Device-Free Presence Detection and Localization With SVM and CSI Fingerprinting

Zhou, Rui; Lu, Xiang; Zhao, Pengbiao; Chen, Jiesong

doi:10.1109/jsen.2017.2762428

Cited by 157 publications

(70 citation statements)

References 22 publications

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“…Table I and Table II summarize the location-specific statistical measures, i.e., precision and recall, for the conference room experiment and lounge experiment, respectively, for DNN-based device-free indoor localization system in comparison with k-NN (k = 5, the best-performing configuration 2 ) and SVM. k-NN classification is based on the raw CSI data and is adopted to benchmark the analysis of DNN in Sections IV and V. SVM with a Gaussian radial basis function (RBF) kernel and the one-against-all technique [8], [21] for solving the multi-classification problem is adopted. The precision and recall measure the percentages of correct classification for each predicted and true class in the testing, respectively.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…Gao et al [14] used a multilayer deep learningbased image processing framework to learn the optimized deep features from the radio images (transformed from the amplitude and phase information of the measured CSI) to estimate the location and activity of a target person. Note that [9]- [11] are device-based and [6]- [8], [12]- [14] are devicefree indoor localization schemes.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization

2019

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Device-free Wi-Fi indoor localization has received significant attention as a key enabling technology for many Internet of Things (IoT) applications. Machine learning-based location estimators, such as the deep neural network (DNN), carry proven potential in achieving high-precision localization performance by automatically learning discriminative features from the noisy wireless signal measurements. However, the inner workings of DNNs are not transparent and not adequately understood especially in the indoor localization application. In this paper, we provide quantitative and visual explanations for the DNN learning process as well as the critical features that DNN has learned during the process. Toward this end, we propose to use several visualization techniques, including: 1) dimensionality reduction visualization, to project the high-dimensional feature space to the 2D space to facilitate visualization and interpretation, and 2) visual analytics and information visualization, to quantify relative contributions of each feature with the proposed feature manipulation procedures. The results provide insightful views and plausible explanations of the DNN in device-free Wi-Fi indoor localization using channel state information (CSI) fingerprints.Index Terms-Wireless indoor localization, fingerprinting, channel state information (CSI), machine learning, deep neural networks (DNN), Internet of Things (IoT), visual analytics.

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Section: Deep Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization

2019

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“…Hu et al [38] proposed a method to detect human motion based on phase eigenvalue and then combined with covariance matrix and dynamic time window algorithm; finally, the experimental results of the HPMD system obtained a high-detection rate. Zhou et al [39] put forward a passive indoor location and detection method based on CSI. This method firstly collects effective CSI data, uses PCA algorithm for feature extraction and dimensionality reduction, then establishes fingerprint database, and finally, carries out on-line detection.…”

Section: Human Behavior Detection With Csimentioning

confidence: 99%

“…With the continuous progress and development of wireless sensor networks (WSNs), people's research perspective has not only limited to the traditional location awareness and indoor location [1]. For example, the typical ultra-wideband (UWB)-based radar system [2] and the relatively new indoor positioning technology based on commercial [3] Wi-Fi infrastructure have better development advantages in all aspects [4].…”

Section: Introductionmentioning

confidence: 99%

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

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Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm's estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human's behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

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“…WiFi sensing-based monitoring systems only require a WiFi router or access point and one or more WiFi enabled devices. WiFi sensing with CSI measurements have been used in various applications, such as human presence/localization [20][21][22], activity recognition [23][24][25], fall classification and detection [26][27][28][29], gesture recognition [30][31][32], and user identification [33][34][35].Recent work has leveraged WiFi sensing for human presence detection and localization. Qian et al[20] used a WiFi-based Multiple Inputs and Multiple Outputs (MIMO) system and CSI measurements to detect presence of humans with dynamic movement speeds utilizing a Support Vector Machine (SVM), resulting in a true positive rate greater than 93%.…”

mentioning

confidence: 99%

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

et al. 2019

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Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson's Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time-frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.China [3]. Every 1 in 37 people in the United Kingdom are diagnosed with PD at some stage of their life [1]. PD is caused by the neuronal loss and damage in the motor area of the brain, particularly the centers of the limbic, visceromotor, and somatomotor systems [4]. Most PD patients experience difficulty in walking, which indicates an onset of gait, balance, and other disabilities [5]. Half of the PD patients experience Freezing of Gate (FOG) symptoms, which represent an episodic absence of forward feet movement, despite of the intention for forward progress [6]. The frequency as well as the duration of FOG episodes increases with the progression of PD. FOG and falls are both interconnected phenomena, and FOG is known to hinder daily activities and increase fall risk [7], resulting in severe consequences for older adults.Conventional clinical methods to validate FOG occurrences and their severity are based on self-report questionnaires [8] and doctors' reports based on direct observation. Due to the impact of FOG on population in general and falls in older adults in particular, there is a high demand for automating the process of detection and recognition of FOG events. Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detectio...

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Device-Free Presence Detection and Localization With SVM and CSI Fingerprinting

Cited by 157 publications

References 22 publications

Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization

Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

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