Monitoring of atopic dermatitis using leaky coaxial cable

In our daily life, inadvertent scratching may increase the severity of skin diseases (such as atopic dermatitis, etc.). However, people rarely pay attention to this matter, so the known measurement behavior of the movement is also very little. Nevertheless, the behavior and frequency of scratching represent the degree of itching, and the analysis of scratching frequency is helpful to the doctor's clinical dosage. In this paper, a novel system is proposed to monitor the scratching motion of a sleeping human body at night. The core device of the system are just a Leaky coaxial cable (LCX) and a router. Commonly, LCX is used in the blind field or semi blind field in wireless communication. The new idea is that the leaky cable is placed on the bed, then the state information of physical layer of wireless communication channels is acquired to identify the scratching motion and other small body movements in the human sleep process. The results show that it can be used to detect the movement and its duration. Channel state information (CSI) packet is collected by card installed in the computer based on the 802.11n protocol. The characterization of the scratch motion in the collected channel state information is unique, so it can be distinguished from the wireless channel amplitude variation trend

COVID-19 and Alzheimer’s disease: Impact of lockdown and other restrictive measures during the COVID-19 pandemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection initially results in respiratory distress symptoms but can also lead to central nervous system (CNS) and neurological manifestations, significantly impacting coronavirus disease 2019 (COVID-19) patients with neurodegenerative diseases. Additionally, strict lockdown measures introduced to curtail the spread of COVID-19 have raised concerns over the wellbeing of patients with dementia and/or Alzheimer’s disease. The aim of this review was to discuss the overlapping molecular pathologies and the potential bidirectional relationship between COVID-19 and Alzheimer’s dementia, as well as the impact of lockdown/restriction measures on the neuropsychiatric symptoms (NPS) of patients with Alzheimer’s dementia. Furthermore, we aimed to assess the impact of lockdown measures on the NPS of caregivers, exploring its potential effects on the quality and extent of care they provide to dementia patients.We utilized the PubMed and Google Scholar databases to search for articles on COVID-19, dementia, Alzheimer’s disease, lockdown, and caregivers. Our review highlights that patients with Alzheimer’s disease face an increased risk of COVID-19 infection and complications. Additionally, these patients are likely to experience greater cognitive decline. It appears that these issues are primarily caused by the SARS-CoV-2 infection and appear to be further exacerbated by restrictive/lockdown measures. Moreover, lockdown measures introduced during the pandemic have negatively impacted both the NPSs of caregivers and their perception of the wellbeing of their Alzheimer’s patients. Thus, additional safeguard measures, along with pharmacological and non-pharmacological approaches, are needed to protect the wellbeing of dementia patients and their caregivers in light of this and possible future pandemics

Detection and diagnosis of paralysis agitans

Humans’ daily behavior can reflect the main physiological characteristics of neurological diseases. Human gait is a complex behavior produced by the coordination of multiple physiological systems such as the nervous system and the muscular system. It can reflect the physiological state of human health, and its abnormality is an important basis for diagnosing some nervous system diseases. However, many early gait anomalies have not been effectively discovered because of medical costs and people's living customs. This paper proposes an effective, economical, and accurate non-contact cognitive diagnosis system to help early detection and diagnosis of paralysis agitans under daily life conditions. The proposed system extract data from wireless state information obtained from antenna-based data gathering module. Further, we implement data processing and gait classification systems to detect abnormal gait based on the acquired wireless data. In the experiment, the proposed system can detect the state of human gait and carries high classification accuracy up to 96.7 %. The experimental results demonstrate that the proposed technique is feasible and cost-effective for healthcare applications

Posture-specific breathing detection

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity

Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment

This research presents the usage of modern 5G C-Band sensing for health care monitoring. The focus of this research is to monitor the respiratory symptoms for COPD （Chronic Obstructive Pulmonary Disease）. The C-Band sensing is used to detect the respiratory conditions, including normal, abnormal breathing and coughing of a COPD patient by utilizing the simple wireless devices, including a desktop system, network interface card, and the specified tool for the extraction of wireless channel information with Omni directional antenna operating at 4.8 GHz frequency. The 5G sensing technique enhances the sensing performance for the health care sector by monitoring the amplitude information for different respiratory activities of a patient using the above-mentioned devices. This method examines the rhythmic breathing patterns obtained from C-Band sensing and digital respiratory sensor and compared the result

A nationwide virtual research education program for medical students in Pakistan: Methodological framework, feasibility testing, and outcomes

Introduction: Equipping young medical trainees with fundamental research skills can be a promising strategy to address the need for professionals who can understand and responsibly communicate evolving scientific evidence during a pandemic. Despite an ardent interest to partake in research, most educational institutions in Pakistan and other low-middle income countries have not yet adopted a comprehensive strategy for research skills education. The authors aimed to design and assess the feasibility of implementing the first nation-wide virtual research workshop for medical students in Pakistan. Methods: The course Beginners Guide to Research, designed as a nation-wide virtual research workshop series, was conducted for medical students across Pakistan in June 2020. Four interactive live workshops took place online on alternate days from June 22nd, 2020, to June 27th, 2020, each lasting 1-2 h. Outcomes included: (i) reach, (ii) efficacy as indexed by pre-post change in score pertaining to knowledge and application of research and (iii) self-rated perceptions about understanding of research on a Likert scale. Results: 3,862 participants enrolled from 41 cities and 123 institutions. Enrolled participants belonged to the following provinces: Sindh (n = 1,852, 48.0%), Punjab (n = 1,767, 45.8%), Khyber Pakhtunkhwa (n = 109, 2.8%), Azad Jammu and Kashmir (n = 84, 2.2%) Balochistan (n = 42, 1.1%). We also saw a few registrations from international students (n = 8, 0.2%). Mean (SD) age of enrolled medical students was 21.1 (2.1) years, 2,453 (63.5%) participants were female and 2,394 (62.0%) were from private-sector medical colleges. Two thousand ninety-three participants participants filled out all four pre-test and post-test forms. The total median knowledge score improved from 39.7 to 60.3% with the highest improvements in concepts of research bioethics and literature search (p \u3c 0.001) with greater change for females compared to males (+20.6 vs. +16.2%, p \u3c 0.001) and private institutions compared to public ones (+16.2 vs. +22.1%, p \u3c 0.001). Conclusion: The overwhelming enrollment and significant improvement in learning outcomes (\u3e50% of baseline) indicate feasibility of a medical student-led research course during a pandemic, highlighting its role in catering to the research needs in the LMICs

Utilizing a 5G spectrum for health care to detect the tremors and breathing activity for multiple sclerosis

Utilizing fifth‐generation (5G) sensing in the health care sector with increased capacity and massive spectrum range increases the quality of health care monitoring systems. In this paper, 5G C‐band sensing operating at 4.8 GHz is used to monitor a particular body motion of multiple sclerosis patients, especially the tremors and breathing patterns. The breathing pattern obtained using 5G C‐band technology is compared with the invasive breathing sensor to monitor the subtle chest movements caused due to respiration. The 5G C‐band has a huge spectrum from 1 to 100 GHz, which enhances the capacity and performance of wireless communication by increasing the data rate from 20 Gb/s to 1 Tb/s. The system captures and monitors the wireless channel information of different body motions and efficiently identifies the tremors experienced since each body motion induces a unique imprint that is used for a particular purpose. Different machine learning algorithms such as support vector machine, k‐nearest neighbors, and random forest are used to classify the wireless channel information data obtained for various human activities. The values obtained using different machine learning algorithms for various performance metrics such as accuracy, precision, recall, specificity, Kappa, and F‐measure indicate that the proposed method can efficiently identify the particular conditions experienced by multiple sclerosis patients

On-Chip Phase Measurement Design Study in 65nm CMOS Technology

Jitter is generally defined as a time deviation of the clock waveform from its desired position. The deviation which occurs can be on the leading or lagging side and it can be bounded (deterministic) or unbounded (random). Jitter is a critical specification in the digital system design. There are various techniques to measure the jitter. The straightforward approach is based on spectrum analyzer or oscilloscope measurements. In this thesis an on-chip jitter measurement technique is investigated and the respective circuit is designed using 65 nm CMOS technology. The work presents the high level model and transistor level model, both implemented using Cadence software. Based on the Vernier concept the circuit is composed of an edge detector, two oscillators, and a phase detector followed by a binary counter, which provides the measurement result. The designed circuit attains resolution of 10ps and can operate in the range of 100 - 500 MHz Compared to other measurement techniques this design features low power consumption and low chip area overhead that is essential for built-in self-test (BIST) applications

On-Chip Phase Measurement Design Study in 65nm CMOS Technology

Jitter is generally defined as a time deviation of the clock waveform from its desired position. The deviation which occurs can be on the leading or lagging side and it can be bounded (deterministic) or unbounded (random). Jitter is a critical specification in the digital system design. There are various techniques to measure the jitter. The straightforward approach is based on spectrum analyzer or oscilloscope measurements. In this thesis an on-chip jitter measurement technique is investigated and the respective circuit is designed using 65 nm CMOS technology. The work presents the high level model and transistor level model, both implemented using Cadence software. Based on the Vernier concept the circuit is composed of an edge detector, two oscillators, and a phase detector followed by a binary counter, which provides the measurement result. The designed circuit attains resolution of 10ps and can operate in the range of 100 - 500 MHz Compared to other measurement techniques this design features low power consumption and low chip area overhead that is essential for built-in self-test (BIST) applications