Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

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 technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%.