Indoor Environment and Health Protocol Monitoring and Control System Integrated into a Smart Robot to Promote Safety on University Campuses

This article discusses about the design and deployment of a smart robotic system on university campuses for monitoring the indoor environment, health protocols, and sanitation. The designed VEX autonomous robotic system performed the following tasks: (a) moving around the university classrooms and scanning the body temperature of students and staff, as well as tracking environmental parameters in classrooms;(b) executing sanitation function by disinfecting objects in classrooms;and (c) performing security function by sending an alert signal to health and safety officer if a student or staff with fever enters the classroom, or if staff or student is not wearing face mask indoors. Particle Photon microcontrollers linked to sensors and actuators were used to detect and manage indoor environmental conditions as well as track individuals' body temperatures from a distance, with the data being stored in the ThingSpeak and Particle cloud platforms and displayed on smartphone apps. Transfer learning through MIT App Inventor's Personal Image Classifier was used to detect health protocol violations with 93.33% accuracy. The maximum distance traversed by the robot prototype was 38 meters, with an average time of 220 seconds and an average speed of 0.17 meters per second. The robot had an 88.89% success rate in following the black-lined course. This intelligent robotic system can limit staff and student exposure to infectious diseases and implement "new normal"health and safety practices on campus as post-COVID-19 precautions. © 2023 IEEE.

Work productivity among healthcare workers with post COVID-19 syndrome

IntroductionOver time, the phenomenon of persistent symptoms after COVID-19 infection has evolved from non-specific symptoms to a clinical entity known as post-covid-19 syndrome, which can lead to disabilities. The aim of this study is assess the impact of post-Covid-19 syndrome on health professionals and its implications on professional activity.MethodsThis is a cross-sectional, analytical study conducted over a two-month period and including all health care staff at the Nabeul University Hospital. The evaluation of work productivity was carried out by the questionnaire‘Work Productivity and Activity Impairment Questionnaire'.ResultsOur study involved 89 health workers. The average age of the population was 41.2±10.6 years with a sex ratio of 0.63. Nurses were the most represented occupational group (59.6%). The majority of participants were infected once and in February 2021 (55.1% of cases). General signs were the most reported and were dominated by fever (51.6%). Post-covid-19 syndrome was reported in 44 patients (49.4% of cases). The main persistent symptoms were headache (61.3%), asthenia (59%), and dyspnea (31.8%).The drop in productivity and daily activities were significantly higher in patients who developed post-covid-19 syndrome;p=0,015 and p=0,002 respectively. However, there was no significant difference between the two groups in terms of absenteeism and presenteeism (p=0.42 and p=0.2 respectively).ConclusionGuidelines for the management of post-COVID syndrome based on established criteria are needed to enhance its outcome.

IoT-Based Telemedicine Health Monitoring System with a Fuzzy Inference-Based Medical Decision Support Module for Clinical Risk Evaluation

This paper discusses the design and implementation of an Internet of Things (IoT)-based telemedicine health monitoring system (THMS) with an early warning scoring (EWS) function that reads, assesses, and logs physiological parameters of a patient such as body temperature, oxygen saturation level, systemic arterial pressure, breathing patterns, pulse (heart) rate, supplemental oxygen dependency, consciousness, and pain level using Particle Photon microcontrollers interfaced with biosensors and switches. The Mandami fuzzy inference-based medical decision support system (FI-MDSS) was also developed using MATLAB to assist medical professionals in evaluating a patient's health risk and deciding on the appropriate clinical intervention. The patient's physiological measurements, EWS, and health risk category are stored on the Particle cloud and Thing Speak cloud platforms and can be accessed remotely and in real-time via the Internet. Furthermore, a RESTful application programming interface (API) was developed using GO language and PostgreSQL database to enhance data presentation and accessibility. Based on the paired samples t-tests obtained from 6 sessions with 10 trials for each vital sign per session, there were no significant differences between the clinical data obtained from the designed prototype and the commercially sold medical equipment. The mean differences between the compared samples for each physiological data were not more than 0.40, the standard deviations were less than 2.3, and the p-values were greater than 0.05. With a 96.67% accuracy, the FI-MDSS predicted health risk levels that were comparable to conventional EWS techniques such as the Modified National Early Warning Score (m-NEWS) and NEWS2, which are used in the clinical decision-making process for managing patients with COVID-19 and other infectious illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Psychosocial skills of ICU healthcare staff providing care to patients with COVID-19

Introduction: The COVID-19 crisis has imposed deep improvements in ICU responsiveness face to unprecedented and uncertain situations. In addition to strengthening logistics resources, this responsiveness required the development of psychosocial skills of healthcare providers, especially in ICU. Objective(s): To assess extrinsic factors interfering with psychosocial skills of the staff working in COVID-19 ICU and to analyze the different dimensions of these skills. Method(s): This is an observational descriptive study conducted at the COVID-19 ICU of an Academic Hospital, during a one-month period. All healthcare providers were enrolled. Data collection was based on a self-administered questionnaire including: sociodemographic factors;the general perception of work in covid-19 ICU and psychosocial skills. Six dimensions were explored separately, then by a standardized scale ranging from 0 to 100.Three levels of satisfaction were considered. Result(s): Fifty-five healthcare providers were enrolled. The average age was 32 years. The sex ratio was 0.25. Mean scales of satisfaction were 53.6 for professional status and occupational security;62.4 for working conditions and 69.8 for relational aspects. The most altered extrinsic factors were satisfaction regarding the salary and satisfaction regarding the administration policy with mean scores of 15 and 10 respectively. Satisfying psychosocial skills were creative and critical thoughts, self-awareness and empathy for others, communication and interpersonal relationships. Whereas the most impaired skills were stress management and problems solving, with mean scales of 49.6 and 68.3 respectively. Conclusion(s): Psychosocial skills were generally acceptable. However, they could be improved by specific actions targeting extrinsic factors.

The support of healthcare workers suffering from COVID 19

Introduction: The COVID-19 pandemic has focused attention on the challenges and risks faced by frontline healthcare workers (HCW). Objective(s): To describe the quality of management of HCW affected by the COVID-19. Method(s): This is a cross-sectional study enrolling all HCW of Farhat Hached Academic hospital who had been affected by COVID-19 during the period from september to December 2020. Result(s): During the study period, 267 HCW were affected with a mean age of 42.3 +/-10 years and a ratio-sex of 0.25. The most represented category was nurses (33.3%) followed by technicians (26.1%). Gynecology department had the highest number of affected HCW (14.4%).The majority of participants (97.4%) reported a medical care. Twelve HCW (4.5%) were hospitalized with an average length of hospital stay of 7.55 +/- 6.12 days. The average length of sick leave was 18.68 +/- 10.99 days. During the lockdown, 38.6% of HCW took care of their children without any external help. All of the HCW were supported by phone calls from colleagues in 88.4% of cases, the hierarchy in 67.4% of cases, occupational medicine in 60.3% of cases. Conclusion(s): The impact of COVID 19 is greater in HCW than in the general population. The affected staff should have a multidimensional management to avoid post covid sequelae in both physical and mental levels.

The support of healthcare workers suffering from COVID 19

Introduction : The COVID-19 pandemic has focused attention on the challenges and risks faced by frontline healthcare workers (HCW). This study aimed to describe the quality of management of HCW affected by the COVID-19. Methods : This is a cross-sectional study enrolling all HCW of Farhat Hached Academic hospital who had been affected by COVID-19 during the period from september to December 2020. Results : During the study period, 27 HCW were affected with a mean age of 42.3 ±10 years and a ratio-sex of 0.25. The most represented category was nurses (33.3%) followed by technicians (26.1%). Gynecology department had the highest number of affected HCW (14.4%) followed by pediatric department and administration in 7.2% and 5.7% respectively. The mean of seniority was 14.5± 11 years. The majority of participants (97.4%) reported a medical care. Twelve HCW (4.5%) were hospitalized with an average length of hospital stay of 7.55 ± 6.12 days. The average length of sick leave was 18.68 ± 10.99 days. During the lockdown, 38.6% of HCW took care of their children without any external help. All of the HCW were supported by phone calls from colleagues in 88.4% of cases, the hierarchy in 67.4% of cases, occupational medicine in 60.3% of cases. Conclusion : The impact of COVID 19 is greater in HCW than in the general population. The affected staff should have a multidimensional management to avoid post covid sequelae in both physical and mental levels.