The biosignalsplux Electromyography (EMG) is designed to monitor muscular activity even in the most extreme conditions. The bipolar configuration is ideal for uncompromised low-noise data acquisition, and the raw data output provides medical-grade data enabling it to be used for advanced and highly accurate biomedical, biomechanics, and sports research, among many other possible applications.
|Connector Type||UC-E6 (Male)|
|Range||±1.5mV (with VCC=3V)|
|Cable Length||100cm±0.5cm (customizable)|
This sensor is not a standalone use sensor and requires the use of a biosignalsplux hub and electrodes in order to acquire any data. It can be included in the following biosignalsplux kits which come with all the needed hardware and accessories for EMG data acquisition:
This add-on is designed to extract useful statistical temporal and spectral information from electromyography data acquired with the Electromyography (EMG) sensor. Its automatic onset detection algorithm is designed to detect muscle activations in the EMG signal, allowing the user to select and derive activation-specific parameters for further analysis of the EMG signal. The onset detection algorithm can be fine-tuned for application-specific muscle activation detection by defining the frequency ranges of interest using the available low pass, high pass, and band pass filters.Find Out More
The Muscle Load Analysis add-on is designed for online (during the acquisition) and offline (post-processing) evaluation of muscular load in ergonomics, biomechanics, and sports research applications, and is the ideal software extension for the biosignalsplux Electromyography (EMG) sensor. It measures the muscular load that muscles are subjected during a period or task (e.g. during a normal work day or work process) according to Jonsson’s method of Amplitude Probability Distribution Analysis (APDA) of acquired EMG signals.Find Out More
G. Ramos, J. Vaz, G. Mendonça, P. Pezarat-Correia, J. Rodrigues, M. Alfaras, H. Gamboa, Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor, in Journal of Healthcare Engineering, vol. 2020, no. 6484129, pp. 1-18, 2020
B. Tassignon, B. Serrien, K. Pauw, J. Baeyens, R. Meeusen, Continuous Knee Cooling Affects Functional Hop Performance - A Randomized Controlled Trial, in Journal of Sports Science & Medicine, vol. 18, no. 2, pp. 322-239, 2018
L. Shu, J. Xie, M. Yang, Z. Li, D. liao, X. Xu, X. Yang, A review of Emotion Recognition Using Phyisological Signals, in Sensors, vol. 18, no. 2074, pp. 1-42, 2018
C. Crasto, A. Montes, P. Carvalho, J. Carral, Pressure biofeedback unit to assess and train lumbopelvic stability in supine individuals with chronic low back pain, in Journal of Physical Therapy Science, vol. 31, pp. 755-759, 2019
A. Conceição, S. Palma, H. Silva, H. Gamboa, H. Louro., Electromyography in Front Crawl Technique - Case Study, in The Open Sports Sciences Journal, vol. 3, no. 1, pp. 67-69, 2010
Visit the biosignalsplux publications page for a full list of available publications.