This paper proposes an evaluation method for autonomy walk by using sole pressure that measured mat type load distribution sensor. The sole pressure data is changed by posture and kicking force of walker. This study tries to find abnormal gait from footprint information. The footprints that maid from the sole pressure data is also reflected foot shape of walker. However, abnormal gait reduces area of footprint. For example, toe area is vanished from walker with floating toes. Our method makes template footprint from 256 subjects, and degree of normal gait is calculated from comparison results with obtained data and the template. In the experiment, we apply the method to the patient's data during rehabilitation and confirmed its effectiveness.

Recently, the number of elderly people living alone is increasing and has become a serious problem in Japan. On the other hand, the stability of both social rhythm and biological rhythm is very important for extension of healthy life expectancy. It is difficult for elderly people to understand the current stability of social rhythm and biological rhythm in daily life. Therefore, we propose several visualization and management systems based on daily life monitoring. First, we explain a measurement method of human daily life logs. Next, we show a social rhythm management support system based on three different types of classification methods, such as (1) ICF (International Classification of Functioning, Disability and Health), (2) NHK (Nippon Hoso Kyokai) Activity classification, and (3) Social Rhythm Metric (SRM). Finally, we discuss the effectiveness of the proposed methods and future works.

This paper describes an air-coupled ultrasound system by using DIO-2000. The system is aimed to use for inner muscle evaluation in rehabilitation process. The system evaluates inner muscle with low constrain than conventional method which measured by using MR image, X-ray CT and contacted ultrasound system. Our air-coupled ultrasound system measures transmitted ultrasound wave with very low power through human abdominal region by employing a pulsar-receiver with high sensitive preamplifier, and wave detection method based on fuzzy inference finds transmitted wave from noisy wave. The fuzzy inference is derived from characteristics of transmitted wave. In the experiment, we evaluate the accuracies of wave detection method for human body.

One of the problems in humanoid locomotion generation is energy efficiency. This paper proposes a method for energy efficiency optimization in simple humanoid robot locomotion using single objective genetic algorithm. With the aim to produce walking trajectory system using minimum energy and good stabilization, torque and oscillation analysis are required to calculate the stabilization. The number of desired outputs in this system is 4 parameters and the number of inputs is 9 parameters. We used neural network with back propagation learning mechanism to realize the relationship between input and output data as well as producing fitness function for genetic algorithm. The trajectory system has 2 trajectory equations, which is pelvis trajectory and ankle trajectory. Ankle trajectory is formed from circle function in Cartesian coordinate space and pelvis trajectory is formed from third order polynomial equation. Both of them are influenced by inclination of robot body. In the experiment, we apply this system using Bioloid robot with inertial sensor already installed. The experimental results show the analysis of energy by observing the torque resulted by servomotor in each joint. We observe that using this system, the torque value resulted by servomotors was decreased and has good stabilization.

In this paper we present synaptic weight optimization for dynamic locomotion in four-legged robot (cat, dog) based on neural oscillator. We investigate the muscular structure to design the relationship for both extensor neuron and flexor neuron. The robot has 3 joints in each leg and each joint is represented by 2 neurons, extensor and flexor neuron. The robot has 4 main circular neurons as the server neuron and the other neurons are the client neurons. The server neurons generate the oscillator signal to the client neurons. The signal can be dynamically adjusted according to the environmental condition. Not only the synaptic network between the neurons, but the synaptic network between neurons and sensors was also designed to realize dynamical locomotion. Pressure sensor and inclination sensor were installed in the robot. The signal is influenced by ground reaction sensor and body inclination feedback. While the foot touches the ground, the sensory neuron sends the signal to the joint neuron. Negative signal will be sent to flexor neuron and positive signal will be sent to extensor neuron. To optimize the strength of weights in the synaptic neurons we apply the Nondominated Sorting Genetic Algorithm II (NSGA-II). The stability of torso body, the velocity, and the movement direction are the three objectives in the multi-objective NSGA-II. In the experiments, a computer simulation framework, the Open Dynamic Engine (ODE) is applied. The solution is evaluated based mainly on the moving distance of the robot. Experiments were conducted to confirm the proposed technique.

Health promotion support systems provide exercise program for elderly people to ensure their health. Some robot partners are used for the system as instructor. In the system, the instructor robot need to select an exercise that improves their health corresponding to the motivation of each person. Our system uses self-efficacy as the motivation index of elderly people for exercise. This paper proposes a self-efficacy estimation method to select more appropriate exercise. This method estimates the self-efficacy based on predicted exercise score acquired through communication and measured score obtained from distance distribution sensor.

Aphasia is one of the conditions of a higher brain function dysfunction. The aphasia decreases quality of life of patient with the dysfunction. Moreover, since patients with aphasia have handicap for talking, reading and understanding about rehabilitation program, the aphasia prevents progress of other rehabilitation. This paper describes a rehabilitation support system for patient with aphasia. The system employ tablet device as multimodal interface device. The system provides several tasks and measures their solution times and answers instead of speech therapist. And, the measured data send to cloud database to diagnose progress of that. In experiment, usability of our system was tested with six aphasia patients.

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.

This paper describes a autonomy walk evaluation method based on fuzzy logic for rehabilitation field. The method acquires dynamic foot pressure distribution change while walking by mat type load distribution sensor. To evaluate the walk, four gait features such as gait speed, time of double support, gait balance and total time of stance phase and are extracted from the foot pressure data. From these features, the method derives eight fuzzy if-then rules and fuzzy membership functions. These functions are learned by the foot pressure of commons and patients. A gait level index is estimated by fuzzy degree. In experiment, we employed ten patients and ninety commons, and took their pressure distribution data. To evaluate our method, we classified them to Patient or Common by their gait level indexes. We compared our method with support vector machines. The proposed method obtained higher classification rate.

This paper describes a personal verification method based on fuzzy logic using dynamic sole pressure distribution while walking. The method employs a pair id sole pressure distribution change, and it is acquired by a mat type load distribution sensor. As a preliminary experiment for shoes, we take sole pressure data by bare foot and two kind of slippered foot. We extract thirty nine gait features from each sole pressure distribution change. We calculate a fuzzy degree of a feature from two fuzzy if-then rules and them fuzzy membership functions for a feature. These fuzzy membership functions are statistically determined by learning data. The fuzzy degree of acquisition sole pressure data is calculated by total of fuzzy degree of all features. The method verifies the walking person by using the fuzzy degree of the acquisition sole pressure data. When the fuzzy degree of acquisition data higher than a threshold, we verify the walking person as the target person. In our experiments, we employed 11 volunteers and took sole pressure data six times for each volunteer and foot situation. When the learning data included same kind of test data, we obtained low equal error rate. We obtained low false acceptance rate.

This paper describes a foot age estimation system using fuzzy logic. The method employs sole pressure distribution change data. The sole pressure data is acquired by a mat type load distribution sensor. The proposed method extracts step length, step center of sole pressure width, distance of single support period and time of double support period as gait features. The fuzzy degrees for young age, middle age and elderly groups are calculated from these gait features. The foot age of the walking person on the sensor is estimated by fuzzy MIN-MAX center of gravity method. In the experiment, the proposed method estimated subject ages with good correlation coefficient.

This paper proposes a biometric personal authentication method based on one step foot pressure distribution change. We acquire the foot pressure distribution change by mat type load distribution sensor and use it as a personal authentication. We employ twelve features based on shape of footprint, and twenty seven features based on movement of weight while walking. A classifier for each feature is developed on the basis of fuzzy inference. The classifier is trained by a clonal selection algorithm in artificial immune system. A personal authentication system for one step is made every classifier for all features. We employed 10 volunteers, and we took the step data five times. We evaluated our method by five-fold cross validation method. We obtained low false rejection and acceptance rates in identification and verification.

In this paper, we analyze human gait pattern and estimate her/his foot age. We acquire foot pressure distribution change as gait pattern by a mat type load distribution sensor. From the foot pressure distribution data, duration of gait cycle and center of foot pressure (CFP) changes are determined for each stride. We employ four estimation indexes such as step length, step CFP width, the time of double supporting period and distance of step CFP changes. We employ 87 volunteers, and divided them to young, middle age and elderly groups. By comparing of three groups, we found that elderly had shorter step length and larger step CFP width than young and middle age people. Besides, the double supporting time of the elderly was longer, and distance of step CFP changes was longer than those of young and middle age people. From these facts, sixteen fuzzy IF-THEN rules are made. We determine a fuzzy degree for her/his foot age by fuzzy MIN-MAX center-of-gravity method. In our experiment on 87 volunteers, we compared these results with regression method.

This paper proposes a biometric personal authentication based on the pressure distribution while one step walking. We extract one step from a walk on a mat type load distribution sensor and use it to personal authentication. With this method, features which are based on weight movement and foot shape during walking are calculated, then a classifier is developed on the basis of fuzzy inference. We employed 30 volunteers. All volunteers are ranged from 20 to 85 years old. For each volunteer, we took walk data six times. Then, we evaluated this method by five training data and one test data. We obtained 6.1% EER (Equal Error Rate) and 13.9% FRR (False Rejection Rate) in verification (1:1 collation) and identification (1:N collation), respectively.

A dance partner robot has been developed as an example of platforms for realizing the effective human-robot coordination with physical interactions. This robot could dance together with a human by estimating the next step intended by the human. If the robot would mistake the step estimation, the human-robot coordination could not be continued. In this paper, an error recovery method for step selections, which changes robot's behavior according to human's behavior, is designed using hidden Markov models. Experimental results illustrate the validity of the proposed method.

This paper discusses advanced human-robot coordination systems. In order to realize the coordination, robots have to behave as not only followers but also leaders when they execute tasks with humans. As an example of the advanced human-robot coordination systems, a male-type dance partner robot is developed, which behaves as a male dancer and executes ballroom dances with a human. In ballroom dances, a male dancer leads a female dancer, and selects the next step based on the information such as the relative position between themselves and other dance couples, their positions in the dance floor, and so on. This paper addresses the step selection problems, which contains collision avoidances with other dance couples. Hidden Markov models are used to estimate their dance step trajectories. Experiments and simulations are performed to illustrate the validity of the proposed method.

As an example of platforms for realizing the effective human-robot coordination with physical interactions, a dance partner robot has been developed, which could dance together with a human by estimating the next dance step intended by the human. Generating robot’s active motion so as to be adapted to its user could be one of essential functions in the next generation robotic technology. This paper proposes the cooperative motion generation method for the robot, which is implemented by adjusting length of dance step stride based on physical interaction between the human and the robot. Experimental results illustrate the validity of the proposed method.

We have proposed a dance partner robot, which has been developed as a platform for realizing the effective human-robot coordination with physical interactions. In the previous research, we have improved an estimation system for dance steps, which estimates the next dance step intended by a human. Although estimating the intended step is important for realizing the human-robot coordination, the cases that the estimation is failed and that the robot selects an incorrect step are not considered. Such cases have to be discussed carefully for realizing the effective human-robot coordination. In this paper, a method for error detections of dance step selections are proposed, which is designed using hidden Markov models. Experimental results illustrate the validity of the proposed method

We have proposed a dance partner robot, which has been developed as a platform for realizing the effective human-robot coordination with physical interactions. In this paper, especially, we improve an estimation system for dance steps, which estimates a next dance step intended by a human. For estimating the dance step, time series data of force/moment applied by a human to the robot are utilized. The time series data of force/moment measured during dancing by a human and the robot include the uncertainty such as time-lag and variations for each repeated trial, because a human can not always apply the same force/moment to the robot exactly. In order to treat the time series data including such uncertainty, hidden Markov models are utilized for designing the dance step estimation system. With the proposed system, the robot estimates a next dance step based on human intention successfully.