Development of wireless EMG control system for rehabilitation devices

Champaty, Biswajeet; Dubey, Prashant; Sahoo, Saikat; Ray, Sirsendu Sekhar; Pal, Kunal; Anis, Arfat

doi:10.1109/aicera.2014.6908260

Cited by 19 publications

(11 citation statements)

References 8 publications

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“…While the aforementioned studies dealt with EMG measurements for rehabilitation or diagnostic purposes, most projects on this topic aimed at using EMG signals for controlling prostheses, exoskeletons or even robots [ 10 , 100 , 101 , 102 , 103 , 104 ]. For this purpose, Champaty et al developed an EMG biopotential amplifier based on the AD620 instrumentation amplifier, connected to an Arduino Uno responsible for signal processing and classification [ 105 ]. The gained signals were transmitted to a wheelchair model by a Xbee transceiver.…”

Section: Emg Measurementsmentioning

confidence: 99%

Measuring Biosignals with Single Circuit Boards

Ehrmann¹,

Błachowicz

Homburg

et al. 2022

Bioengineering

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To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.

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Section: Emg Measurementsmentioning

confidence: 99%

Measuring Biosignals with Single Circuit Boards

Ehrmann¹,

Błachowicz

Homburg

et al. 2022

Bioengineering

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“…Arduino UNO is used to convert analog to digital output due to the signal produced by muscle sensor v3. Muscle sensor v3 output was interfaced with an Arduino UNO microcontroller to record the signal on a laptop operating in battery mode for signal processing and classification [29]. Data will save and upload to Matlab workspace for extraction and classification [30].…”

Section:  Electrode Padsmentioning

confidence: 99%

Classification of Electromyography Signal of Diabetes using Artificial Neural Networks

Zulkifli¹,

Nasir²

2022

IJACSA

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Diabetes is one of the most chronic diseases, with an increasing number of sufferers yearly. It can lead to several serious complications, including diabetic peripheral neuropathy (DPN). DPN must be recognized early to receive appropriate treatment and prevent disease exacerbation. However, due to the rapid development of machine learning classification, like in the health science sector, it is very easy to identify DPN in the early stages. Therefore, the aim of this study is to develop a new method for detecting neuropathy based on the myoelectric signal among diabetes patients at a low cost with utilizing one of the machine learning techniques, the artificial neural network (ANN). To that aim, muscle sensor V3 is used to record the activity of the anterior tibialis muscle. Then, the representative time domain features which is mean absolute value (MAV), root mean square (RMS), variance (VAR), and standard deviation (SD) used to evaluate fatigue. During neural network training, a different number of hidden neurons were used, and it was found that using seven hidden neurons showed a high accuracy of 98.6%. Thus, this work indicates the potential of a low-cost system for classifying healthy and diabetic individuals using an ANN algorithm.

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“…The EMG sensor to be used in the study is the AD8232 module. This sensor generates signals about muscle activity with 3-point measurement and has been used in many studies [34]- [36]. The signal is received from the EMG sensor in the range of 10-1000Hz.…”

Section: Figure 2 Emg and Imu Wrist Bandagementioning

confidence: 99%

Otonom Araçların Görsel Eğitimi için EEG, EMG ve IMU ile Etiketleme Sistemi

Seçkin

2019

Bilişim Teknolojileri Dergisi

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Autonomous vehicles are tools that make decisions and take decisions by perceiving their environment. Today, autonomous vehicles are also used in traffic in some countries. Various types of cameras, laser radars (LIDAR), sonar distance sensors, etc. are used for environmental detection in autonomous vehicles. After the environment is perceived, the collected data is taught to the vehicle with the help of machine learning methods and the vehicle reaches the target by following the traffic rules. At the point of traffic rules, the biggest task belongs to image-based systems. However, ideal traffic conditions and environmental conditions are not always provided. It is important to identify situations that may present a danger to autonomous vehicles. When the literature is examined, no visual data set or a scientific study with dangerous labeling has been found. In this study, it is aimed to design a data collection and labeling system to overcome this gap in the literature. In the system designed for the purpose, a system which automatically creates a video label from the physiological data of the driver (EEG ve EMG) and the inertia change data during human driving is designed. For this reason, firstly, the sensor signals were collected by experiments. In the time and frequency field, attributes were extracted by using the non-overlapping sliding window with 0.33 sec length. The input variables in the data set were reduced by PCA and classified by DT, RF and K-NN algorithms. According to the preliminary study findings, the K-NN method was the most successful algorithm among the algorithms tested with 0.922 accuracy.

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Development of wireless EMG control system for rehabilitation devices

Cited by 19 publications

References 8 publications

Measuring Biosignals with Single Circuit Boards

Measuring Biosignals with Single Circuit Boards

Classification of Electromyography Signal of Diabetes using Artificial Neural Networks

Otonom Araçların Görsel Eğitimi için EEG, EMG ve IMU ile Etiketleme Sistemi

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