Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Khan, Muhammad Bilal; Rehman, Mubashir; Mustafa, Ali; Shah, Raza Ali; Yang, Xiaodong

doi:10.3390/electronics10131558

Cited by 13 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Customers are divided into groups by their ages, according to the bank's classi cation system. When hotspots and headlines are used together, the bank may strengthen its brand recognition across all age groups while also increasing consumers' viscosity and con dence in corporate entities [27,28].…”

Section: Ai Algorithm To Promote Intelligent Marketingmentioning

confidence: 99%

Artificial Intelligence Enterprise Management Using Deep Learning

Liu

Han

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In this paper, we explore the application status of deep learning (DL) in enterprise management, with China Merchants Bank as an example, and the role of DL in bank enterprise management. We analysed the application status of AI in marketing, risk control, investment, and other fields of CMB and identified five types of problems encountered in the current practical application of AI. We proposed five countermeasures: strengthening the AI organisation system’s construction, enhancing the financial data guarantee mechanism, concentrating on customer-oriented, tightly managing the danger of AI technology, and building a full AI talent system. Recent data are used to assess the impact of DL in marketing, risk management, and investment consulting. According to the data, by the end of 2019, the number of clients of CMB’s two APP platforms had reached 114 million and 91.2643 million, respectively. In 2019, CMB’s personal savings balance climbed by roughly 53% compared to 2016, and its personal loan amount increased by approximately 61%. These findings indicate that the use of AI improves consumer happiness and trust in businesses.

show abstract

Section: Ai Algorithm To Promote Intelligent Marketingmentioning

confidence: 99%

Artificial Intelligence Enterprise Management Using Deep Learning

Liu

Han

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In [ 42 ], a contactless breathing pattern detection system is developed using universal software radio peripheral (USRP). This platform utilized CSI to record the tiny movements generated due to breathing activity and detected three breathing patterns, while in [ 43 ], SDR-based system is developed, and system design is validated by first analyzing the CFR for various simulated channels. Finally, several breathing patterns are successfully classified using ML algorithms.…”

Section: Related Workmentioning

confidence: 99%

Enhancing System Performance through Objective Feature Scoring of Multiple Persons’ Breathing Using Non-Contact RF Approach

Rehman

Shah

Ali

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

show abstract

“…The findings of these sensors were published to the Internet using the IoT based on the ThingSpeak application. Khan et al [35] provided a solution to remotely monitoring vital signs such as breathing related to COVID-19 patients. The authors used software-defined radio (SDR) technology and artificial intelligence.…”

Section: Related Workmentioning

confidence: 99%

Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia

et al. 2021

View full text Add to dashboard Cite

The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.

show abstract

Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Cited by 13 publications

References 41 publications

Artificial Intelligence Enterprise Management Using Deep Learning

Artificial Intelligence Enterprise Management Using Deep Learning

Enhancing System Performance through Objective Feature Scoring of Multiple Persons’ Breathing Using Non-Contact RF Approach

Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia

Contact Info

Product

Resources

About