Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms.

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level-SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton.

Motion assistance exoskeletons are designed to support the joint movement of people who perform repetitive tasks that cause damage to their health. To guarantee motion accompaniment, the integration between sensors and actuators should ensure a near-zero delay between the signal acquisition and the actuator response. This study presents the integration of a platform based on Imocap-GIS inertial sensors, with a motion assistance exoskeleton that generates joint movement by means of Maxon motors and Harmonic drive reducers, where a near zero-lag is required for the gait accompaniment to be correct. The Imocap-GIS sensors acquire positional data from the user's lower limbs and send the information through the UDP protocol to the CompactRio system, which constitutes a high-performance controller. These data are processed by the card and subsequently a control signal is sent to the motors that move the exoskeleton joints. Simulations of the proposed controller performance were conducted. The experimental results show that the motion accompaniment exhibits a delay of between 20 and 30 ms, and consequently, it may be stated that the integration between the exoskeleton and the sensors achieves a high efficiency. In this work, the integration between inertial sensors and an exoskeleton prototype has been proposed, where it is evident that the integration met the initial objective. In addition, the integration between the exoskeleton and IMOCAP is among the highest efficiency ranges of similar systems that are currently being developed, and the response lag that was obtained could be improved by means of the incorporation of complementary systems.