As countermeasure for preventing dementia of aging population, coimagination method has been developed. The coimagination method helps participants in utilizing brain cognitive functions of maintaining recent episodic memorization, retention and recall by the process of conversations. Hence, the risk of older adults in getting into mild cognitive impairment (MCI), which is a previous stage of dementia caused by disuse of brain cognitive functions, will decline. However, we observed situations of some older adults that recent episodic memory functions were not activated as expected. Such situations are older adults who talk about knowledge rather than episodic memories or older adults who talk about past experiences rather than recent experiences. Therefore, a novel coimagination program named coimagination method with expedition was developed to solve these situations. By adding expedition in a sightseeing area before the coimagination method, older adults have the opportunity to find topic of conversations through expedition. During conversation supported by the coimagination method, older adults are expected to recall their episodic memories in expedition and talk about it. The purpose of this research is to verify the effect of the coimagination method with expedition in older adults, by comparing mental time of older adults in the coimagination methods with and without expedition. Firstly, we estimate the mental time of older adults by analyzing their utterances during conversations supported by both coimagination methods. The past, present and future mental times of participants are enumerated in percentage. Secondly, we study the mental time travelling of participants during conversations. Finally, we study the transition points of mental time to find tendency of participants to talk about recent experiences. In this research, the analytical results validate the effectiveness of helping older adults to talk about recent episodic memories during conversation supported by the coimagination method with expedition compared to the coimagination method.

In the field of self-help groups for recovering from developmental disorders or alcohol dependence or the other problem, a meeting of non-crosstalk style has been used. On the meeting style, participants speak about one topic without conversation with the other participants, and advising and asking to others are not recommended besides. We are proposing an online meeting system that is specialized to support non-crosstalk style meeting. Now, we would like to develop an online agent who can perform like a human participant or more useful for participants. Since the developments of above online meeting system and autonomous agents contribute to supporting many people in the self-help field, this study has an impact in the designing methods for making better well-being space environment. In order to inspire human behaviors to the agent, this paper shows analysis the results of online humans meetings using our proposed system. As the experimental results which were compared with a classical style system, it has revealed about proposed system as follows: (1) frustrating with rules of non-crosstalk is small, (2) conversational speech didn’t reduce, (3) conversational speeches were increasing along with the time but they are leading to prevent from decreasing the number of speeches, and (4) more sensitive to the speed of displaying speeches.

This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in real-time, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping.

Highly sensitive low noise electrodes, capability of fast processing of multivariate signals, low cost of hardware and wireless communication have widen the possibilities of the use of electroencephalogram (EEG) data for various applications. These applications are not only restricted to medical investigations (like epileptic seizures, monitoring anesthesia or brain functions etc.), but also for well-being of disabled patients, as well as for entertainments like playing games. Our focus in this study is BCI applications based on identification of Event Related Potential (ERP) P300. One such application is BCI speller, which is used in our experiments. BCI speller on the market use 8 probes and take 72 seconds to collect data to reliably spell a single character. The motivation of this work is to reduce the number of probes and the time needed to spell a letter. A commercial product should reliably work for every user (customer). Such a large number of probes and long time to spell a letter are necessary to ensure correct recognition. We have shown that if we identify the position of the probes appropriately for an individual, as few as two probes could give even better results. All experiments are conducted at our in-house facility, where most ot the subjects undergone no prior training.

Due to the advances in medical science, increasing health consciousness, improved quality of food, the average human life span has increased to a great extent. On the other hand, stresses of modern life, overwork and less sleep, increased usage of digital devices and internet, less exercise, are leading us to poor quality of life. Elderly people are more vulnerable to reduced life quality due to deterioration of both physical and mental health. People at any age need to maintain a minimum level of wellbeing to pursue his or her daily activities to lead a fulfilling life. Thus the need of assessing and restoring wellness is very important. Fortunately the progress of information and communication technologies provide use sensor devices and computing platform to feel, monitor and restore the wellness. In this work, a study has been done to define and determine wellness related to daily activities data obtained from various sensors and provide recommendation to the user regarding improvement of life style to achieve wellness. A small-scale experiment has been done using a simple lifelog device. The daily activities data including walking steps, sleep time, inactive period, calories burned are collected from 8 subjects. In addition food intake, eating times, cell phone usage, messaging time, time of interaction with other people and solo time are also manually collected. The correlation of physical activities (walking time, exercise time), mental activities (cell phone usage, study time, interaction with friends) and sleep patterns are studied. A simple parameter Tiredness Factor has been proposed to determine wellness and a recommendation system for improving wellness has been developed. Questionnaire from the subjects about the personal feelings of wellness has been noted and used to evaluate our proposal.

Numerous studies have identified differences between left-handed and right-handed people, especially in the fields of psychology and neuroscience. Using a social media setting, this paper presents a data-driven approach to explore whether a person's handedness can be identified given his or her writing, and shows handedness characteristics that can be inferred from language.

Artificially intelligent conversational agents have been demonstrated to positively impact team based learning in classrooms and hold even greater potential for impact in the now widespread Massive Open Online Courses (MOOCs) if certain challenges can be overcome. These challenges include team formation, coordination and management of group processes in teams working together while distributed both in time and space. Our work begins with an architecture for orchestrating conversational agent based support for group learning called Bazaar, which has facilitated numerous successful studies of learning in the past including some early investigations in MOOC contexts. In this paper, we briefly describe our experience in designing, developing and deploying agent supported collaborative learning activities in 3 different MOOCs in three iterations. Findings from this iterative design process provide an empirical foundation for a reusable framework for facilitating similar activities in future MOOCs.

People increasingly rely on machine learning (ML) to make intelligent decisions. However, the ML results are often difficult to interpret and the algorithms do not support interaction to solicit clarification or explanation. In this paper, we highlight an emerging research area of interpretable explanations for transfer learning in sequential tasks, in which an agent must explain how it learns a new task given prior, common knowledge. The goal is to enhance a user's ability to trust and use the system output and to enable iterative feedback for improving the system. We review prior work in probabilistic systems, sequential decision-making, interpretable explanations, transfer learning, and interactive machine learning, and identify an intersection that deserves further research focus. We believe that developing adaptive, transparent learning models will build the foundation for better human-machine systems in applications for elder care, education, and health care.

Social media is increasingly populated with bots. To protect the authenticity of the user, experience machine learning algorithms are used to detect these bots. Ethical dimensions of these methods have not been thoroughly considered yet. Taking histogram analysis of Twitter users' profile images as example, the paper demonstrates the trade-offs of accuracy, transparency, and robustness. Because there is no general optimum in ethical considerations, these dimensions form a "devil's triangle".

Here ought does require able--computationally and indeed logically intractable systems The question of Robot Ethics is difficult to resolve not because such as Asimov's laws are excluded (Myers, 2010). of the nature of Robots but because of the nature of What makes moral reasoning about intelligent artefacts Ethics. As with all normative considerations, robot ethics requires different from moral reasoning about natural entities is that that we decide what "really" matters--our most fundamental our obligations can be met not only through constructing the priorities. Are we more obliged to our biological socio-ethical system but also through specifications of the kin or to those with whom we share ideas? Do we value the artefacts. This is the definition of an artefact.