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Journal articles on the topic 'Electromyography (EMG) sensor'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Zhao, Shumi, Jianxun Liu, Zidan Gong, et al. "Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training." Sensors 20, no. 17 (2020): 4861. http://dx.doi.org/10.3390/s20174861.

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Secondary injuries are common during upper limb rehabilitation training because of uncontrollable physical force and overexciting activities, and long-time training may cause fatigue and reduce the training effect. This study proposes a wearable monitoring device for upper limb rehabilitation by integrating electrocardiogram and electromyogram (ECG/EMG) sensors and using data acquisition boards to obtain accurate signals during robotic glove assisting training. The collected ECG/EMG signals were filtered, amplified, digitized, and then transmitted to a remote receiver (smart phone or laptop) v

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Ahmed, Mohammad, Michael Grillo, Amirtaha Taebi, Mehmet Kaya, and Peshala Thibbotuwawa Gamage. "A Comprehensive Analysis of Trapezius Muscle EMG Activity in Relation to Stress and Meditation." BioMedInformatics 4, no. 2 (2024): 1047–58. http://dx.doi.org/10.3390/biomedinformatics4020058.

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Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapeziu

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Kimoto, Akira, Yuya Oishi, and Masanao Machida. "A Wireless 2-Channel Layered EMG/NIRS Sensor System for Local Muscular Activity Evaluation." Sensors 23, no. 20 (2023): 8394. http://dx.doi.org/10.3390/s23208394.

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A wireless 2-channel layered sensor system that enables electromyography (EMG) and near-infrared spectroscopy (NIRS) measurements at two local positions was developed. The layered sensor consists of a thin silver electrode and a photosensor consisting of a photoemitting diode (LED) or photodiode (PD). The EMG and NIRS signals were simultaneously measured using a pair of electrodes and photosensors for the LED and PD, respectively. Two local muscular activities are presented in detail using layered sensors. In the experiments, EMG and NIRS signals were measured for isometric constant and ramp c

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Ahsan, Prof Dr Md Shamim, and Kazi Mahmud Hasan. "DESIGN AND DEVELOPMENT OF AN ELECTROMYOGRAPHY SENSOR ACTUATED PROSTHETIC ARM." Latin American Applied Research - An international journal 52, no. 3 (2022): 191–200. http://dx.doi.org/10.52292/j.laar.2022.817.

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Replacement of missing arms by an active prosthetic arm can improve the quality of life of an amputee. We demonstrate the development of a prosthetic arm by analyzing the electromyography (EMG) signals received from the EMG sensor connected to the muscles of a cleft arm to support disabled people. The prosthetic arm was designed using SolidWorks simulator. After convincing simulation results, we developed the prosthetic arm using 3D printing technology. The robust prosthetic arm was made of polymer for light weight and long durability. We analyzed the EMG signals of different muscles such as f

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Amrinsani, Farid, Levana Forra Wakidi, Made Dwi Pandya Suryanta, Dessy Tri Wulandari, and Wahyu Caesarendra. "Comparison of two Wireless Electromyography Sensor Module Designs using wet electrodes and dry electrodes at the time of Sitting motion to stand." Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics 4, no. 4 (2022): 182–91. http://dx.doi.org/10.35882/ijeeemi.v4i4.249.

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One of the biosignals used to identify muscle signals in humans is electromyography. Electromyography signals are frequently utilized as input and are designed to aid in post-stroke therapy recovery or to assist people with disabilities. This phenomena has led to the development of numerous electromyography module sensor designs for use in support of various research-based applications. In this study, an electromyography sensor module without an electrode cable is compared to an electromyography sensor module that uses gel electrodes, plate electrodes, electrode cables, and other electrode tec

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Rahagiyanto, Angga, Gandu Eko Julianto Suyoso, Veronika Vestine, and Abdullah Iskandar. "Prototype of MyoWare (Electromyography Muscle Sensor) for Measuring People’s Muscle Strengths." International Journal of Health and Information System 1, no. 1 (2023): 19–26. http://dx.doi.org/10.47134/ijhis.v1i1.9.

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Human-Computer Interaction (HCI) becomes a solution to help humans connect with computers. Research and tools related to HCI have been developed by many researchers. HCI is able to help humans connect between humans and computers and humans with humans at a considerable distance. One of HCI model is applied to the MyoWare tool that can capture hand muscle movements using an electromyograph (EMG) sensor. This article describes how to assemble and identify the raw data generated from the MyoWare tool. Using MyoWare on the hand could produce EMG data output. MyoWare only used the EMG sensor and g

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Woo, Seung Hoon. "Advances in intraoperative neuromonitoring for thyroid surgery: from electromyography tubes to sensor-based alternatives." Korean Intraoperative Neuromonitoring Society 5, no. 1 (2025): 35–38. https://doi.org/10.54441/jnn.2025.5.1.35.

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Intraoperative neuromonitoring (IONM) plays a pivotal role in thyroid surgery, particularly for preventing recurrent laryngeal nerve (RLN) injury. While electromyography (EMG) tubes remain standard, newer sensor-based methods are emerging. To evaluate current EMG-based IONM techniques and highlight alternative approaches involving pressure and accelerometer sensors, needle electrodes, and surface monitoring, a comprehensive review of literature and comparative analysis was performed. EMG tube monitoring, though widely adopted, is susceptible to signal loss. Supplementary technologies like piez

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Bangaru, Srikanth Sagar, Chao Wang, and Fereydoun Aghazadeh. "Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition." Sensors 20, no. 18 (2020): 5264. http://dx.doi.org/10.3390/s20185264.

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The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm

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Roland, Theresa. "Motion Artifact Suppression for Insulated EMG to Control Myoelectric Prostheses." Sensors 20, no. 4 (2020): 1031. http://dx.doi.org/10.3390/s20041031.

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Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by electromyography, which measures an electrical signal at the skin surface during muscle contractions. In this contribution, the electromyography is measured with innovative flexible insulated sensors, which separate the skin and the sensor area by a dielectric layer. Electromyography sensors, and biosignal sensors in general, are striving for higher robustness against motion artifacts, which are a major obstacle in real-world environment. The motion artifact suppression

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Roland, Theresa, Kerstin Wimberger, Sebastian Amsuess, Michael Russold, and Werner Baumgartner. "An Insulated Flexible Sensor for Stable Electromyography Detection: Application to Prosthesis Control." Sensors 19, no. 4 (2019): 961. http://dx.doi.org/10.3390/s19040961.

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Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin and are therefore sensitive to sweat and require preparation of the skin. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Due

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MULDAYANI, WAHYU, ARIZAL MUJIBTAMALA NANDA IMRON, KHAIRUL ANAM, SUMARDI SUMARDI, WIDJONARKO WIDJONARKO, and ZILVANHISNA EMKA FITRI. "Pengenalan Pola Sinyal Electromyography (EMG) pada Gerakan Jari Tangan Kanan." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 3 (2020): 591. http://dx.doi.org/10.26760/elkomika.v8i3.591.

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ABSTRAKSinyal EMG merupakan salah satu sinyal yang dapat digunakan untuk memberikan perintah pada kursi roda listrik. Sinyal EMG yang digunakan diambil dari sinyal otot fleksor dan ekstensor yang berada di tangan kanan. Sinyal tersebut diambil menggunakan sensor Myo Armband. Klasifikasi sinyal EMG diambil dari pergerakan jari yang mewakili perintah gerak yaitu jari kelingking untuk bergerak maju, jari manis untuk berhenti, jari tengah untuk belok kanan dan jari telunjuk untuk belok kiri. Setiap sinyal EMG diekstraksi fitur untuk menentukan karakteristik sinyal sehingga fitur yang diperoleh ada

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Sung, Eui-Suk, Sung-Chan Shin, Hyun-Keun Kwon, et al. "Application of Novel Intraoperative Neuromonitoring System Using an Endotracheal Tube With Pressure Sensor During Thyroid Surgery: A Porcine Model Study." Clinical and Experimental Otorhinolaryngology 13, no. 3 (2020): 291–98. http://dx.doi.org/10.21053/ceo.2019.01249.

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Objectives. The loss of signal during intraoperative neuromonitoring (IONM) using electromyography (EMG) in thyroidectomy is one of the biggest problems. We have developed a novel IONM system with an endotracheal tube (ETT) with an attached pressure sensor instead of EMG to detect laryngeal twitching. The aim of the present study was to investigate the feasibility and reliability of this novel IONM system using an ETT with pressure sensor during thyroidectomy in a porcine model.Methods. We developed an ETT-attached pressure sensor that uses the piezoelectric effect to measure laryngeal muscle

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13

Mohammed, Mohammed Guhdar, Belnd Saadi Salih, and Vaman Muhammed Haji. "Employing EMG sensors in Bionic limbs based on a New Binary Trick Method." Science Journal of University of Zakho 11, no. 1 (2023): 54–58. http://dx.doi.org/10.25271/sjuoz.2023.11.1.1027.

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Human muscles can be read by using electromyography (EMG) sensors, which are electrical signals generated by the muscles of human and animal bodies. This means it is possible to use electricity generated by muscles to control actuators/servo motors for any specific tasks. This could support a wide range of applications, especially for people with disabilities. One such application would be making bionic limbs based on servo motors. According to a study held by the K4D helpdesk report based on estimations that 15.3% of the world’s population has a moderate or severe disability, this proportion

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Aiamklin, Wachiraporn, Yutana Jewajinda, and Yunyong Punsawad. "Light Sleep Detection based on Surface Electromyography Signals for Nap Monitoring." International Journal of Biology and Biomedical Engineering 16 (January 7, 2022): 140–45. http://dx.doi.org/10.46300/91011.2022.16.18.

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This paper proposes the development of automatic sleep stage detection by using physiological signals. We aim to develop an application to assist drivers after drowsiness or fatigue detection by a commercial driver vigilance system. The proposed method used a low-cost surface electromyography (EMG) device for sleep stage detection. We investigate skeletal muscle location and EMG features from sleep stage 2 to provide an EMG-based nap monitoring system. The results showed that using only one channel of a bipolar EMG signal from an upper trapezius muscle with median power frequency can achieve 8

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Kim, Inwoo, Taehoon Lee, and Soo-Hong Lee. "Ankle intention detection algorithm using electromyography signal." Journal of Computational Design and Engineering 8, no. 5 (2021): 1234–42. http://dx.doi.org/10.1093/jcde/qwab042.

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Abstract In this study, an ankle intention detection algorithm was developed to calculate the torque the user wants to exert from the ankle starting from the user’s EMG signal. Since the subtalar joint axis of the ankle is very important for stability, the intent detection algorithm also calculates the torque of the eversion motion of the subtalar joint axis. A dry EMG sensor was used to measure the EMG signal, and an ankle biaxial torque measurement device was manufactured to measure the ankle torque to perform the experiment. The experiment was conducted on four healthy subjects (mean ± SD:

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Kimoto, Akira, Hiromu Fujiyama, and Masanao Machida. "A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation." Sensors 23, no. 3 (2023): 1539. http://dx.doi.org/10.3390/s23031539.

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A wireless multi-layered sensor that allows electromyography (EMG), mechanomyography (MMG) and near-infrared spectroscopy (NIRS) measurements to be carried out simultaneously is presented. The multi-layered sensor comprises a thin silver electrode, transparent piezo-film and photosensor. EMG and MMG measurements are performed using the electrode and piezo-film, respectively. NIRS measurements are performed using the photosensor. Muscular activity is then analyzed in detail using the three types of data obtained. In experiments, the EMG, MMG and NIRS signals were measured for isometric ramp con

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Kang, John S., Kee S. Moon, Sung Q. Lee, Nicholas Satterlee, and Xiaowei Zuo. "A Wearable Silent Text Input System Using EMG and Piezoelectric Sensors." Sensors 25, no. 8 (2025): 2624. https://doi.org/10.3390/s25082624.

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This paper introduces a wearable silent text input system designed to capture text input through silent speech, without generating audible sound. The system integrates Electromyography (EMG) and piezoelectric lead zirconate titanate (PZT) sensors in a miniaturized form that can be comfortably attached to the chin, making it both comfortable to wear and esthetically pleasing. The EMG sensor records muscle activity linked to specific tongue and jaw movements, while the PZT sensor measures the minute vibrations and pressure changes in the chin skin caused by silent speech. Data from both sensors

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18

Ke, Ang, Jian Huang, Luyao Chen, Zhaolong Gao, and Jiping He. "An Ultra-Sensitive Modular Hybrid EMG–FMG Sensor with Floating Electrodes." Sensors 20, no. 17 (2020): 4775. http://dx.doi.org/10.3390/s20174775.

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To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG–FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and

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Pamungkas, Daniel S. "Penggunaan Kernel SVM untuk Klasifikasi Pergerakan Jari Mengunakan Sinyal EMG." Jurnal Elektro dan Mesin Terapan (ELEMENTER), Vol. 7 No. 2 (2021) (November 30, 2021): 1–6. http://dx.doi.org/10.35143/elementer.v7i2.5146.

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Biomekanik merupakan sebuah bidang ilmu yang mempelajari pergerakkan makhluk hidup, khususnya manusia. Pada ilmu tersebut dikenal adanya suatu sinyal yang dinamakan electromyography (EMG). EMG adalah suatu sinyal listrik yang berasal dari otot manusia. Sinyal ini banyak digunakan sebagai media pengendali, salah satunya adalah robot tangan. Penelitian ini bertujuan untuk mengklasifikaikan pola pergerakkan jari manusia. Sebuah sensor Myo Armband digunakan untuk mendeteksi sinyal EMG. Sensor ini diletakkan pada lengan bawah tangan kanan subjek untuk mendapatkan sinyal EMG. Sebagian data digunakan

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Sanford, Joe, Rita Patterson, and Dan O. Popa. "Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue." Journal of Rehabilitation and Assistive Technologies Engineering 4 (January 2017): 205566831770873. http://dx.doi.org/10.1177/2055668317708731.

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Objective Surface electromyography has been a long-standing source of signals for control of powered prosthetic devices. By contrast, force myography is a more recent alternative to surface electromyography that has the potential to enhance reliability and avoid operational challenges of surface electromyography during use. In this paper, we report on experiments conducted to assess improvements in classification of surface electromyography signals through the addition of collocated force myography consisting of piezo-resistive sensors. Methods Force sensors detect intrasocket pressure changes

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Roland, Theresa, Sebastian Amsuess, Michael Russold, and Werner Baumgartner. "Ultra-Low-Power Digital Filtering for Insulated EMG Sensing." Sensors 19, no. 4 (2019): 959. http://dx.doi.org/10.3390/s19040959.

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Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Here, we present ultra-low-power digital signal processing algorithms for an insulated EMG sensor which couples the EMG signal capacitiv

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Chen, Zhiyong, Qingsuo Wang, Yunce Bi, et al. "Analyzing Human Muscle State with Flexible Sensors." Journal of Sensors 2022 (July 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/5227955.

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Analyzing human muscle states has attracted extensive attention. EMG (electromyography) pattern recognition methods based on these works have been proposed for many years. However, uncomfortable wearing and high prices make it inconvenient for motion tracking and muscle analysis by using robotic arms and inertial sensors in daily life. In this study, we propose to use smart clothes integrated with flexible sensors to collect arm motion data, estimate the kinematic information of continuous arm motion, and predict the EMG signal of each arm muscle. Firstly, the neural network regression model i

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Kuber, Pranav Madhav, Abhineet Rajendra Kulkarni, and Ehsan Rashedi. "Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors." Applied Sciences 14, no. 11 (2024): 4563. http://dx.doi.org/10.3390/app14114563.

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Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implem

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Dyvak, M. P., and V. I. Tymets. "Improved method and tools with automatic adjustment of electrical signal parameters for detection of the reverse laryngeal nerve." Optoelectronic Information-Power Technologies 49, no. 1 (2025): 264–77. https://doi.org/10.31649/1681-7893-2025-49-1-264-277.

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The concept of using electromyography during thyroid gland surgery is considered. The electrophysiological features of surgical wound tissues, namely the muscle membrane potential of the vocal cord, were investigated. The analysis of EMG hardware that can be used during thyroid gland operations is carried out. The choice of EMG sensor characteristics that can be implemented in the existing complex of RLN monitoring is justified. The complex of RLN monitoring is based on a single-board computer, Raspberry Pi 4 Model B. A description of additional hardware elements to combine complex sensor and

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Yu, Haoran, Mingqi Ma, Baishun Zhang, et al. "Bionic Sensors for Biometric Acquisition and Monitoring: Challenges and Opportunities." Sensors 25, no. 13 (2025): 3981. https://doi.org/10.3390/s25133981.

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The development of materials science, artificial intelligence and wearable technology has created both opportunities and challenges for the next generation of bionic sensor technology. Bionic sensors are extensively utilized in the collection and monitoring of human biological signals. Human biological signals refer to the parameters generated inside or outside the human body to transmit information. In a broad sense, they include bioelectrical signals, biomechanical information, biomolecules, and chemical molecules. This paper systematically reviews recent advances in bionic sensors in the fi

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Sobh, Khaled Nedal Mahmoud, Nasrul Anuar Abd Razak, and Noor Azuan Abu Osman. "Investigation of EMG parameter for transtibial prosthetic user with flexion and extension of the knee and normal walking gait: A preliminary study." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 235, no. 4 (2021): 419–27. http://dx.doi.org/10.1177/0954411920985753.

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Electromyography signal has been used widely as input for prosthetic’s leg movements. C-Leg, for example, is among the prosthetics devices that use electromyography as the main input. The main challenge facing the industrial party is the position of the electromyography sensor as it is fixed inside the socket. The study aims to investigate the best positional parameter of electromyography for transtibial prosthetic users for the device to be effective in multiple movement activities and compare with normal human muscle’s activities. DELSYS Trigno wireless electromyography instrument was used i

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Riky, Tri Yunardi, Inaiyah Agustin Eva, Latifah Risalatul, and Winarno. "Application of EMG and Force Signals of Elbow Joint on Robot-assisted Arm Training." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 6 (2018): 2913–20. https://doi.org/10.12928/TELKOMNIKA.v16i6.11707.

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Flexion-extension based on the system's robotic arm has the potential to increase the patient's elbow joint movement. The force sensor and electromyography signals can support the biomechanical system to detect electrical signals generated by the muscles of the biological. The purpose of this study is to implement the design of force sensor and EMG signals application on the elbow flexion motion of the upper arm. In this experiments, the movements of flexion at an angle of 45º, 90º and 135º is applied to identify the relationship between the amplitude of the EMG and forc

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Hagengruber, Annette, Ulrike Leipscher, Bjoern M. Eskofier, and Jörn Vogel. "A New Labeling Approach for Proportional Electromyographic Control." Sensors 22, no. 4 (2022): 1368. http://dx.doi.org/10.3390/s22041368.

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Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with proportional and simultaneous control schemes. Machine learning approaches and methods based on regression are often used to realize the desired functionality. Training procedures often include the tracking of visual stimuli on a screen or additional sensors, such as cameras or force sensors, to creat

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Park, Dahee, and Eui-Suk Sung. "EMG tube based Intraoperative neuromonitoring in thyroid surgery." Korean Intraoperative Neuromonitoring Society 2, no. 2 (2022): 25–29. http://dx.doi.org/10.54441/jnn.2022.2.2.25.

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Thyroid surgery can cause many complications, but recurrent laryngeal nerve palsy is the most important. Therefore, many efforts have been tried to avoid recurrent laryngeal nerve palsy. Among them, intraoperative neuromonitoring (IONM) is one of the useful tools to find nerves and avoid nerve palsy during surgery. There are various methods, but the method of directly monitoring the movement of the vocal cords using an electromyography tube was introduced first. After that, a method of increasing the signal accuracy by using a pressure sensor and an accelerometer sensor was also introduced. In

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Liu, Yang, Chengdong Lin, and Zhenjiang Li. "WR-Hand." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (2021): 1–27. http://dx.doi.org/10.1145/3478112.

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This paper presents WR-Hand, a wearable-based system tracking 3D hand pose of 14 hand skeleton points over time using Electromyography (EMG) and gyroscope sensor data from commercial armband. This system provides a significant leap in wearable sensing and enables new application potentials in medical care, human-computer interaction, etc. A challenge is the armband EMG sensors inevitably collect mixed EMG signals from multiple forearm muscles because of the fixed sensor positions on the device, while prior bio-medical models for hand pose tracking are built on isolated EMG signal inputs from i

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Yin, Wei, Yinong Chen, Curran Reddy, and Xudong Zhang*. "Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 656–60. http://dx.doi.org/10.1177/1071181322661205.

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Knowledge of low-back loading is essential for understanding and mitigating the risk of low-back overexertion injuries. Conventional data acquisition methods for estimating joint loading are limited to laboratory settings, whereas wearable sensors can provide a mobile and cost-effective alternative. This study investigated the feasibility of learning prediction of L5S1 flexion moment based on kinematics and electromyography (EMG) measurements from flexible sensors. Four machine learning methods were compared, and different subsets of sensor inputs were explored. Results indicated that the supp

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Cenit, Mikecon, and Vaibhav Gandhi. "Design and development of the sEMG-based exoskeleton strength enhancer for the legs." Journal of Mechatronics, Electrical Power, and Vehicular Technology 10, no. 2 (2019): 61. http://dx.doi.org/10.14203/j.mev.2019.v10.61-71.

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This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed, 3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are used coherently to create different state filters for the EMG, human body posture and contro

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Cenit, Mikecon, and Vaibhav Gandhi. "Design and development of the sEMG-based exoskeleton strength enhancer for the legs." Journal of Mechatronics, Electrical Power, and Vehicular Technology 11, no. 2 (2020): 64. http://dx.doi.org/10.14203/j.mev.2020.v11.64-74.

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This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed, 3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are used coherently to create different state filters for the EMG, human body posture and contro

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Moon, Kee S., John S. Kang, Sung Q. Lee, Jeff Thompson, and Nicholas Satterlee. "Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments." Sensors 24, no. 13 (2024): 4125. http://dx.doi.org/10.3390/s24134125.

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This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who

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Vijayvargiya, Ankit, Bharat Singh, Rajesh Kumar, Usha Desai, and Jude Hemanth. "Hybrid Deep Learning Approaches for sEMG Signal-Based Lower Limb Activity Recognition." Mathematical Problems in Engineering 2022 (November 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/3321810.

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Lower limb activity recognition utilizing body sensor data has attracted researchers due to its practical applications, such as neuromuscular disease detection and kinesiological investigations. The employment of wearable sensors including accelerometers, gyroscopes, and surface electromyography has grown due to their low cost and broad applicability. Electromyography (EMG) sensors are preferable for automated control of a lower limb exoskeleton or prosthesis since they detect the signal beforehand and allow faster movement detection. The study presents hybrid deep learning models for lower li

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Sharma, Neeraj, Alok Prakash, and Shiru Sharma. "An optoelectronic muscle contraction sensor for prosthetic hand application." Review of Scientific Instruments 94, no. 3 (2023): 035009. http://dx.doi.org/10.1063/5.0130394.

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Surface electromyography (sEMG) is considered an established means for controlling prosthetic devices. sEMG suffers from serious issues such as electrical noise, motion artifact, complex acquisition circuitry, and high measuring costs because of which other techniques have gained attention. This work presents a new optoelectronic muscle (OM) sensor setup as an alternative to the EMG sensor for precise measurement of muscle activity. The sensor integrates a near-infrared light-emitting diode and phototransistor pair along with the suitable driver circuitry. The sensor measures skin surface disp

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Song, Min-Su, Sung-Gu Kang, Kyu-Tae Lee, and Jeonghyun Kim. "Wireless, Skin-Mountable EMG Sensor for Human–Machine Interface Application." Micromachines 10, no. 12 (2019): 879. http://dx.doi.org/10.3390/mi10120879.

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The development of advanced technologies for wireless data collection and the analysis of quantitative data, with application to a human–machine interface (HMI), is of growing interest. In particular, various wearable devices related to HMIs are being developed. These devices require a customization process that considers the physical characteristics of each individual, such as mounting positions of electrodes, muscle masses, and so forth. Here, the authors report device and calculation concepts for flexible platforms that can measure electrical signals changed through electromyography (EMG).

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Etana, Bulcha Belay, Benny Malengier, Kwa Timothy, Sitek Wojciech, Janarthanan Krishnamoorthy, and Lieva Van Langenhove. "A review on the recent developments in design and integration of electromyography textile electrodes for biosignal monitoring." Journal of Industrial Textiles 53 (January 2023): 152808372311750. http://dx.doi.org/10.1177/15280837231175062.

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Due to recent developments in wearable sensor technology, textile electrodes are routinely being employed in electromyography (EMG) for continuous monitoring of the biosignals from the muscles. However, the performance of such smart textile-based health monitoring devices depends on several factors such as, the sensitivity (impedance), durability (reusable/washable), users' comfort ability, integrability, and automatability. In this article we review the characteristics and the performance of the EMG textile electrodes, in the context of functional textile materials, smart textile materials, a

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Zhu, Wenchao, and Yingzi Lin. "Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing." Sensors 25, no. 7 (2025): 2086. https://doi.org/10.3390/s25072086.

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Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the “gold standard” for pain assessment, tools are needed to objectively monitor and account for inter-individual differences. This study introduced a novel framework to objectively classify pain intensity levels using physiological signals during Quantitative Sensory Testing sessions. Twenty-four participants participated in the study wearing physiological sensors (blood volume pulse (BVP), galvanic skin response (GSR), electromyography (EMG), respiration rate (R

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Kadhim, Abdul-Sahib N., Mohammed M. Abdul-Sattar, and Hasan S. Falah. "Surface Electromyography Signal Collecting Strategy for Artificial Hand Controlling Purposes." European Journal of Engineering Research and Science 2, no. 10 (2018): 21–25. http://dx.doi.org/10.24018/ejers.2017.2.10.467.

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Surface electromyography technique is presented for muscle activation detection and intuitive control of prostheses or robot arm. This research aims to develop a strategy for collecting EMG signal in order to be used as input control for an artificial hand. Methodology of this research composed of, collecting EMG signal using clinical device to choose the best position. Collect the signal using myoware muscle sensor depending on these positions locating above with determining the threshold value for each position. The test was done on a healthy female person and an amputee person, a number of

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Kadhim, Abdul-Sahib N., Mohammed M. Abdul-Sattar, and Hasan S. Falah. "Surface Electromyography Signal Collecting Strategy for Artificial Hand Controlling Purposes." European Journal of Engineering and Technology Research 2, no. 10 (2018): 21–25. http://dx.doi.org/10.24018/ejeng.2017.2.10.467.

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Surface electromyography technique is presented for muscle activation detection and intuitive control of prostheses or robot arm. This research aims to develop a strategy for collecting EMG signal in order to be used as input control for an artificial hand. Methodology of this research composed of, collecting EMG signal using clinical device to choose the best position. Collect the signal using myoware muscle sensor depending on these positions locating above with determining the threshold value for each position. The test was done on a healthy female person and an amputee person, a number of

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Biagetti, Giorgio, Paolo Crippa, Laura Falaschetti, and Claudio Turchetti. "A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy." Electronics 9, no. 6 (2020): 934. http://dx.doi.org/10.3390/electronics9060934.

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This paper proposes a wireless sensor device for the real-time acquisition of bioelectrical signals such as electromyography (EMG) and electrocardiography (ECG), coupled with an inertial sensor, to provide a comprehensive stream of data suitable for human activity detection, motion analysis, and technology-assisted nursing of persons with physical or cognitive impairments. The sensor is able to acquire up to three independent bioelectrical channels (six electrodes), each with 24 bits of resolution and a sampling rate up to 3.2 kHz, and has a 6-DoF inertial platform measuring linear acceleratio

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Ersin, Çağatay, and Mustafa Yaz. "Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control." Journal of Sensors 2024 (April 3, 2024): 1–17. http://dx.doi.org/10.1155/2024/6808322.

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In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy l

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Md. Naimul Hasan, Md. Saizuddin (Kabir), Syed Masrur Ahmed, and Sarowar Hossain. "Role of Electromyography(EMG) for (Muscle Conductivity) management of Paralyzed Patients in Dhaka city." Z H Sikder Women’s Medical College Journal 3, Number 2 (2021): 12–15. http://dx.doi.org/10.47648/zhswmcj.2021.v0302.03.

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The purpose of this study is to assess the electromyography (EMG) signals of biceps muscle of paralyzed patients to know the condition of their affected muscle. For measuring the muscle signal, 5 voltages analog signal is sent to biceps muscle via EMG sensor with Arduino. As higher the voltage is, the condition of the muscle status is better. In this study, we have observed paralyzed patients in Dhaka city having different kinds of paralysis patients like Complete paralysis, Partial or incomplete paralysis and localized paralysis. The study also focused on monitoring different stage of paralys

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Aljobouri, Hadeel K. "A Virtual EMG Signal Control and Analysis for Optimal Hardware Design." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 02 (2022): 154–66. http://dx.doi.org/10.3991/ijoe.v18i02.27047.

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Background: A muscle-computer interface is one of the new applications of the human-computer interface technologies and specifically the brain-computer interface. Brain-muscle-computer interface based on the Electromyography (EMG) signal. EMG signal is an electrical activity from a muscle that is used as an input for effecting several tasks.Objective: This work presented an interfacing process between the Graphical User Interface (GUI) and hardware system. Using the implemented system, the researcher shall deals with the raw EMG data easily by analyzing the signal from the muscle sensor detect

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Wu, Yi-Da, Shanq-Jang Ruan, and Yu-Hao Lee. "An Ultra-Low Power Surface EMG Sensor for Wearable Biometric and Medical Applications." Biosensors 11, no. 11 (2021): 411. http://dx.doi.org/10.3390/bios11110411.

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In recent years, the surface electromyography (EMG) signal has received a lot of attention. EMG signals are used to analyze muscle activity or to evaluate a patient’s muscle status. However, commercial surface EMG systems are expensive and have high power consumption. Therefore, the purpose of this paper is to implement a surface EMG acquisition system that supports high sampling and ultra-low power consumption measurement. This work analyzes and optimizes each part of the EMG acquisition circuit and combines an MCU with BLE. Regarding the MCU power saving method, the system uses two different

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Badawi, Abeer A., Ahmad Al-Kabbany, and Heba A. Shaban. "Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks." Journal of Healthcare Engineering 2020 (June 8, 2020): 1–14. http://dx.doi.org/10.1155/2020/7914649.

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This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly perfor

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Esposito, Daniele, Emilio Andreozzi, Antonio Fratini, et al. "A Piezoresistive Sensor to Measure Muscle Contraction and Mechanomyography." Sensors 18, no. 8 (2018): 2553. http://dx.doi.org/10.3390/s18082553.

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Measurement of muscle contraction is mainly achieved through electromyography (EMG) and is an area of interest for many biomedical applications, including prosthesis control and human machine interface. However, EMG has some drawbacks, and there are also alternative methods for measuring muscle activity, such as by monitoring the mechanical variations that occur during contraction. In this study, a new, simple, non-invasive sensor based on a force-sensitive resistor (FSR) which is able to measure muscle contraction is presented. The sensor, applied on the skin through a rigid dome, senses the

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Krašna, Simon, and Srđan Đorđević. "Estimating the Effects of Awareness on Neck-Muscle Loading in Frontal Impacts with EMG and MC Sensors." Sensors 20, no. 14 (2020): 3942. http://dx.doi.org/10.3390/s20143942.

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Critical traffic situations, such as vehicle collisions and emergency manoeuvres, can cause an occupant to respond with reflex and voluntary actions. These affect the occupant’s position and dynamic loading during interactions with the vehicle’s restraints, possibly compromising their protective function. Electromyography (EMG) is a commonly used method for measuring active muscle response and can also provide input parameters for computer simulations with models of the human body. The recently introduced muscle-contraction (MC) sensor is a wearable device with a piezo-resistive element for me

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Sathik, Muhammadu, Kanimozhi V, Samipriya S, and Sarumathi M. "Design and Construction of 5-Dof EMG Based Robotic Arm System." International Research Journal of Computer Science 11, no. 09 (2024): 562–72. http://dx.doi.org/10.26562/irjcs.2024.v1109.01.

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Electromyography (EMG) is an alternate method of obtaining muscle signal outputs. As a result, the current development in designing my electric plans has piqued the attention of researchers in this subject. This is because Standard controllers lack essential components, limiting the use of limbs to operate equipment, namely an arm controlled by robotics. EMG signals are subject to noise, including crosstalk, motion artifacts, ambient noise, and intrinsic noise. Electromyography preparation requires careful selection of muscle groups, electrode placement, and environment quality, all of which i

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