Dalai Tang

Localization of human based on fuzzy spiking neural network in informationally structured space

This paper proposes a human localization method in informationally structured space based on sensor network First, we explain informationally structured space, robot partners, and sensor networks developed in this study. Next, we apply a fuzzy spiking neural network to extract a person from the measured data by the sensor network. Furthermore, we propose a learning method of fuzzy spiking neural network based on the time series of measured data. Finally, we discuss the effectiveness of the proposed methods through experimental results in a living room.

A novel multimodal communication framework using robot partner for aging population

It is expected that the population of elderly in the world will double in 2050.This paper proposes a human-friendly robot partner to assist the elderly.A new communication framework between the human and robot partner is developed.Informationally structured space was proposed to realize natural communication.Experiments using three case studies show the strength of the proposed framework. In developed country such as Japan, aging has become a serious issue, as there is a disproportionate increasing of elderly population who are no longer able to look after themselves. In order to tackle this issue, we introduce human-friendly robot partner to support the elderly people in their daily life. However, to realize this, it is essential for the robot partner to be able to have a natural communication with the human. This paper proposes a new communication framework between the human and robot partner based on relevance theory as the basis knowledge. The relevance theory is implemented to build mutual cognitive environment between the human and the robot partner, namely as the informationally structured space (ISS). Inside the ISS, robot partner employs both verbal as well as non-verbal communication to understand human. For the verbal communication, Rasmussens behavior model is implemented as the basis for the conversational system. While for the non-verbal communication, environmental and human state data along with gesture recognition are utilized. These data are used as the perceptual input to compute the robot partners emotion. Experimental results have shown the effectiveness of our proposed communication framework in establishing natural communication between the human and the robot partner.

Extraction of Daily Life Log Measured by Smart Phone Sensors Using Neural Computing

Abstract This paper deals with the information extraction of daily life log measured by smart phone sensors. Two types of neural computing are applied for estimating the human activities based on the time series of the measured data. Acceleration, angular velocity, and movement distance are measured by the smart phone sensors and stored as the entries of the daily life log together with the activity information and timestamp. First, growing neural gas performs clustering on the data. Then, spiking neural network is applied to estimate the activity. Experiments are performed for verifying the effectiveness of the proposed method.

Human localization by fuzzy spiking neural network based on informationally structured space

This paper analyzes the performance of the human localization by a spiking neural network in informationally structured space based on sensor networks. First, we discuss the importance of information structuralization. Next, we apply a spiking neural network to extract the human position in a room equipped with sensor network devices. Next, we propose how to update the base value as a method of preprocessing to generate input values to the spiking neurons, and the learning method of the spiking neural network based on the time series of measured data. Finally, we show several experimental results, and discuss the effectiveness of the proposed method.

Robot Partner Development Using Emotional Model Based on Sensor Network

This paper discusses the development of robot partner that can perform not only static conversation, but also can perform emotion expression by facial expression, gesture, and word expression using emotional model based on sensor network, therefore it can interact naturally with a person. Generally, the robot has sensors equipped inside it, however to express emotion the equipped sensors are not enough to grasp the necessary input information about the surrounding environmental situation. Therefore we propose a sensor network applied to the robot partner for estimating the environment states as input data, after that the acquired data is processed using emotional model to gain the desired emotional expression. In this paper, first we explain the concept of informationally structured space, robot partner that we are developing, and sensor network. Next, we explain the development of emotional model that consists of data acquisition from sensor network as an input and the model output such as face, gesture, and word expression. Finally, we conduct several experiments based on the proposed method, and discuss the ability of emotional model to develop robot partner that can perform emotional expression.

Multi-modal communication interface for elderly people in informationally structured space

This paper proposes a universal remote controller using iPhone for elderly people. First, we discuss system configuration of universal remote controllers for elderly people in informationally structured space. The developed system is composed of database management server PC, physical robot partners, environmental systems, and human interface systems. Next, we explain human interface of universal remote controllers using accelerometer and compass. Finally, we discuss the usability of the developed universal remote controller through experimental results.

Informationally Structured Space for Life Log Monitoring in Elderly Care

Recently, various types of wireless sensor network systems have been developed to realize daily care for elderly people living alone. Furthermore, visualization methods of life logs have been presented. However, it is important to integrate different types of data measured by each sensor node to estimate human states and behaviors. Therefore, we have proposed the concept of informationally structured space (ISS). This paper proposes a methodology to deal with data measured by sensor nodes in wireless sensor networks on ISS. First, we explain how to use ISS for wireless sensor networks. Next, we apply the proposed method to elderly care. We propose four different components such as (1) human localization by spiking neurons, (2) human movement transition probability, (3) redundant monitoring by simultaneous firing of sensor nodes, and (4) temporal life pattern extraction by Gaussian membership functions. Finally, we show several simulation results and discuss the effectiveness of the proposed method.

Informationally structured space for multimodal monitoring in smart houses

Recently, various types of wireless sensor network systems have been developed. Their price has also been reduced, and we can easily use such systems to monitor human states and behaviors in a house. Furthermore, such kind of information is useful for elderly care and nursing care by robot partners. However, we have to integrate different types of data measured by each sensor node to estimate human states and behaviors. If the measurement data are organized and structured, the monitoring system can do flexible monitoring and share the information. Therefore, we have proposed the concept of Informationally Structured Space (ISS). This paper proposes a methodology for human behavior estimation by wireless sensor networks in ISS. Next, we propose a monitoring system for sensor state and human behavior using ISS. Finally, we show several experimental results and discuss the effectiveness of the proposed method.

Wearable sensor-based monitoring system for human behavior estimation

Many types of wearable sensor systems have been developed for health monitoring system. This paper proposes a method of abnormal behavior detection utilizing 429MHz band wireless sensor module as a wearable sensor system. We apply fuzzy spiking neural network for feature extraction and use decision tree to identify the behavior patterns. Furthermore, we show an experimental result and discuss the validity of the proposed method.

Fuzzy Spiking Neural Network for Abnormality Detection in Cognitive Robot Life Supporting System

In aging nation such as Japan, elderly people belong to the vulnerable group that constantly need health care and monitoring for their well-being. Therefore, an early warning system for detecting abnormality in their daily activities could save their life (e.g. Heart attack, stroke and etc.). However, such early warning system must not trigger any false warning signals in order to robustly operate in real world applications. Robot interactions with human are useful to prevent false warning signals from sending out to healthcare worker. Next, the system should be able to detect short-term abnormal and also long-term abnormal behaviors of the elderly people within their normal daily life routine. Therefore, it is important to integrate information ally structured space with cognitive robot to confirm the elderlys abnormal situation with human-robot interactions before sending out warning signals to healthcare workers. In this work, we proposed an evolutionary computation based approach to optimize fuzzy spiking neural network for detecting abnormal activities in the elderly peoples daily activities.