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1. Artificial Intelligence-Driven Wireless Sensing for Health Management

   https://doi.org/10.3390/bioengineering12030244  

   Toruner, Merih Deniz; Shi, Victoria; Sollee, John; Hsu, Wen-Chi; Yu, Guangdi; Dai, Yu-wei; Merlo, Christian; Suresh, Karthik; Jiao, Zhicheng; Wang, Xuyu; et al (March 2025, Bioengineering)

   Zaza, Gianluca; Gallo, Crescenzio (Ed.)

   (1) Background: With technological advancements, the integration of wireless sensing and artificial intelligence (AI) has significant potential for real-time monitoring and intervention. Wireless sensing devices have been applied to various medical areas for early diagnosis, monitoring, and treatment response. This review focuses on the latest advancements in wireless, AI-incorporated methods applied to clinical medicine. (2) Methods: We conducted a comprehensive search in PubMed, IEEEXplore, Embase, and Scopus for articles that describe AI-incorporated wireless sensing devices for clinical applications. We analyzed the strengths and limitations within their respective medical domains, highlighting the value of wireless sensing in precision medicine, and synthesized the literature to provide areas for future work. (3) Results: We identified 10,691 articles and selected 34 that met our inclusion criteria, focusing on real-world validation of wireless sensing. The findings indicate that these technologies demonstrate significant potential in improving diagnosis, treatment monitoring, and disease prevention. Notably, the use of acoustic signals, channel state information, and radar emerged as leading techniques, showing promising results in detecting physiological changes without invasive procedures. (4) Conclusions: This review highlights the role of wireless sensing in clinical care and suggests a growing trend towards integrating these technologies into routine healthcare, particularly patient monitoring and diagnostic support. 

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2. Demo abstract: Towards Real-Time Rich Scene Analysis Using Vision-Guided Wireless Vibrometry

   https://doi.org/10.1145/3560905.3568060  

   Wang, Ziqi; Sarker, Ankur; Wu, Jason; Hua, Derek; Dong, Gaofeng; Singh, Akash Deep; Srivastava, Mani (November 2022, Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems)

   Intelligent systems commonly employ vision sensors like cameras to analyze a scene. Recent work has proposed a wireless sensing technique, wireless vibrometry, to enrich the scene analysis generated by vision sensors. Wireless vibrometry employs wireless signals to sense subtle vibrations from the objects and infer their internal states. However, it is difficult for pure Radio-Frequency (RF) sensing systems to obtain objects' visual appearances (e.g., object types and locations), especially when an object is inactive. Thus, most existing wireless vibrometry systems assume that the number and the types of objects in the scene are known. The key to getting rid of these presumptions is to build a connection between wireless sensor time series and vision sensor images. We present Capricorn, a vision-guided wireless vibrometry system. In Capricorn, the object type information from vision sensors guides the wireless vibrometry system to select the most appropriate signal processing pipeline. The object tracking capability in computer vision also helps wireless systems efficiently detect and separate vibrations from multiple objects in real time. 

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3. Deep Learning Based RFI Detection and Mitigation for SMAP Using Convolutional Neural Networks

   Ahmed Manavi Alam*, Ali Cafer (January 2022, RFI Workshop 2022)

   Passive Remote Sensing services are indispensable in modern society because of the applications related to climate studies and earth science. Among those, NASA’s Soil Moisture Active and Passive (SMAP) mission provides an essential climate variable such as the moisture content of the soil by using microwave radiation within protected band over 1400-1427 MHz. However, because of the increasing active wireless technologies such as Internet of Things (IoT), unmanned aerial vehicles (UAV), and 5G wireless communication, the SMAP’s passive observations are expected to experience an increasing number of Radio Frequency Interference (RFI). RFI is a well-documented issue and SMAP has a ground processing unit dedicated to tackling this issue. However, advanced techniques are needed to tackle the increasing RFI problem for passive sensing systems and to jointly coexist communication and sensing systems. In this paper, we apply a deep learning approach where a novel Convolutional Neural Network (CNN) architecture for both RFI detection and mitigation is employed. SMAP Level 1A spectrogram of antenna counts and various moments data are used as the inputs to the deep learning architecture. We simulate different types of RFI sources such as pulsed, CW or wideband anthropogenic signals. We then use artificially corrupted SMAP Level 1B antenna measurements in conjunction with RFI labels to train the learning architecture. While the learned detection network classifies input spectrograms as RFI or no-RFI cases, the mitigation network reconstructs the RFI mitigated antenna temperature images. The proposed learning framework both takes advantage of the existing SMAP data and the simulated RFI scenarios. Future remote sensing systems such as radiometers will suffer an increasing RFI problem and spectrum sharing and techniques that will allow coexistance of sensing and communication systems will be utmost importance for both parties. RFI detection and mitigation will remain a prerequisite for these radiometers and the proposed deep learning approach has the potential to provide an additional perspective to existing solutions. We will present detailed analysis on the selected deep learning architecture, obtained RFI detection accuracy levels and RFI mitigation performance. 

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4. Wall Matters: Rethinking the Effect of Wall for Wireless Sensing

   https://doi.org/10.1145/3631417  

   Xie, Binbin; Cui, Minhao; Ganesan, Deepak; Xiong, Jie (December 2023, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Wireless sensing has demonstrated its potential of utilizing radio frequency (RF) signals to sense individuals and objects. Among different wireless signals, LoRa signal is particularly promising for through-wall sensing owing to its strong penetration capability. However, existing works view walls as a bad thing as they attenuate signal power and decrease the sensing coverage. In this paper, we show a counter-intuitive observation, i.e., walls can be used to increase the sensing coverage if the RF devices are placed properly with respect to walls. To fully understand the underlying principle behind this observation, we develop a through-wall sensing model to mathematically quantify the effect of walls. We further show that besides increasing the sensing coverage, we can also use the wall to help mitigate interference, which is one well-known issue in wireless sensing. We demonstrate the effect of wall through two representative applications, i.e., macro-level human walking sensing and micro-level human respiration monitoring. Comprehensive experiments show that by properly deploying the transmitter and receiver with respect to the wall, the coverage of human walking detection can be expanded by more than 160%. By leveraging the effect of wall to mitigate interference, we can sense the tiny respiration of target even in the presence of three interferers walking nearby. 

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5. WiStress: Contactless Stress Monitoring Using Wireless Signals

   https://doi.org/10.1145/3478121  

   Ha, Unsoo; Madani, Sohrab; Adib, Fadel (September 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Stress plays a critical role in our lives, impacting our productivity and our long-term physiological and psychological well-being. This has motivated the development of stress monitoring solutions to better understand stress, its impact on productivity and teamwork, and help users adapt their habits toward more sustainable stress levels. However, today's stress monitoring solutions remain obtrusive, requiring active user participation (e.g., self-reporting), interfering with people's daily activities, and often adding more burden to users looking to reduce their stress. In this paper, we introduce WiStress, the first system that can passively monitor a user's stress levels by relying on wireless signals. WiStress does not require users to actively provide input or to wear any devices on their bodies. It operates by transmitting ultra-low-power wireless signals and measuring their reflections off the user's body. WiStress introduces two key innovations. First, it presents the first machine learning network that can accurately and robustly extract heartbeat intervals (IBI's) from wireless reflections without constraints on a user's daily activities. Second, it introduces a stress classification framework that combines the extracted heartbeats with other wirelessly captured stress-related features in order to infer a subject's stress level. We built a prototype of WiStress and tested it on 22 different subjects across different environments in both stress-induced and free-living conditions. Our results demonstrate that WiStress has high accuracy (84%-95%) in inferring a person's stress level in a fully-automated way, paving the way for ubiquitous sensing systems that can monitor stress and provide feedback to improve productivity, health, and well-being. 

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