WiFi sensing has recently received significant interest from academics, industry, healthcare professionals and other caregivers (including family members) as a potential mechanism to monitor our aging population at distance, without deploying devices on users bodies. In particular, these methods have gained significant interest to efficiently detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems stems from its practical deployments in indoor settings and compliance from monitored persons, unlike other sensors such as wearables, camera-based, and acoustic-based solutions. This paper reviews state-of-the-art research on collecting and analysing channel state information, extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, untapped areas, and related trends.This work aims to provide an overarching view in understanding the technology and discusses its uses-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

Vision-based gait analysis and human identification systems have been widely proposed in the literature. However, these systems cannot be readily applied in many real-time applications due to involved challenges such as video quality, occlusion, and serious privacy concerns. To overcome such issues, we propose the LoGait system that leverages ubiqutous LoRa signals recognise gait in different indoor environments. Our work is based on the intuition that the walking pattern of different users can be distinguished by distinct stride size and frequency. The wireless LoRa signal which is interfered by human walking will capture the gait information of subjects. In combination with the long-distance transmission ability of LoRa signal, the system enables a larger sensing range of gait recognition compared to the WiFi-based gait recognition system. The proposed LoGait system utilizes the phase difference between two LoRa receive channels, along with a set of filtering techniques, to extract distinctive features and generate a human gait profile. This profile is then matched against a database using a dynamic time wraping (DTW) based recognition algorithm, enabling accurate identification based on unique gait patterns. It has been validated in three different scenarios for gait recognition namely line of sight (LOS), non-line of sight (NLOS), and long-distance, with accuracy of 85.13%, 79.14%, and 84.14%, respectively.

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being.

We present RFSensingGPT, an integrated framework for radio frequency (RF) sensing that combines technical question-answering, code retrieval, and spectrogram analysis through retrieval-augmented generation (RAG). Our framework addresses the fundamental challenge of applying large language models to RF sensing applications, where specialized domain knowledge is underrepresented in general training corpora. The system leverages a filtered RedPajama dataset containing RF-relevant technical documents, processed through a hybrid retrieval mechanism that combines vector-based similarity search with best match (BM25)-based query fusion. Performance evaluation using document collections ranging from 5K to 80K demonstrates that RAG consistently maintains superior faithfulness across all dataset sizes (0.9033-0.9779 vs 0.8162-0.8506, average improvement of 13.0%) compared to baseline LLM implementations. Our hierarchical chunking approach using MarkdownHeaderTextSplitter achieves optimal precision (0.31-0.32) at lower k-values while maintaining correctness scores of 4.0-5.0. The framework integrates CLIP-based vision models for RF pattern recognition, achieving 93.23% accuracy in radar data analysis tasks. Implementation benchmarks show efficient processing with minimal GPU memory requirements (0.66GB) even at scale. Through a comprehensive evaluation of the embedding models, RFSensingGPT establishes a new benchmark for technical query understanding and RF spectrogram analysis in the emerging field of integrated sensing and communications systems for 6G networks.

Video inpainting aims to fill in missing regions of a video after any undesired contents are removed from it. This technique can be applied to repair the broken video or edit the video content. In this paper, we propose a depth-guided deep video inpainting network (DGDVI) and demonstrate its effectiveness in processing challenging broken areas crossing multiple depth layers. To achieve our goal, we divide the inpainting into depth completion, content reconstruction, and content enhancement. Three corresponding modules are designed to implement a process-flow. Firstly, we develop a depth completion module based upon the spatio-temporal Transformer which is used to obtain the completed depth information for each video frame. Secondly, we design a content reconstruction module to generate initially inpainted video. With this module, the contents of the missing regions are composed via the depth-guided feature propagation. Thirdly, we construct a content enhancement module to enhance the temporal coherence and texture quality for the inpainted video. All of proposed modules are jointly optimized to guarantee the high inpainting efficiency. The experimental results demonstrate that our proposed method provides better inpainting results, both qualitatively and quantitatively, compared with the previous state-of-the-art. The code is available at https://github.com/lishibo888/DGDVI .

Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject’s lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition.