Jihoon Hong

Signal Eigenvector-Based Device-Free Passive Localization Using Array Sensor

Device-free passive (DFP) localization techniques have received increasing attention for location-based services due to their ability to realize localization without holding any wireless device. Most of the existing DFP localization systems are based on the measurement of received signal strength (RSS) only. However, the localization accuracy is easily affected by the spatial and temporal variance of RSS due to multipath fading and noise, even in a static environment. In this paper, we propose a novel localization system for DFP localization using an array sensor, which uses an antenna array at a receiver and is mainly based on the signal eigenvector. We use a fingerprinting technique with multiclass support vector machines (SVMs) based on a combination of array signal features with spatial and temporal averaging. We evaluate the localization performance of our proposed system in different propagation environments, i.e., line-of-sight (LOS) and non-line-of-sight (NLOS). In addition, we analyze two types of receive antenna placement, i.e., centralized and distributed antennas. The experimental results show that the localization accuracy can be improved by the proposed system, particularly in the centralized antenna case. Moreover, they show that the proposed system can improve localization accuracy compared with the conventional RSS-only-based system.

Ambient intelligence sensing using array sensor: device-free radio based approach

In this paper we introduce a novel device-free radio based activity recognition with localization method with various applications, such as e-Healthcare and security. Our method uses the properties of the signal subspace, which are estimated using signal eigenvectors of the covariance matrix obtained from an antenna array (array sensor) at the receiver side. To classify human activities (e.g., standing and moving) and/or positions, we apply a machine learning method with support vector machines (SVM). We compare the classification accuracy of the proposed method with signal subspace features and received signal strength (RSS). We analyze the impact of antenna deployment on classification accuracy in non-line-of-sight (NLOS) environments to prove the effectiveness of the proposed method. In addition, we compare our classification method with k-Nearest Neighbor (KNN). The experimental results show that the proposed method with signal subspace features provides accuracy improvements over the RSS-based method.

Cooperative fall detection using Doppler radar and array sensor

Doppler radar-based fall detection has been attractive due to its low cost and high detection performance. However, fall detection based on Doppler signatures is affected by the spatial variance due to the multipath and non-line-of-sight (NLOS) effects, which has been one of the key issues for detection performance in current Doppler radar-based systems. Moreover, the drawbacks of Doppler radar are the limited measurement range and sensitivity to the targets falling directions. In this paper, we present a cooperative fall detection system that uses a Doppler radar and an array sensor which can be used even for multipath and NLOS environments. We analyze the impact of Doppler signatures in multipath and NLOS environments and account for undesirable Doppler measurements. We use the temporal-spatial characteristics of the signals using an array sensor and propose a novel fall detection system, which cooperates with the Doppler radar to enhance fall detection performance in multipath and NLOS environments. We evaluate the proposed system performance in a typical laboratory environment in LOS and NLOS conditions. The experimental results show that the proposed system reduces the false alarm rate and improves true positive rate. Moreover, our proposed system can significantly enhance the fall detection accuracy compared with the corresponding only Doppler radar-based approach.

Activity recognition using low resolution infrared array sensor

Now, aging society is a worldwide problem, and the population of people aged over 60 years is growing faster than any other age group. Therefore, monitoring services for elderly people are attracting a great deal of attention. We have proposed a fall detection method using a low resolution infrared array sensor to inform an unexpected falling in our previous work. However, knowing daily fundamental activities of elderly people is also important to prevent future accidents. In this paper, we propose an activity recognition method using a low resolution infrared array sensor. This sensor can detect temperature on a two dimensional area. From the viewpoint of general versatility (available in darkness), cost, size, privacy (low resolution), and availability (commercial off-the-shelf), this sensor is better than other sensing devices like video cameras, Doppler radars, acceleration sensors, and so on. In the proposed method, temperature distribution obtained from the sensor is analyzed and classified into five fundamental states: “No event”, “Stopping”, “Walking”, “Sitting”, and “Falling” (emergency situation). As a result of experiments, our proposed method achieved recognition accuracy of 100 %, 94.8 %, 99.9 %, and 78.6 % respectively. In particular, 100 % accuracy of “Falling” recognition was achieved.

A fall detection system using low resolution infrared array sensor

Nowadays, aging society is a big problem and demand for monitoring systems is becoming higher. Under this circumstance, a fall is a main factor of accidents at home. From this point of view, we need to detect falls expeditiously and correctly. However, usual methods like using a video camera or a wearable device have some issues in privacy and convenience. In this paper, we propose a system of fall detection using a low resolution infrared array sensor. The proposed system uses this sensor with advantages of privacy protection (low resolution), low cost (cheap sensor), and convenience (small device). We propose four features and based on them, classify activities as either a fall or a non-fall using k-nearest neighbor (k-NN) algorithm. We show a proof-of-concept of our proposed system using a commercial-off-the-shelf (COTS) hardware. Results of experiments show the detection rate of higher than 94% irrespective of training data contains objects data or not.

A state classification method based on space-time signal processing using SVM for wireless monitoring systems

In this paper we focus on improving state classification methods that can be implemented in elderly care monitoring systems. The authors group has previously proposed an indoor monitoring and security system (array sensor) that uses only one array antenna as the receiver. The clear advantages over conventional systems are improvement of privacy concern from the usage of closed-circuit television (CCTV) cameras, and elimination of installation difficulties. Our approach is different from the previous detection method which uses an array of sensors and a threshold that can classify only two states: nothing and something happening. In this paper, we present a state classification method that uses only one feature obtained from the radio wave propagation, and assisted by multiclass support vector machines (SVM) to classify the occurring states. The feature is the first eigenvector that spans the signal subspace of interest. The proposed method can be applied to not only indoor environments but also outdoor environments such as vehicle monitoring system. We performed experiments to classify seven states in an indoor setting: “No event,” “Walking,” “Entering into a bathtub,” “Standing while showering,” “Sitting while showering,” “Falling down,” and “Passing out;” and two states in an outdoor setting: “Normal state” and “Abnormal state.” The experimental results show that we can achieve 96.5 % and 100 % classification accuracy for indoor and outdoor settings, respectively.

IGMM-Based Co-Localization of Mobile Users With Ambient Radio Signals

Co-localization of mobile users combines methods of detecting nearby users and providing them interesting and useful services or information. By exploiting the massive use of smartphones, nearby users can be co-localized using only their captured ambient radio signals. In this paper, we propose a real-time co-localization system, in a centralized manner, that leverages co-located users with high accuracy. We exploit the similarity of radio frequency measurements from users’ mobile terminal. We do not require any further information about them. Our co-localization system is based on a nonparametric Bayesian method called infinite Gaussian mixture model that allows the model parameters to change with observed input data. In addition, we propose a modified version of Gibbs sampling technique with an average similarity threshold to better fit user’s group. We design our system in a completely centralized manner. Hence, it enables the network to control and manage the formation of the users’ groups. We first evaluate the performance of our proposal numerically. Then, we carry out an extensive experiment to demonstrate the feasibility, and the efficiency of our approach with data sets from a real-world setting. Results on experiment favor our algorithm over the state-of-the-art community detection-based clustering method.

Hidden Markov model based localization using array antenna

We present a hidden Markov model based localization using array sensor. In this method, we use the eigenvector spanning signal subspace as a feature for location. The eigenvector does not depend on received signal strength (RSS) but on direction of arrival (DOA) of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the electric wave. The conventional method based on fingerprinting does not take previous information into account. In this paper, we propose an algorithm that applies HMM to conventional fingerprinting of the eigenvector. This algorithm takes previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference location according to setting in advance. In an indoor environment represented in a quantized grid, we decide the HMM transition probabilities denoting the possible moving range from previous estimation location. The most likely trajectory is calculated by means of the Viterbi algorithm. The results show that the localization accuracy is improved owing to the use of a possible moving range from the previous location.

Detecting unexpected fall using array antenna

Nowadays, the population of the elderly people is increasing in Japan. The system to protect an elderly person who is living alone is needful. The bigger part of the accident in their house is falling. There are several kinds of products to detect a persons fall. However, there are some problems that it does not have enough detection range and privacy. Array antenna is an antenna, which detects radio wave propagation by observing the direction of arrival (DOA) of the signal. We developed previously the fall detection using array antenna. In the conventional fall detection method, it is needed to learn and observe the whole activity scenario, which includes how the person moves before and after the fall. As a result, when the unexpected fall scenario happens, it is not able to detect the fall correctly. In this paper, we propose a detection algorithm for unexpected fall using array antenna. We detect fall for every fixed time, and by the results of the detection, we decide whether the activity is falling or not. By using this method, it can detect the unexpected fall scenario, which is not learned. In addition, we use new features for support vector machine (SVM) to distinguish confusing activities and improve the fall detection accuracy.

Array Antenna based Localization Using Spatial Smoothing Processing

An array antenna based localization using spatial smoothing processing (SSP) is proposed for wireless security and monitoring, referred to as array sensor. The proposed method is based on the array sensor that exploits an array antenna at the receiver to detect the propagation environment of interest. If an event occurs, e.g., human motion, the propagation environment is changed. Thus the eigenvector and eigenvalue spanning the signal subspace that is inherent to its environment changes as well. Using a machine learning technique based on the eigenvector and eigenvalue, we can detect the event accurately. The proposed method is improved from our previous work which uses only a limited number of signal subspace features. The basic idea of this work is the extension of the dimension of the signal subspace by using SSP without increasing the number of array element. In addition, this work investigates the impact of the array antenna placement on localization performance. The experimental results show that the proposed SSP based method achieves a 41.83 % improvement in localization accuracy, and a 1.24 m improvement in root mean square error (RMSE) compared to the previous method.