In the field of indoor human behavior recognition, traditional deep learning methods often underperform in adequately capturing temporal features and core information in RFID behavior data, leading to suboptimal predictive performance. We note that human behavior is primarily composed of complex action sequences, where certain sequences formed by Received Signal Strength Indicator (RSSI) values may exhibit more representative and information-rich patterns. Therefore, we propose SATCN: an innovative model that leverages Temporal Convolutional Networks (TCN) and self-attention mechanisms for advanced RFID-based human behavior recognition. The SATCN model employs TCN to adeptly capture the inherent temporal dependencies within behavior data, while integrating self-attention mechanisms to focus on high-value features in the data. Experimental validation on RFID indoor human behavior recognition dataset reveals that SATCN achieves an accuracy of 94.51%, a 10.83 percentage point increase over existing models, highlighting SATCN’s potential in enhancing the accuracy of RFID-based indoor human behavior recognition.

Amidst the rapid advancement of Internet of Things (IoT) technology, achieving precise indoor localization has emerged as a pivotal research area. Localization algorithms relying on Radio Frequency Identification (RFID) received signal strength indicator (RSSI) have gained widespread adoption in numerous indoor positioning systems due to their straightforward implementation and cost-effectiveness. However, in indoor settings, challenges like building obstructions and multipath effects often lead to signal reception failures by RFID antennas, consequently compromising the reliability of positioning outcomes. Recent research has approached indoor localization as a regression problem, employing deep learning models for analysis and prediction. But most current indoor localization models primarily focus on either spatial or temporal features within RSSI data, leading to suboptimal localization outcomes. To tackle these challenges, this paper proposes an enhanced methodology that leverages Generative Adversarial Networks (GAN) to impute missing RSSI data. Additionally, Convolutional Neural Networks (CNN) are utilized to extract spatial domain features, while Long Short-Term Memory Networks (LSTM) are employed for extracting temporal domain features. Ultimately, this paper designs a novel model, GCLA, which integrates an Attention mechanism with a location coding strategy to fuse features for precise location prediction. Experimental results show that the proposed GCLA model can obtain stable localization results after a short training on a small number of datasets.

Performing cross-domain activity recognition using a few samples is a challenge in the field of radio frequency identification (RFID) sensing. Due to multipath effect, RF signals collected from different domains exhibit unbalanced label distribution and heterogeneous signal distribution, which leads to domain shift problem. Moreover, constrained by cost, the labeled data available for training in each domain is limited. In this paper, we propose RFRN, an RFID activity recognition system based on Relation Network, and improve the recognition accuracy through several designs. First, the structure of Relation Network is improved to extract activity-related features, filter domain-related interference and then match different activities without additional fine-tuning. Second, a new task generation strategy is proposed to make full use of source domains and enable the model to experience more tasks. Experiments demonstrate that RFRN can effectively handle the domain shift problem and adapt well to new domains with a few samples. With one and five samples of each activity, RFRN outperforms the baselines by at least 7.3% and 4.0% on a real-world dataset with balanced label distribution and around 22.2% and 7.7% on a dataset with unbalanced label distribution, respectively.

Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning.

Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years.At present, the research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method.Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on singleobject activity recognition, which is not suitable for multi-object scenarios.To address these issues, we propose DeepMultiple, a novel tag non-attachment activity recognition model for multiobject.The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally ProbSparse is used to optimize the vanilla Transformer-based Encoder to enhance the activity recognition ability.We deployed a single reader and antenna for multi-object activity tracking during the experiments to reduce deployment difficulties.Extensive experimental results show that DeepMultiple can recognize ten types of multi-object activities with 98.12% precision under different challenging settings, which has excellent performance compared with several state-of-the-art methods.

Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years. At present, research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method. Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on single-object activity recognition, which is not suitable for multi-object scenarios. To address these issues, we propose STMultiple, a novel tag non-attachment activity recognition model for multi-object. The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally the feature pyramid structure and ProbSparse are used to optimize the vanilla Transformer-Encoder to enhance the activity recognition ability. Extensive experiments show that STMultiple can achieve recognition accuracy of up to 97.93% and down to about 90% in challenging environments ranging from two to five users, which has excellent performance compared to several state-of-the-art methods.