Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask.

Wearable sensors and machine learning algorithms are widely used for predicting an individual's thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual's thermal sensation was significantly correlated with SKT, EBT, and associated features.

Heart rate variability and heart rate monitoring of nurses using PPG and ECG signals during working condition: A pilot study.

The use of wearable photoplethysmography (PPG) technology for estimating heart rate (HR) and HR variability (HRV) in the health care system is gaining attention in recent years. However, the performance of these devices remains questionable in their ability to collect data in real working conditions for long-term monitoring. The present study aimed to examine the data collected from nurses during working hours by PPG and electrocardiography (ECG) devices. Twenty-two nurses underwent a 60-minute work protocol during the normal working conditions while wearing a PPG device and an ECG device. HR, low-frequency component (LF) and high-frequency component (HF), LF/HF ratio, and percent LF distribution in total spectral power, and steps were examined. Pearson's correlation analysis and Bland-Altman method was performed to examine the relationships between the two devices based on HR and HRV indices. The results found strong positive correlations between HR estimates of both devices, and moderate correlations between LF/HF ratio and percent LF indices estimates, respectively. Moreover, the Bland-Altman analysis showed a small mean bias in general between the captured data of both devices. This pilot study suggested that the PPG device appears to demonstrate good overall reliability in measuring HR, LF/HF ratio, and percent LF. A further large-scale study is required to investigate the feasibility and practicality for HR and HRV analysis in nurses during real working conditions using PPG devices.

Remote Monitoring of Critically-Ill Post-Surgical Patients: Lessons from a Biosensor Implementation Trial.

Biosensors represent one of the numerous promising technologies envisioned to extend healthcare delivery. In perioperative care, the healthcare delivery system can use biosensors to remotely supervise patients who would otherwise be admitted to a hospital. This novel technology has gained a foothold in healthcare with significant acceleration due to the COVID-19 pandemic. However, few studies have attempted to narrate, or systematically analyze, the process of their implementation. We performed an observational study of biosensor implementation. The data accuracy provided by the commercially available biosensors was compared to those offered by standard clinical monitoring on patients admitted to the intensive care unit/perioperative unit. Surveys were also conducted to examine the acceptance of technology by patients and medical staff. We demonstrated a significant difference in vital signs between sensors and standard monitoring which was very dependent on the measured variables. Sensors seemed to integrate into the workflow relatively quickly, with almost no reported problems. The acceptance of the biosensors was high by patients and slightly less by nurses directly involved in the patients' care. The staff forecast a broad implementation of biosensors in approximately three to five years, yet are eager to learn more about them. Reliability considerations proved particularly troublesome in our implementation trial. Careful evaluation of sensor readiness is most likely necessary prior to system-wide implementation by each hospital to assess for data accuracy and acceptance by the staff.

Recent advances in nanomaterials based biosensors for point of care (PoC) diagnosis of Covid-19 - A minireview.

Early diagnosis and ultrahigh sample throughput screening are the need of the hour to control the geological spread of the COVID-19 pandemic. Traditional laboratory tests such as enzyme-linked immunosorbent assay (ELISA), reverse transcription polymerase chain reaction (RT-PCR) and computed tomography are implemented for the detection of COVID-19. However, they are limited by the laborious sample collection and processing procedures, longer wait time for test results and skilled technicians to operate sophisticated facilities. In this context, the point of care (PoC) diagnostic platform has proven to be the prospective approach in addressing the abovementioned challenges. This review emphasizes the mechanism of viral infection spread detailing the host-virus interaction, pathophysiology, and the recent advances in the development of affordable PoC diagnostic platforms for rapid and accurate diagnosis of COVID-19. First, the well-established optical and electrochemical biosensors are discussed. Subsequently, the recent advances in the development of PoC biosensors, including lateral flow immunoassays and other emerging techniques, are highlighted. Finally, a focus on integrating nanotechnology with wearables and smartphones to develop smart nanobiosensors is outlined, which could promote COVID-19 diagnosis accessible to both individuals and the mass population at patient care.

Wearable electrochemical biosensors in North America.

Tremendous research and commercialization efforts around the world are focused on developing novel wearable electrochemical biosensors that can noninvasively and continuously screen for biochemical markers in body fluids for the prognosis, diagnosis and management of diseases, as well as the monitoring of fitness. Researchers in North America are leading the development of innovative wearable platforms that can comfortably comply to the human body and efficiently sample fluids such as sweat, interstitial fluids, tear and saliva for the electrochemical detection of biomarkers through various sensing approaches such as potentiometric ion selective electrodes and amperometric enzymatic sensors. We start this review with a historical timeline overviewing the major milestones in the development of wearable electrochemical sensors by North American institutions. We then describe how such research efforts have led to pioneering developments and are driving the advancement and commercialization of wearable electrochemical sensors: from minimally invasive continuous glucose monitors for chronic disease management to non-invasive sweat electrolyte sensors for dehydration monitoring in fitness applications. While many countries across the globe have contributed significantly to this rapidly emerging field, their contributions are beyond the scope of this review. Furthermore, we share our perspective on the promising future of wearable electrochemical sensors in applications spanning from remote and personalized healthcare to wellness.