Preliminary Investigation on Wrist Temperature Control by Wearable Peltier Armband

Due to COVID-19 pandemic, body temperature measurement in commercial facilities is performed using a non-contact method. However, if the human body can be controlled in some way to disguise body temperature, a thermometer would have difficulty detecting an entrant with a fever. In this study, we propose a method to control body temperature measured at the wrist in order to demonstrate the vulnerability of temperature measurement at the wrist. Our device lowers body temperature by cooling the upper arm, thereby cooling blood flow and reducing the intensity of infrared radiation detected by a thermometer. The implemented device was used to cool the upper arm under three different conditions. The results showed that cooling the blood flow in the upper arm can lower the body temperature at the wrist. The cooled body temperature was difficult to maintain after the end of cooling, irrespective of the cooling intensity and cooling time. © 2022 ACM.

Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method. IEEE

Implementation of LBPH Algorithm for Covid Patient Isolation Violation Detection

LBPH (Local Binary Pattern Histogram) is a Facial recognition algorithm used to monitor a COVID infected person using a non-contact method of isolation check. The algorithm is programmed using Python software and the results are analysed using visual studio code. The program extracts feature from an input test image and compares it with the system database. The major goal would be to send the message if the person has violated the isolation norms. This algorithm captures the image of an isolated COVID patient when he/she breaks the isolation norms by opening the door and trying to escape from isolation. © 2022 IEEE.

Measurement of Blood Oxygen based on Remote-Photoplethysmography

SpO2, also known as blood oxygen saturation, is a vital physiological indicator in clinical care. Since the outbreak of COVID-19, silent hypoxia has been one of the most serious symptoms. This symptom makes the patient's SpO2 drop to an extremely low level without discomfort and causes medical care delay for many patients. Therefore, regularly checking our SpO2 has become a very important matter. Recent work has been looking for convenient and contact-free ways to measure SpO2 with cameras. However, most previous studies were not robust enough and didn't evaluate their algorithms on the data with a wide SpO2 range. In this paper, we proposed a novel non-contact method to measure SpO2 by using the weighted K-nearest neighbors (KNN) algorithm. Five features extracted from the RGB traces, POS, and CHROM signals were used in the KNN model. Two datasets using different ways to lower the SpO2 were constructed for evaluating the performance. The first one was collected through the breath-holding experiment, which induces more motion noise and confuses the actual blood oxygen features. The second dataset was collected at Song Syue Lodge, which locates at an elevation of 3150 meters and has lower oxygen concentration in the atmosphere making the SpO2 drop between the range of 80% to 90% without the need of holding breath. The proposed method outperforms the benchmark algorithms on the leave-one-subject-out and cross-dataset validation. © 2022 IEEE.