TechNews digests: Jan - Nov 2008

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

An inclusive survey of contactless wireless sensing: a technology used for remotely monitoring vital signs has the potential to combating COVID-19

With the Coronavirus pandemic showing no signs of abating, companies and governments around the world are spending millions of dollars to develop contactless sensor technologies that minimize the need for physical interactions between the patient and healthcare providers. As a result, healthcare research studies are rapidly progressing towards discovering innovative contactless technologies, especially for infants and elderly people who are suffering from chronic diseases that require continuous, real-time control, and monitoring. The fusion between sensing technology and wireless communication has emerged as a strong research candidate choice because wearing sensor devices is not desirable by patients as they cause anxiety and discomfort. Furthermore, physical contact exacerbates the spread of contagious diseases which may lead to catastrophic consequences. For this reason, research has gone towards sensor-less or contactless technology, through sending wireless signals, then analyzing and processing the reflected signals using special techniques such as frequency modulated continuous wave (FMCW) or channel state information (CSI). Therefore, it becomes easy to monitor and measure the subject’s vital signs remotely without physical contact or asking them to wear sensor devices. In this paper, we overview and explore state-of-the-art research in the field of contactless sensor technology in medicine, where we explain, summarize, and classify a plethora of contactless sensor technologies and techniques with the highest impact on contactless healthcare. Moreover, we overview the enabling hardware technologies as well as discuss the main challenges faced by these systems.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E39

Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users

Contactless WiFi Sensing and Monitoring for Future Healthcare:Emerging Trends, Challenges and Opportunities

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

Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19

The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence

MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation

Having been studied for more than a decade, Wi-Fi human sensing still faces a major challenge in the presence of multiple persons, simply because the limited bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically separate multiple subjects. Existing solutions mostly avoid this challenge by switching to radars with GHz bandwidth, at the cost of cumbersome deployments. Therefore, could Wi-Fi human sensing handle multiple subjects remains an open question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing system with physical separability. The principle behind MUSE-Fi is that, given a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field channel variation caused by the subject significantly overwhelms variations caused by other distant subjects. Consequently, focusing on the channel state information (CSI) carried by the traffic in and out of this device naturally allows for physically separating multiple subjects. Based on this principle, we propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI, and iii) downlink beamforming feedback, where we specifically tackle signal recovery from sparse (per-user) traffic under realistic multi-user communication scenarios. Our extensive evaluations clearly demonstrate that MUSE-Fi is able to successfully handle multi-person sensing with respect to three typical applications: respiration monitoring, gesture detection, and activity recognition.Comment: 15 pages. Accepted by ACM MobiCom 202

HeadScan: A Wearable System for Radio-Based Sensing of Head and Mouth-Related Activities

The popularity of wearables continues to rise. However, possible applications, and even their raw functionality are constrained by the types of sensors that are currently available. Accelerometers and gyroscopes struggle to capture complex user activities. Microphones and image sensors are more powerful but capture privacy sensitive information. Physiological sensors are obtrusive to users as they often require skin contact and must be placed at certain body positions to function. In contrast, radio-based sensing uses wireless radio signals to capture movements of different parts of the body, and therefore provides a contactless and privacy-preserving approach to detect and monitor human activities. In this paper, we contribute to the search for new sensing modalities for the next generation of wearable devices by exploring the feasibility of mobile radiobased human activity recognition. We believe radio-based sensing has the potential to fundamentally transform wearables as we currently know them. As the first step to achieve our vision, we have designed and developed HeadScan, a first-of-its-kind wearable for radio-based sensing of a number of human activities that involve head and mouth movements. HeadScan only requires a pair of small antennas placed on the shoulder and collar and one wearable unit worn on the arm or the belt of the user. Head- Scan uses the fine-grained CSI measurements extracted from radio signals and incorporates a novel signal processing pipeline that converts the raw CSI measurements into the targeted human activities. To examine the feasibility and performance of HeadScan, we have collected approximate 50.5 hours data from seven users. Our wide-ranging experiments include comparisons to a conventional skin-contact audio-based sensing approach to tracking the same set of head and mouth-related activities. Our experimental results highlight the enormous potential of our radio-based mobile sensing approach and provide guidance to future explorations

Unmanned Drug Delivery Vehicle for COVID-19 Wards in Hospitals

The prime reason for proposing the work is designing and developing a low-cost guided wireless Unmanned Ground Vehicle (UGV) for use in hospitals for assistance in contactless drug delivery in COVID-19 wards. The Robot is designed as per the requirements and technical specifications required for the healthcare facility. After a detailed survey and tests of various mechanisms for steering and structure of UGV, the best mechanism preferred for steering articulated and for body structure is hexagonal as this approach provides decent performance and stability required to achieve the objective. The UGV has multiple sensors onboard, such as a Camera, GPS module, Hydrogen, and Carbon Gas sensor, Raindrop sensor, and an ultrasonic range finder on UGV for the end-user to understand the circumferential environment and status of UGV. The data and control options are displayed on any phone or computer present in the Wi-Fi zones only if the user login is validated. ESP-32 microcontroller is the prime component utilized to establish reliable wireless communication between the user and UGV.These days, the demand for robot vehicles in hospitals has increased rapidly due to pandemic outbreaks as using this makes a contactless delivery of the medicinal drug. These systems are designed specifically to assist humans in the current situation where life can be at risk for healthcare facilities. In addition, the robot vehicle is suitable for many other applications like supervision, sanitization, carrying medicines and medical equipment for delivery, delivery of food and used dishes, laundry, garbage, laboratory samples, and additional supply

Software-defined radio based contactless localization for diverse human activity recognition

This paper presents a study on contactless localization for activity recognition based on radio-frequency sensing. The focus of this study is on the quantitative analysis of the collected data, which is in the form of channel state information (CSI). The proposed method utilizes a software-defined radio (SDR) system in combination with an ensemble learning technique to localize and identify daily living activities such as leaning, sitting, standing and walking. Specifically, SDR device, Universal Software Radio Peripheral (USRP) models X300/X310 are utilized to collect data on the aforementioned activities. The data is collected from an empty space and a participant performing daily living activities in different territories. The acquired data is labelled based on the region, zone and performed activity. The CSI data is subsequently preprocessed and fed into an ensemble-based machine learning algorithm for classification. Furthermore, the stability analysis of the proposed method is performed to evaluate its reliability and robustness. The performance of the algorithm is evaluated using various metrics, including a confusion matrix, accuracy, cross-validation score and training time. The results demonstrate that the proposed ensemble-based approach achieves a high accuracy of up to 90% in activity recognition, highlighting the effectiveness of the proposed method in contactless localization for activity recognition