Lightweight Regularized Network for Multilabel Indoor HAR in Multiuser CSI Environments With Uncertainty Quantification

In this work, we address the issue of Wi-Fi-based human activity recognition (HAR) system in densely deployed Wi-Fi environments. With the benefit of sufficient information provided by Wi-Fi channel state information (CSI), HAR based on Wi-Fi has become an active research area in recent years. Traditional Wi-Fi CSI-based HAR applications usually focus on utilizing one Wi-Fi transmitter and one or several Wi-Fi receivers to extract the activity-related features, ignoring the communication among multiple Wi-Fi devices in the real world. In this paper, we present a novel Wi-Fi link selection model on the basis of continuous state decision-making process in which CSI is modeled as a part of the state. The model, referred to as WiAgent, takes an action of selecting one Wi-Fi link according to current state, and then updates the state for the choice of the next action. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi transmitters exist.

Neuromorphic computing with hybrid CNN–Stochastic Reservoir for time series WiFi based human activity recognition

Dear Editor, Human activity recognition (HAR) using WiFi signals has been a significant task due to its potential applications in for example, healthcare services and smart homes. This letter deals with the WiFi channel state information (CSI)-based HAR task. To capture the dynamics of human activities well from CSI without using a huge number of training samples, we propose a recurrent model of convolution blocks and transformer encoders. Firstly, the model utilizes the convolution blocks to capture local variation and the self-attention mechanism in transformer encoders to characterize long-range dependencies. Secondly and more importantly, the recurrent architecture models the context information well within CSI signals and allows the network to deepen without scale increase, making it particularly suited to learning from a small amount of CSI samples.

Fine-grained occupant activity monitoring with Wi-Fi channel state information: Practical implementation of multiple receiver settings

We develop a novel deep learning technique for human fall detection using WiFi Channel State Information (CSI) of a WiFi transmitter and receiver. Different motions in the environment generate distinct features in CSI, which can be fed to a supervised learning machine learning algorithm for training. However, the CSI varies from one environment to another, requiring the collection of environment-specific training data. To overcome this challenge, we propose 1-d convolutional neural network using domain adaptation technique. By adapting to un-labeled data from a new environment, we significantly improve precision and recall, making activity recognition accurate in new environments.

This study proposes a new deep learning method for reconstructing depth images of moving objects within a specific area using Wi-Fi channel state information (CSI). The Wi-Fibased depth imaging technique has novel applications in domains such as security and elder care. However, reconstructing depth images from CSI is challenging because it is difficult to learn the mapping function between CSI and depth images, both of high-dimensionalities. To address the challenge, we propose Wi-Depth. The main idea behind the design of Wi-Depth is that a depth image of a moving object can be decomposed into three core components: the shape, depth, and position of the target. Therefore, in the depth-image reconstruction task, Wi-Depth simultaneously estimates the three core pieces of information as auxiliary tasks in our proposed VAE-based teacher-student architecture, enabling it to output images with the consistency of a correct shape, depth, and position. In addition, the design of Wi-Depth is based on our idea that this decomposition efficiently takes advantage of the fact that shape, depth, and position relate to primitive information inferred from CSI such as angle-of-arrival, time-of-flight, and Doppler frequency shift.

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

In human presence detection for Internet of Things (IoT) applications, Wi-Fi channel state information (CSI)-based methods offer significant advantages in cost-effectiveness and privacy preservation. Existing approaches typically rely on feature engineering or deep learning techniques to process CSI data. However, these methods often struggle to accurately model the complex, probabilistic nature of CSI measurements, particularly in environments with nonhuman moving elements. This leads to suboptimal detection probabilities under low false alarm rate (FAR) conditions. Therefore, this article introduces Wi-CCFAR, a controllable FAR human presence detector utilizing Wi-Fi CSI data. First, we employ a normalizing flow network with spline transformations for data modeling, capturing the complexity of CSI measurement distribution. Second, to address the inherent limitation of the same input and output dimensions in normalizing flow networks, we introduce an efficient feature extractor, which reduces input dimensions and computational requirements while maintaining high detection probabilities by preserving essential information. Last, we implement an enhanced information bottleneck loss function, which balances data fitness and class divergence, to achieve favorable detection probabilities under low FAR conditions. Experimental results demonstrate that Wi-CCFAR outperforms conventional methods, achieving higher detection probabilities with lower false alarm probabilities, even in challenging scenarios with various interferences such as moving objects and multiple occupants.

In the context of the progressive development of integrated communication and sensing technology, indoor motion detection relying on Wi-Fi Channel State Information (CSI) has attracted significant attention within the industry [1][2][3]. Nevertheless, the home environment presents several challenges that impinge on the reliability of CSI. These challenges include interference [4], intricate spatial arrangements, and the instability of device placement. Moreover, motion sensing based on the CSI paths between terminals and routers may encounter problems such as unpredictable effective ranges [5] and difficulties in penetrating walls. To surmount these obstacles, this paper proposes the following technologies: a) adaptive Signal-to-Noise Ratio (SNR) enhancement; b) adversarial sample training; c) a method for refining spatial granularity; and d) a lightweighting approach for edge-side models. These technologies enable the detection of region-specific human activities through Wi-Fi CSI. The experimental findings demonstrate a reduction in false detection rates and the successful implementation of deep-learning models on compact communication equipment.

Many WiFi signal based deep learning approaches on human activity recognition (HAR) were proposed by researchers, for the activity of human in the WiFi environment will affect the Received Signal Strength Indicator (RSSI) and wireless Channel State Information (CSI). Conventional methods concentrated on RSSI or amplitude of CSI, which can't fully characterize the dynamics of the human activity hidden in the signal. For utilizing the full information in CSI with both amplitude and phase, we proposed a new HAR algorithm by using complex-value neural network (CVNN) combined with complex-value transformer (CVTransformer). In the proposed scheme, we treat the complex CSI information as a complex valued image, then we can use CVNN extract different activity features from the CSI “image”, and introduce complex attention mechanism by CVTransformer into the CVNN to enhance the capability to recognize key feature of the activity, thus increase the precision of the activity recognition. Experimental result shows that the proposed approach can effectively recognize most of the human activities in the datasets, with a precision of more than 96.24%, outperforms the related approach.

Human action recognition (HAR)in videos is a challenging task in computer vision. Conventional methods are prone to explore the spatiotemporal or optical representations for video actions. However, optical representation might be inefficient in some real-life situations, such as object occlusion and dim light. To address this issue, this paper presents a novel approach for human action recognition by jointly exploiting video and Wi-Fi clues. We leverage the fact that Wi-Fi signals carry discriminative information of human actions, which is robust to optical limitations. To validate this innovative thought, we conceive a practical framework for HAR and setup a dataset containing both video clips and Wi-Fi Channel State Information of human actions. The 3D convolutional neural network was used to extract the video features and the statistical algorithms were used to extract radio features. A classical linear support vector machine is employed as the classifier after the video and radio feature fusion. Comprehensive experiments on this dataset achieved desirable results with the maximum improvement in accuracy by 10%. This demonstrates our promising findings: with the aid of Wi-Fi Channel State Information, the performance of the video action recognition methods can be improved significantly, even under the optical limitation.

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

Device free human gesture recognition using Wi-Fi CSI: A survey

Queue counting using WiFi channel state information (CSI) faces challenges due to susceptibility to external factors and relies on ideal testing environments for current methods. We propose an efficient CSI recurrence plot (RP)-based framework for queue counting (CRPF-QC), containing a transformation module and a recognition module. The conversion module transforms the CSI into RP, distinct from traditional models using a single signal point as the unit for feature extraction, utilizing the signal changes at different timestamps as units for feature extraction and effectively preserving the amplitude and phase relationships between any two time points. In the recognition module, the convolutional neural network (CNN) and the long short-term memory (LSTM) network are combined to profoundly understand the internal structure and changes within the image. The proposed integration framework is adept in the automatic extraction of amplitude and phase features, therefore improving image recognition accuracy. Meanwhile, we explore dynamic changes in the queuing crowd detection based on the Fresnel zone theory, identifying individuals’ entering and exiting behaviors at different positions within the Fresnel zone and updating the count accordingly, which makes up for the shortcomings of the static model. Intensive evaluations demonstrate that CRPF-QC, employing just two layers of CNN and one layer of LSTM, excels in adapting to dynamic environmental changes, outperforming traditional queue counting methods. Additionally, the dynamic model attains a perfect 100% accuracy in both scenarios.

Crowd counting is of great importance to many applications. Conventional vision-based approaches require line of sight and pose privacy concerns, while most radio-based approaches involve high deployment cost. In this paper, we propose to utilize WiFi channel state information (CSI) to infer crowd count in a device-free way, with only one pair of WiFi transmitter and receiver. The proposed method establishes the statistical relationship between the variation of CSI and the number of people with deep neural networks (DNN) and thereafter estimates the people count according to the real-time CSI through the trained DNN model. Evaluations demonstrate the effectiveness of the method. For the crowd size of 6, the counting error was within 1 person for 100% of the cases. For the crowd size of 34, the counting error was within 1 person for 97.7% of the cases and within 2 persons for 99.3% of the cases.