ABSTRACT
The countermeasure for preventing COVID-19 should be further studied in order to make sure countries are prepared for the endemic phase. The biggest challenge of COVID-19 is its high infection rate and infection mortality rate. Robots offer a very good solution to this, hence, we developed a robot that can autonomously navigate a closed indoor room, sanitize it, and monitor social proximity practices. The quality of the hardware design, electronic system and software developments are conducted and experimental works to test the performance of the robot are performed. © 2022 IEEE.
ABSTRACT
COVID-19 disease spreads rapidly from person to person through airborne transmission. The preventive measures are very important to prevent and control the rapid spread of COVID-19. The effective protection technique is by wearing a face mask in public space. Therefore, this study proposed a face mask detection through Haar Cascade approaches. Besides, body thermal screening features has also been considered in this study to determine whether an individual is healthy or not by using thermal sensor. The pilot study was run for a week in other to test the capabilities and the durability of the program to run in the community. The results from the pilot study showed that the thermal scanner is able to run standalone in the public for a week. In overall, the thermal Scanner device was successfully developed with a combinations of deep learning technique to detect a person with facemask and able to screen a body temperature just by using low-cost components.
ABSTRACT
Objective: To demonstrate the effectiveness and usability of a novel tele-neurology service in Nairobi. Background: There is severe shortage of neurology healthcare workers in low-/lower-middle income countries (LLMICs), especially in Africa. Tele-neurology consultations (TNC), necessitated widely due to the COVID-19 pandemic, have been demonstrated to be effective in bridging neurology service gaps, but there is little evidence of TNC effectiveness in LLMICs. Design/Methods: We conducted a prospective cross-sectional study, enrolling neurology patients referred to our tertiary referral neurology outpatients center over 12 months from October 2020. We measured satisfaction and acceptability using Likert scales, and compared TNC to face-to-face (F2F) consultations. TNC were delivered as per 2020 British and American guidelines. Descriptive data are presented as median (inter-quartile range) and statistical comparisons made using paired student t-test. Results: From 219 enrolled patients, 66.7% (146/219) responded [74% (108/146) had both F2F and TNC]: age 40.9 (30.6-55.2) years;63.0% (92/146) female;2.7% (4/146) from neighboring countries;follow-up period with neurologist (DSS) 6.8 (1.5-29.8) months;and most common presentations were headache [30.8% (45/146)], seizure [26.0% (38/146)] and neurodegenerative [15.1% (22/146)] disorders. For TNC, >90%: (i) found it just as comfortable as F2F (p=0.35) and not in violation of their privacy;(ii) saved time [3.0 (2.0-4.0) hours], travel [11.0 (7.2-21.1) km] and cost [$10 (5-20)];(iii) felt satisfied with the care and that their neurological concerns were adequately addressed;and (iv) would use TNC again. Conversely, 15.1% (22/146) did not agree with TNC being as effective as F2F, including the neurologist identifying all their health problems satisfactorily (p=0.03). In total, our TNC service saved our patients $6,125, 1,143 hours, and 25,506km of travel, equating to 3.5 tons (21 trees) of carbon dioxide emissions. Conclusions: Our study demonstrates that our regionally unique TNC service is an acceptable, efficient, effective, and environmentally-friendly care delivery model in our resource-poor setting.
ABSTRACT
According to the World Health Organization, there are approximately 17.9 million people in the world who will die under the cause of Cardiovascular diseases (CVDs) in 2019. Heart and Brain are both related to Cardiovascular diseases. Even if the patients do not pass away due to the disease, the post-effect of this illness burdens the patients and their families. Also, the outbreak of COVID-19 makes the patients take a risk of undergoing rehabilitation in the hospital. Thus, a smart healthcare solution which is a Smart Healthcare Tracker through the Internet of Things is designed. The system consists of an EMG sensor, accelerometer, gyroscope, and heart rate/pulse oximeter connected to ESP 32 with an interface of NodeMCU to study the patients' health condition for arms and legs strength by sending the data to the caregivers or physicians. The project aimed to obtain a consistent and accurate reading for each of the features for arms and legs strength analysis and sleeping disturbance analysis. The BLYNK app is also applied to the project design as a platform to display the analysis result to the caregivers/physicians on the gadgets at any time and anywhere. The prototype has been constructed and the data collection is built successfully. The prototype is trusted to obtain accurate and consistent results and can provide a sustainable way for the rehabilitation to indicate the health condition and the recovery stage of the patients. © 2021 IEEE.