Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand

EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition

Implementation of Gyro Accelerometer Sensor for Measuring Respiration Rate Based on Inhale and Exhale Using Kalman Filter

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions including heart, lung, emotional stress, the influence of body temperature and activity fatigue. The respiratory rate in humans is measured by counting the number of breaths for one minute by monitoring and counting the number of times the chest rises and falls during the inhale and exhale process. Various methods for measuring respiratory rate that are commonly used including pneumograph, impedance and capnography are applied in patient monitoring. This study aims to examine and analyze the application of the kalman filter on the output of the gyro accelerometer sensor to increase the results of the detection of respiratory rates using the gyro accelerometer sensor. This study test was carried out using a patient simulator in Surabaya Ministry of Health Polytechnic nursing laboratory. This simulator patient can simulate respiration with a mechanical work system up and down the chest and abdomen, uses an Arduino Nano microcontroller to filter the output of the gyro accelerometer sensor and the results will be compared before and after the filter. The independent variable in this study is the respiration value, while the dependent variable is the sensor output before being filtered. In the relaxed condition of the respondent The most effective use of the kalman filter is found in the parameters R = 10, Q = 0.1 because in the use of these parameters, the value after being filtered has a value that tends to be stable. The highest error value in the application of the gyro accelerometer sensor occurs at sensor position 1 with R = 1 Q = 10 value of 2,6%. This study shows the effect of differences in respiration values before and after using a kalman filter. This study has limited differences in values that are far between the pre filter and after being filtered in several data collections

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well

GAS CHROMATOGRAPHY - MASS SPECTROMETRY (GC-MS) IN ORGANIC GEOCHEMICAL INVESTIGATION OF CRUDE OILS FROM KIKINDA AND VELEBIT FIELDS IN SERBIA

In this work two crude oil samples were investigated to present the difference between biodegraded (Velebit) and non-degraded (Kikinda) oil. Two samples are from the Velebit oil–gas field and the Kikinda oil-gas field. These are two of the largest oil and gas deposits in the Serbian part of the Pannonian Basin. In the experimental part of this work, two samples of crude oil were separated by column chromatography. Saturated hydrocarbons were analyzed by gas chromatography-mass spectrometry instruments. Based on the abundance and distribution of biomarkers, it could be conclude that the distribution is typical of oil in both samples, with difference in the distribution of n-alkane. GC-MS chromatogram of n-alkanes and isoprenoids of saturated fraction isolated from Velebit crude oil show the distribution typical of oils altered by biodegradation

A rare presentation of acute flaccid myelitis in covid-19 patient: a case report

SARS-CoV-2 virus enters human cells via ACE-2 receptors and causes multiple organs dysfunction. These ACE-2 receptors are in cells surface of human lung, liver, heart, kidney and blood vessels. The expression of ACE2 receptors in cortical neurons, glial cells and spinal cord cells create nervous system susceptible to SARS-CoV-2 attack and may be a source of different neurological deficits including myelitis in COVID-19 patients

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Suspension System Control Process for Buses with In-Wheel Motors

In the last few years, there has been considerable growth in in-wheel electric motors manufacturing and the number of electric buses operating around the world. As a result of this clear increase, competition between electric bus manufacturers is increasing to reach the best satisfaction and comfort for passengers. This paper aims to deliver significantly better results due to suspension system. This paper also aims to evaluate an active bus suspension system with an in-wheel electric motor and to show the effect of this motor on the performance of the bus’s suspension system. In this work, a quarter bus suspension system is simulated and modelled using Matlab software, in addition to using one of the well-known control technologies, which is the linear quadratic regulator (LQR). The results showed that the weight of the electric motors in the bus’s wheels had a slightly negative effect on passenger comfort, as the reason for this effect is because the electric motor increased the mass of the wheel

Hepatitis C: Knowledge and attitude of graduating dentist from Faculty of Dentistry, Sebha, Libya

Objectives: The present study was undertaken to assess the knowledge and attitude of the graduating dentist with respect to various aspect of hepatitis C. Materials and Methods: A cross-sectional survey using a self-administered, structured, and pilot tested closed-ended 26-item questionnaire was completed by 99 dental students from Faculty of Dentistry, Sebha (Libya). Descriptive analysis was carried out, and Chi-square test was used for intergroup comparison. Results: Overall 54.5% students reported that their knowledge with respect to hepatitis C virus (HCV) infection was inadequate. 45.5% participants felt that books were the most common source for acquiring HCV information. Only, 44.4% participants were willing to treat high-risk HCV, patients. 70.7% interns feel that the dentists should not have the right to reject treating an HCV patient. 83% of the participants said that a dentist can contract hepatitis C from their patients if they do not use proper barrier techniques intergroup comparison showed statistically significant difference with issues related to contracting HCV from patient, HCV vaccine, treating patient in normal setting, perceptions toward HCV patients, and attitudes toward providing treatment. Conclusion: The study revealed that the knowledge of HCV among the dental students was not satisfactory, and their attitude toward HCV patients was discriminatory

Power management and sizing optimization for hybrid grid-dependent system considering photovoltaic wind battery electric vehicle

Energy Management Strategy (EMS) as a control strategy for microgrid (MG) systems is a complex task to operate the integrated power systems and utilize consumer-based. Alternative energy sources such as solar and wind can be used to generate energy that can be used to power electrical appliances when combined with energy storage units. Additionally, with other sources to complement the drawbacks of each source. But the common drawback of the aforementioned sources are naturally unpredictable and climatology changes dependent. The main aim of this study is to minimize the cost and losses of the system, contrary, maximizing the renewability. This study considers a Tripoli-Libya as a case study located in the north of Libya and coordinated between32.88o N latitude and 13.19o E longitude. The system utilizes night-Time for exchanging the power between the Electric Vehicle (EV) and the utility grid to form Vehicle-To-Grid (V2G) technology. The result of the study shows that the Cost of Energy (COE), Renewable Energy Fraction (REF), and Deficiency Power Supply Probability (DPSP), are an objective of the study along with the analysis of collected data based on the Grasshopper Optimization Algorithm (GOA) using Matlab. The acquired result for the sizing system configuration has been validated with nature-inspired metaheuristic algorithms