The field of human activity recognition (HAR) using channel state information (CSI) is advancing rapidly through recent research. In this study, we propose and investigate a novel lightweight and fast-response gated recurrent unit (LFR-GRU) model that can distinguish continuous dynamic and static postures over time of a person without electronic devices in an indoor environment by utilizing the propagation characteristics of Wi-Fi signals. The CSI time-series amplitude data used in this study are collected from the characteristics of continuous movements and postures in two directions (horizontal and vertical) using an 802.11n Intel network interface card and a Wi-Fi access point. The proposed LFR-GRU model is evaluated using criteria such as F1-score, total number of parameters, learning time, processing speed, and memory usage, and its performance is compared with conventionally available models like 1D convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and Bi-GRU. The experiment results show that the proposed LFR-GRU model achieved an F1-score accuracy of 94.16% when trained with one person’s data and 96% accuracy when trained with two people. Additionally, it demonstrates comprehensively improved performance compared to other time-series models in terms of weight reduction, computational cost, and fast response (the model’s ability to respond quickly to input) speed. Based on these results, it can be confirmed that the proposed LFR-GRU model has sufficient potential to real-time HAR applications in indoor environments.

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%.

Sensor-related indoor localization has attracted considerable attention in recent years. The accuracy of conventional fingerprint solutions based on a single sensor, such as a Wi-Fi sensor, is affected by multipath interferences from other electronic devices that are produced as a result of complex indoor environments. Light sensors and magnetic (i.e., geomagnetic) field sensors can be used to enhance the accuracy of a system since they are less vulnerable to disturbances. In this paper, we propose a deep feedforward (DFF)-neural-network-based method, termed DFF-WGL, which integrates the data from the embedded Wi-Fi sensor, geomagnetic field sensor, and light sensor (WGL) in a smart device to localize the device in an indoor environment. DFF-WGL does not require complex and expensive auxiliary equipment, except for basic fluorescent lamps and low-density Wi-Fi signal coverage, conditions that are easily satisfied in modern offices or educational buildings. The proposed system was implemented on a commercial off-the-shelf android device, and performance was evaluated through an experimental analysis conducted in two different indoor testbeds, one measuring 60.5 m² and the other measuring 38 m², with 242 and 60 reference points, respectively. The results indicate that the model prediction with an input consisting of the combination of light, a magnetic field sensor, and two Wi-Fi RSS signals achieved mean localization errors of 0.01 m and 0.04 m in the two testbeds, respectively, compared with any subset of combination of sensors, verifying the effectiveness of the proposed DFF-WGL method.

Emergency scenario recognition has diverse applications, including monitoring older adults living alone and detecting crimes. Predominantly, emergency scenario recognition methods rely on cameras, presenting privacy issues, especially in bathrooms and homes. Additionally, most studies concentrate on recognizing simple isolated activity such as falling down and lying down. However, real-life emergency scenarios often more than just isolated activity. This paper introduces a method for classifying emergency scenarios, including falling down, lying down, as well as the combined activities such as falling down and standing up, lying down and standing up, falling down and crawling, and lying down and crawling. Falling and lying down are similar scenarios, and we consider that the people may stand up or crawl afterward. Our approach hinges on the micro-Doppler features derived from continuous-wave radar signals. We first process the human-generated radar echo signals to produce micro-Doppler spectrogram images. Subsequently, we propose an AI model utilizing multi-head attention mechanisms for image training and testing, demonstrating strong performance on our small datasets while reducing computational complexity compared to precious vision transformer models. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 95%.

Device-free localization (DFL) is a technology designed to determine the positions of targets without the need for them to carry electronic devices. It achieves this by analyzing the shadowing effects of radio links within wireless sensor networks (WSNs). However, obtaining high precision in DFL often results in increased energy consumption, severe electromagnetic interference, and other challenges that impact positioning accuracy. Most DFL schemes for accurate tracking require substantial memory and computing resources, which make them unsuitable for resource-constrained applications. To address these challenges, we propose an intelligent mesh cluster (IMC) algorithm that achieves accurate tracking by adaptively activating a subset of wireless links. This approach not only reduces electromagnetic interference but also saves energy. The IMC algorithm leverages geometric objects, such as meshes and mesh clusters formed by wireless links, to achieve low computational complexity. By scanning a subset of mesh cluster-related wireless links near the DFL target, the algorithm significantly reduces the computational requirements. The target’s location estimate is determined based on the connection information among the mesh clusters. We conducted numerous simulations to evaluate the performance of the IMC algorithm. The results demonstrate that the IMC algorithm outperforms grid-based and particle filter-based DFL methods, confirming its effectiveness in achieving accurate and efficient localization.

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information.