervised data from the video of wheelchair driving. As the result of classification, estimation of moving/static was achieved 98.2% accuracy rate and estimation of tough/smooth surface was achieved 82.6% accuracy rate. From the surface estimation result, wheelchair-driving difficulty was mapped and evaluated. © 2012 Springer-Verlag.

Protecting the lives and the rights of the impaired people and promoting their social participation is a paramount principle today. Especially for visually impaired people, map usage and route recognition is an important function for promoting social participation. So we have been developing a new assistive interface which they can intuitively recognize the map using audio and touch panels. The assistive interface is universal designed to enable not only the visually impaired people but also the non-impaired people to enjoy using interactive digital map contents together. This paper introduces our recent progress about the assistive interface called the One Octave Scale Interface. The effectiveness of the interface was confirmed by doing experiments of graph and map recognition and a walking experiment after presenting route guide map.

Multi-site multi-user multiple input multiple output (MS-MU-MIMO) is a key technology to construct the next generation high performance mobile communication networks; however, in MS-MU-MIMO systems, co-channel interference (CCI) occurred at cell edges significantly reduce user throughput. To overcome this problem, in this paper, we proposed a MS-MU-MIMO system with block diagonalization (BD)-based interference cancellation. Overall downlink performances of the proposed system and the traditional MS-MIMO system are compared through computer simulation, and the simulation results show that the proposed system can improved user throughput.

This paper studies human movement of both mobility and visually impaired people using mobile sensing devices as the first step toward creating an accessible information base. Nine mobility impaired persons conduct an experiment of wheelchair moving, and the visualized sensing results mapped on Googlemap is compared with their subjective feelings. Also, one blind person conducts an experiment of walking with a walking assistant. The sensing results show that a single accelerometer enabled to detect walking, descending and waiting behaviors.