Kosuke Okusa

Indoor location estimation based on the RSS method using radial log-normal distribution

We study the problem of analyzing indoor location estimation by statistical radial distribution model. In this study, we suppose the observed distance data between transmitter and receiver as a radial log-normal distribution. We estimate the subjects location using marginal likelihoods of radial lognormal distribution. To demonstrate the effectiveness of our method, we conducted two sets of experiments, assessing the accuracy of location estimation of static case and dynamic case. In static experiment, subject is stationary state in some places in the chamber. This experiment is able to measure the precise performance of proposed method. In dynamic experiment, subject is move around in the chamber. This experiment is able to measure the suitability for practical use of proposed method. As a result, our method shows high accuracy for the static case indoor spatial location estimation.

Human Gait Modeling and Statistical Registration for the Frontal View Gait Data with Application to the Normal/Abnormal Gait Analysis

We study the problem of analyzing and classifying frontal view human gait data by registration and modeling on a video data. In this study, we suppose that frontal view gait data as a mixing of scale changing, human movements and speed changing parameter. Our gait model is based on human gait structure and temporal-spatial relations between camera and subject. To demonstrate the effectiveness of our method, we conducted two sets of experiments, assessing the proposed method in gait analysis for young/elderly person and abnormal gait detecting. In abnormal gait detecting experiment, we apply K-NN classifier, using the estimated parameters, to perform normal/abnormal gait detect, and present results from an experiment involving 120 subjects (young person), and 60 subjects (elderly person). As a result, our method shows high detection rate.

A Simulation Study on Indoor Location Estimation Based on the Extreme Radial Weibull Distribution

In this study, we investigate the possibility of analyzing indoor location estimation under the NLoS environment by radial extreme value distribution model based on the simulation. We assume that the observed distance between the transmitter and receiver is a statistical radial extreme value distribution. The proposed method is based on the marginal likelihoods of radial extreme value distribution generated by positive distribution among several transmitter radio sites placed in a room. Okusa and Kamakura (Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017) [16] not discussed the more detail performance of radial distribution based approach. To cope with this, to demonstrate the effectiveness of the proposed method, we carried out a simulation experiments. Results indicate that high accuracy was achieved when the method was implemented for indoor spatial location estimation.

Young and elderly, normal and pathological gait analysis using frontal view gait video data based on the statistical registration of spatiotemporal relationship

We study the problem of analyzing and classifying frontal view gait video data. In this study, we focus on the shape scale changing in the frontal view human gait, we estimate scale parameters using the statistical registration and modeling on a video data. To demonstrate the effectiveness of our method, we apply our model to young and elderly, normal and pathological gait analysis. As a result, our model shows good performance for the scale estimation and gait analysis.

A Simulation Study on Performance Validation of Indoor Location Estimation Based on the Radial Positive Distribution

We study the problem of analyzing indoor location estimation by statistical radial distribution model. In this study, we suppose the observed distance data between transmitter and receiver as a statistical radial distribution. The proposed method is based on the marginal likelihoods of radial distribution generated by positive distribution among the several transmitter radio sites placed in the room. In this paper we compare the performance of radial Weibull distribution and radial log-normal distribution for the indoor location estimation. To compare the performance of each distribution based approach, we conducted simulation study of location estimation. As a result, radial Weibull distribution based method shows high accuracy for the indoor spatial location estimation.

Statistical Recognition of Aspiration Presence

A study on the healthcare application is very important for the solitary death in aging society. Many previous works had been proposed a detection method of aspiration using the non-contact radar. But the works are only in subjects with sitting in a chair. We consider that user falls down in the state when he happen abnormal situation as daily life. In this study, we focus on the detection of “aspiration” or “apnea” for the lying position, because the final decision of the life or death is aspiration. As initial stage of the system, we propose the recognition method for the presence of aspiration with lying position under the low-disturbance environment from microwave Doppler signals by using support vector machine (SVM).