Feature and muscle selection for an effective hand motion classifier based on electromyography

An issue that arises in the hand motion classification based on the electromyography (EMG) system is the failure of choosing the right features and number of muscles. These parameters are fundamental in determining the accuracy and effectiveness of the classifier system. Therefore, the objective of this study is to develop and evaluate an effective hand motion classifier based on the EMG signal. The three-channel of EMG was collected by placing three pairs of electrodes on the surface of the skin. Six statistic features (mean, variance, standard deviation, kurtosis, skewness, and entropy) were selected to extract the EMG signal using a window length of 100 samples. A muscle and features selection is applied to the classifier machine (linear discriminant analysis (LDA), support vector machine (SVM) and K nearest neighborhood (KNN)) to retrieve the most useful feature and muscle. In this study, we found that there was no significant difference in accuracy among a number of muscles (p-value>0.05). LDA and SVM showed the best accuracy and no significant difference in accuracy between both were found. This study concluded that EMG signal from a single muscle can classify the hand motion (hand close, open, wrist flexion, and extension) effectively.

Design of Force Meter for Traction Unit

Force gauge is an instrument used to measure the force on the traction unit. The purpose of this study is made a force gauge meter which used during traction operation. The main component of this force gauge meter is Atmega 328, HX711 module and Loadcell type S sensor.  The mikrokontroller Atmega 328 is the main board. Loadcell type S used to detect the force of traction, and the module HX711 is used to amplify the output of the loadcell sensor. In this study, the measurements were performed in the hospital. The error  of this design is 0.01%  and 4.8% for minimum and maximum, respectively. The force gauge designed portable and comes with a battery indicator

Patient Monitor for SpO2 and Temperature Parameters

Patient monitor is an apparatus used to monitor the patient\u27s condition in real-time, hence the patient\u27s physiological conditions can be identified at that time. The purpose of this study is to design a patient monitor for SpO2 and temperature parameters based on computer with Delphi progaming. In this work, the author developed  patient monitor with two parameters (SpO2 and Temperature). The workings of this tool are very simple by installing the finger sensor on the finger and the temperature sensor in the armpit area will then be detected by the two sensors that will be displayed on the PC and LCD Characters, analog data from the ADC Atmega is received by the personal computer (PC) via Bluetooth HC -05 and values ​​per parameter are also displayed on the Character LCD. After measuring, get an error in the tool, the biggest SpO2 error of this tool is 1.02% and get the smallest error of 0.8%. And for the biggest error of Temperature of 1.02% and the smallest error of 0.8%

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

Pulse Oximeter Design for SpO2 and BPM Recording on External Memory to Support the Covid-19 Diagnosis

COVID-19 (coronavirus disease) is an acute respiratory illness induced by exposure to coronavirus 2 in 2019 (SARS-CoV-2). WHO confirms that there were 1.8 million registered deaths in 2020 and that there were 3.5 million recorded deaths in 2021. People who are infected with SARS-CoV-2 without symptoms should have a pulse oximeter. Early detection of low oxygen levels in the blood can lead to fewer complications. Continuously decreasing oxygen saturation, if not controlled, will cause hypoxia (an abnormal respiratory circulation system condition that causes breathlessness). In normal conditions, oxygen levels and heart rate are related. When a person has a shortage of oxygen (breathlessness), their heart rate increases to supply the oxygen. Regulating heart rate can aid in the prevention of disorders such as arrhythmia, coronary heart disease, and hypertension. A pulse oximeter is used to measure the oxygen saturation in the blood and the patient's heart rate (BPM) with non-invasive methods. Conventional pulse oximeters do not support users by not having features such as medical records, which are required for further examination by a doctor. The purpose of this research is to make a pulse oximeter with external storage capability. The difference in wavelength between the red and infrared LED lights that will be captured by the photodiode is measured. SpO2 and HR values will be generated as a result of comparative measurements. Using a MAX30102 sensor to detect SpO2 and heart rate, and an Arduino Mega256 to process data for display on the TFT Nextion with Memory Card storage. By comparing the module to a conventional pulse oximeter, data was collected 10 times for each respondent. The maximum SpO2 error value is 0.43%, whereas the BPM parameter has the largest error value of 2.02% and the smallest error value of 0.01% based on the data collected. A significant error value is caused by finger movement. The module is usable, based on the results, because the maximum error tolerance for a pulse oximeter is 1% SpO2 and 5% BPM, according to the 2001 Ministry of Health Ministry's Guidelines for Testing and Calibrating Medical Devices

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

Temperature Distribution Monitoring on Blood Bank Chamber Using Android Application on Mobile Phone

Blood cold chain is a mandatory requirement of blood donation procedures to protect blood from bacterial contamination and to extend the shelf life of blood. Blood bank as a storage medium for blood bags requires a temperature between 2℃-6℃ on average. However, in general, blood banks only have 1 cold temperature distribution point, which is feared that the spread of cold temperatures in the compartment will be different at each point. For this reason, the researcher intended to design a blood bank temperature distribution monitoring device consisting of 7 measurement points. In this case, temperature sensor readings at each point are displayed wirelessly to smartphone devices using the Blynk platform and are also on a 3.5-inch TFT screen. The measurement data were then stored on the SD card memory so that the level of temperature fluctuations in the blood bank compartment can be analyzed during use. The module was also equipped with an alarm warning on the module and the Blynk application if the temperature is out of the normal temperature range (2℃-6℃). Before being used for measuring temperature distribution, the device made was compared with the standard Fluke DPM4 tester, in which the highest error obtained was 2.08% at T1, 1.58% at T2, -2.73% at T3, 1,61% at T4, -1.07% at T5, -0.06% at T6, and -2.32% at T7. After being compared with standard equipment, the device was used to measure the temperature spread in the Kirsch brand blood bank and the average temperature obtained was 3.56℃ at T1, 3.58℃ at T2, 3.73℃ at T3, 3.57℃ at T4, 3.67℃ at T5, 3.63℃ at T6, and 3.72℃ at T7. Based on the analysis results, the blood bank monitoring device can be used to measure the temperature spread in the blood bank compartment at 7 measurement points. Furthermore, temperature readings can be monitored wirelessly and remotely. It is hoped that this research can further help laboratory personnel at the Blood Transfusion Unit to monitor and evaluate the level of temperature spread in the blood bank compartment and prevent early damage to blood components

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

Design And Development Of Spo2, Bpm, And Body Temperature For Monitoring Patient Conditions In Iot-Based Special Isolation Rooms

The use of batteries as the main power source in portable equipment systems has several drawbacks, including the percentage of battery power that must be monitored so that the system is always active. Analysis of battery power efficiency is needed to determine the resistance of portable systems. This study makes a portable system for monitoring the condition of patients with infectious diseases in a special isolation room that can measure heart rate, body temperature, and oxygen saturation. The design of this device uses a 2200mAH battery as a power source on the IC TTGO ESP32 to manage data and display measurement results, the MAX30102 sensor to measure oxygen saturation and heart rate, and the MCP9808 sensor to measure body temperature. The design of this device has been tested on respondents aged 25-40 years by placing the sensor on the fingertip then the measurement results are compared with a standard device that has been calibrated. The measurement results show that the device is feasible to use because the measurement error value is ±5%. Testing the efficiency of battery power in normal mode and save mode. In normal mode, the current used in the device is 154.9 mA, while the save mode by not activating the LCD TTGO ESP32 requires a current of 126.7 mA. The results of the analysis show that using the battery in normal mode can activate the device for up to ±14 hours and in save mode for ±17 hours. This designed method is useful for measuring power efficiency in different device modes and the user knows the battery charging time at regular intervals

Alat Ukur Berat untuk Pengujian Status Gizi Balita dengan Metode Anthropometry

Masa anak balita merupakan kelompok yang rentan mengalami kurang gizi salah satunya adalah stunting. Stunting menggambarkan status gizi kurang yang bersifat kronik pada masa pertumbuhan dan perkembangan sejak awal kehidupan. Masalah gizi terutama stunting pada balita dapat menghambat perkembangan anak. Tujuan penelitian ini adalah merancang alat ukur berat dan tinggi badan dilengkapi penilaian status gizi balita, tujuan menentukan penilaian status gizi adalah apabila terjadi penyimpangan status gizi pada balita dapat segera diberi tindakan agar kondisi balita tidak memburuk. Kontribusi penelitian ini adalah mengukur berat dan tinggi badan balita, dari data berat dan tinggi badan tersebut dapat diketahui status gizi pada balita. Agar dapat mengetahui penilaian status gizi balita, dasar utama dalam penelitian ini menggunakan metode Antropometri. Penulis ingin membuat sebuah modul yang digunakan untuk melakukan pengukuran pada balita dengan parameter tinggi badan. Dalam perancangannya, modul ini menggunakan Arduino sebagai pengontrol utama. Sensor yang digunakan adalah variabel resistor (potensiometer) yang berfungsi untuk mendeteksi tinggi badan balita lalu dikirim oleh modul bluetooth HC-05 ke PC untuk dilakukan pembacaan dan hasilnya ditampilan dalam bentuk penilaian status gizi. Berdasarkan hasil pengukuran tinggi badan balita pada modul diperoleh error maksimal sebesar 0.35 % dan rata-rata errornya sebesar 0.093%. Alat ini dapat diimplementasikan pada pemantauan pertumbuhan berat dan tinggi balita. &nbsp